From 53d7d91e20edb3798d2390bb219b07af946dfae9 Mon Sep 17 00:00:00 2001 From: Eric Traut Date: Sun, 26 Apr 2026 23:08:59 -0700 Subject: [PATCH] Add Codex issue digest skill --- .codex/skills/codex-issue-digest/SKILL.md | 102 ++ .../codex-issue-digest/agents/openai.yaml | 4 + .../scripts/collect_issue_digest.py | 988 ++++++++++++++++++ .../scripts/test_collect_issue_digest.py | 614 +++++++++++ 4 files changed, 1708 insertions(+) create mode 100644 .codex/skills/codex-issue-digest/SKILL.md create mode 100644 .codex/skills/codex-issue-digest/agents/openai.yaml create mode 100755 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py create mode 100644 .codex/skills/codex-issue-digest/scripts/test_collect_issue_digest.py diff --git a/.codex/skills/codex-issue-digest/SKILL.md b/.codex/skills/codex-issue-digest/SKILL.md new file mode 100644 index 000000000000..b531748f8c47 --- /dev/null +++ b/.codex/skills/codex-issue-digest/SKILL.md @@ -0,0 +1,102 @@ +--- +name: codex-issue-digest +description: Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows. Use when asked to summarize recent Codex bug reports or enhancement requests, especially for owner-specific labels such as tui, exec, app, or similar areas. +--- + +# Codex Issue Digest + +## Objective + +Produce a concise, insight-oriented digest of `openai/codex` issues for the requested feature-area labels over the previous 24 hours by default. Honor a different duration when the user asks for one, for example "past week" or "48 hours". + +Include only issues that currently have `bug` or `enhancement` plus at least one requested owner label. If the user asks for all areas or all labels, collect `bug`/`enhancement` issues across all labels. + +## Inputs + +- Feature-area labels, for example `tui exec` +- `all areas` / `all labels` to scan all current feature labels +- Optional repo override, default `openai/codex` +- Optional time window, default previous 24 hours; examples: `48h`, `7d`, `1w`, `past week` + +## Workflow + +1. Run the collector from a current Codex repo checkout: + +```bash +python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --labels tui exec --window-hours 24 +``` + +Use `--window "past week"` or `--window-hours 168` when the user asks for a non-default duration. Use `--all-labels` when the user says all areas or all labels. + +2. Use the JSON as the source of truth. It includes new issues, new issue comments, new reactions/upvotes, current labels, current reaction counts, model-ready `summary_inputs`, and detailed `digest_rows`. +3. Start the report with `## Summary`, then `## Details`. +4. In `## Summary`, write skim-first headlines: + - Lead with the most important fact or judgment. Do not start with aggregate counts unless the aggregate itself is the story. + - Make the first 1-3 bullets answer "what should owners pay attention to right now?" + - Bold only the critical insight phrase in each high-priority bullet, for example `**GPT-5.5 context is the dominant pressure point**`. + - Keep summary bullets short enough to scan in about 20 seconds. + - Put broad stats near the end of the summary, after the owner-relevant takeaways. + - Say clearly when there is nothing significant to act on. + - Call out any areas or themes receiving lots of user attention. + - Cluster and name themes yourself from `summary_inputs`; the collector intentionally does not hard-code issue categories. + - Use a cluster only when the issues genuinely share the same product problem. If several issues merely share a broad platform or label, describe them individually. + - Do not omit a repeated theme just because its individual issues fall below the details table cutoff. Several similar reports should be called out as a repeated customer concern. + - For single-issue rows, summarize the concern directly instead of calling it a cluster. + - Use inline numbered issue links from each relevant row's `ref_markdown`. +5. In `## Details`, include a compact table only when useful: + - Prefer rows from `digest_rows`; include a `Refs` column using each row's `ref_markdown`. + - Keep the table short; omit low-signal rows when the summary already covers them. + - Use compact columns such as marker, area, type, description, interactions, and refs. + - The `Description` cell should be a short owner-readable phrase. Use row `description`, title, body excerpts, and recent comments, but do not mechanically copy the raw GitHub issue title when it contains incidental details. + - A clear quiet/no-concern sentence when there is no meaningful signal. +6. Use the JSON `attention_marker` exactly. It is empty for normal rows, `🔥` for elevated rows, and `🔥🔥` for very high-attention rows. The actual cutoffs are in `attention_thresholds`. +7. Use inline numbered references where a row or bullet points to issues, for example `Compaction bugs [1](https://github.com/openai/codex/issues/123), [2](https://github.com/openai/codex/issues/456)`. Do not add a separate footnotes section. +8. Label `interactions` as `Interactions`; it counts posts/comments/reactions during the requested window, not unique people. +9. Mention the collector `script_version`, repo checkout `git_head`, and time window in the digest footer or final line. + +## Reaction Handling + +The collector uses GitHub reactions endpoints, which include `created_at`, to count reactions created during the digest window for hydrated issues. It reports both in-window reaction counts and current reaction totals. Treat current reaction totals as standing engagement, and treat `new_reactions` / `new_upvotes` as windowed activity. + +By default, the collector fetches issue comments with `since=` and caps the number of comment pages per issue. This keeps very long historical threads from dominating a digest run and focuses the report on recent posts. Use `--fetch-all-comments` only when exhaustive comment history is more important than runtime. + +GitHub issue search is still seeded by issue `updated_at`, so a purely reaction-only issue may be missed if reactions do not bump `updated_at`. Covering every reaction-only case would require either a persisted snapshot store or a broader scan of labeled issues. + +## Attention Markers + +The collector scales attention markers by the requested time window. The baseline is 10 human user interactions for `🔥` and 20 for `🔥🔥` over 24 hours; longer or shorter windows scale those cutoffs linearly and round up. For example, a one-week report uses 70 and 140 interactions. Human user interactions are human-authored new issue posts, human-authored new comments, and human reactions created during the window, including upvotes. Bot posts and bot reactions are excluded. In prose, explain this as high user interaction rather than naming the emoji. + +## Freshness + +The automation should run from a repo checkout that contains this skill. For shared daily use, prefer one of these patterns: + +- Run the automation in a checkout that is refreshed before the automation starts, for example with `git pull --ff-only`. +- If the automation cannot safely mutate the checkout, have it report the current `git_head` from the collector output so readers know which skill/script version produced the digest. + +## Sample Owner Prompt + +```text +Use $codex-issue-digest to run the Codex issue digest for labels tui and exec over the previous 24 hours. +``` + +```text +Use $codex-issue-digest to run the Codex issue digest for all areas over the past week. +``` + +## Validation + +Dry run the collector against recent issues: + +```bash +python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --labels tui exec --window-hours 24 +``` + +```bash +python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --all-labels --window "past week" --limit-issues 10 +``` + +Run the focused script tests: + +```bash +pytest .codex/skills/codex-issue-digest/scripts/test_collect_issue_digest.py +``` diff --git a/.codex/skills/codex-issue-digest/agents/openai.yaml b/.codex/skills/codex-issue-digest/agents/openai.yaml new file mode 100644 index 000000000000..706ce5e11b3e --- /dev/null +++ b/.codex/skills/codex-issue-digest/agents/openai.yaml @@ -0,0 +1,4 @@ +interface: + display_name: "Codex Issue Digest" + short_description: "Summarize Codex issues by labels or all areas" + default_prompt: "Use $codex-issue-digest to run the Codex issue digest for labels tui and exec over the previous 24 hours." diff --git a/.codex/skills/codex-issue-digest/scripts/collect_issue_digest.py b/.codex/skills/codex-issue-digest/scripts/collect_issue_digest.py new file mode 100755 index 000000000000..e211af08f8b9 --- /dev/null +++ b/.codex/skills/codex-issue-digest/scripts/collect_issue_digest.py @@ -0,0 +1,988 @@ +#!/usr/bin/env python3 +"""Collect recent openai/codex issue activity for owner-focused digests.""" + +import argparse +import json +import math +import re +import subprocess +import sys +from datetime import datetime, timedelta, timezone +from pathlib import Path +from urllib.parse import quote + +SCRIPT_VERSION = 2 +QUALIFYING_KIND_LABELS = ("bug", "enhancement") +REACTION_KEYS = ("+1", "-1", "laugh", "hooray", "confused", "heart", "rocket", "eyes") +BASE_ATTENTION_WINDOW_HOURS = 24.0 +ONE_ATTENTION_INTERACTION_THRESHOLD = 10 +TWO_ATTENTION_INTERACTION_THRESHOLD = 20 +ALL_LABEL_PHRASES = {"all", "all areas", "all labels", "all-areas", "all-labels", "*"} + + +class GhCommandError(RuntimeError): + pass + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Collect recent GitHub issue activity for a Codex owner digest." + ) + parser.add_argument( + "--repo", default="openai/codex", help="OWNER/REPO, default openai/codex" + ) + parser.add_argument( + "--labels", + nargs="+", + default=[], + help="Feature-area labels owned by the digest recipient, for example: tui exec", + ) + parser.add_argument( + "--all-labels", + action="store_true", + help="Collect bug/enhancement issues across all feature-area labels", + ) + parser.add_argument( + "--window", + help='Lookback duration such as "24h", "7d", "1w", or "past week"', + ) + parser.add_argument( + "--window-hours", type=float, default=24.0, help="Lookback window" + ) + parser.add_argument( + "--since", help="UTC ISO timestamp override for the window start" + ) + parser.add_argument("--until", help="UTC ISO timestamp override for the window end") + parser.add_argument( + "--limit-issues", + type=int, + default=200, + help="Maximum candidate issues to hydrate after search", + ) + parser.add_argument( + "--body-chars", type=int, default=1200, help="Issue body excerpt length" + ) + parser.add_argument( + "--comment-chars", type=int, default=900, help="Comment excerpt length" + ) + parser.add_argument( + "--max-comment-pages", + type=int, + default=3, + help=( + "Maximum pages of issue comments to hydrate per issue after applying the " + "window filter. Use 0 with --fetch-all-comments for no page cap." + ), + ) + parser.add_argument( + "--fetch-all-comments", + action="store_true", + help="Hydrate complete issue comment histories instead of only window-updated comments.", + ) + return parser.parse_args() + + +def parse_timestamp(value, arg_name): + if value is None: + return None + normalized = value.strip() + if not normalized: + return None + if normalized.endswith("Z"): + normalized = f"{normalized[:-1]}+00:00" + try: + parsed = datetime.fromisoformat(normalized) + except ValueError as err: + raise ValueError(f"{arg_name} must be an ISO timestamp") from err + if parsed.tzinfo is None: + parsed = parsed.replace(tzinfo=timezone.utc) + return parsed.astimezone(timezone.utc) + + +def format_timestamp(value): + return ( + value.astimezone(timezone.utc) + .replace(microsecond=0) + .isoformat() + .replace("+00:00", "Z") + ) + + +def resolve_window(args): + until = parse_timestamp(args.until, "--until") or datetime.now(timezone.utc) + since = parse_timestamp(args.since, "--since") + if since is None: + hours = parse_duration_hours(getattr(args, "window", None)) + if hours is None: + hours = getattr(args, "window_hours", 24.0) + if hours <= 0: + raise ValueError("window duration must be > 0") + since = until - timedelta(hours=hours) + if since >= until: + raise ValueError("--since must be before --until") + return since, until + + +def parse_duration_hours(value): + if value is None: + return None + text = value.strip().casefold().replace("_", " ") + if not text: + return None + text = re.sub(r"^(past|last)\s+", "", text) + aliases = { + "day": 24.0, + "24h": 24.0, + "week": 168.0, + "7d": 168.0, + } + if text in aliases: + return aliases[text] + match = re.fullmatch(r"(\d+(?:\.\d+)?)\s*(h|hr|hrs|hour|hours)", text) + if match: + return float(match.group(1)) + match = re.fullmatch(r"(\d+(?:\.\d+)?)\s*(d|day|days)", text) + if match: + return float(match.group(1)) * 24.0 + match = re.fullmatch(r"(\d+(?:\.\d+)?)\s*(w|week|weeks)", text) + if match: + return float(match.group(1)) * 168.0 + raise ValueError(f"Unsupported duration: {value}") + + +def normalize_requested_labels(labels, all_labels=False): + out = [] + seen = set() + for raw in labels: + for piece in raw.split(","): + label = piece.strip() + if not label: + continue + key = label.casefold() + if key not in seen: + out.append(label) + seen.add(key) + phrase = " ".join(label.casefold() for label in out) + if all_labels or phrase in ALL_LABEL_PHRASES: + return [], True + if not out: + raise ValueError( + "At least one feature-area label is required, or use --all-labels" + ) + return out, False + + +def quote_label(label): + if re.fullmatch(r"[A-Za-z0-9_.:-]+", label): + return f"label:{label}" + escaped = label.replace('"', '\\"') + return f'label:"{escaped}"' + + +def build_search_queries( + repo, owner_labels, since, kind_labels=QUALIFYING_KIND_LABELS, all_labels=False +): + since_date = since.date().isoformat() + queries = [] + if all_labels: + for kind_label in kind_labels: + queries.append( + " ".join( + [ + f"repo:{repo}", + "is:issue", + f"updated:>={since_date}", + quote_label(kind_label), + ] + ) + ) + return queries + for owner_label in owner_labels: + for kind_label in kind_labels: + queries.append( + " ".join( + [ + f"repo:{repo}", + "is:issue", + f"updated:>={since_date}", + quote_label(owner_label), + quote_label(kind_label), + ] + ) + ) + return queries + + +def _format_gh_error(cmd, err): + stdout = (err.stdout or "").strip() + stderr = (err.stderr or "").strip() + parts = [f"GitHub CLI command failed: {' '.join(cmd)}"] + if stdout: + parts.append(f"stdout: {stdout}") + if stderr: + parts.append(f"stderr: {stderr}") + return "\n".join(parts) + + +def gh_json(args): + cmd = ["gh", *args] + try: + proc = subprocess.run(cmd, check=True, capture_output=True, text=True) + except FileNotFoundError as err: + raise GhCommandError("`gh` command not found") from err + except subprocess.CalledProcessError as err: + raise GhCommandError(_format_gh_error(cmd, err)) from err + raw = proc.stdout.strip() + if not raw: + return None + try: + return json.loads(raw) + except json.JSONDecodeError as err: + raise GhCommandError( + f"Failed to parse JSON from gh output for {' '.join(args)}" + ) from err + + +def gh_text(args): + cmd = ["gh", *args] + try: + proc = subprocess.run(cmd, check=True, capture_output=True, text=True) + except (FileNotFoundError, subprocess.CalledProcessError): + return "" + return proc.stdout.strip() + + +def git_head(): + try: + proc = subprocess.run( + ["git", "rev-parse", "--short=12", "HEAD"], + check=True, + capture_output=True, + text=True, + ) + except (FileNotFoundError, subprocess.CalledProcessError): + return None + return proc.stdout.strip() or None + + +def skill_relative_path(): + try: + return str(Path(__file__).resolve().relative_to(Path.cwd().resolve())) + except ValueError: + return str(Path(__file__).resolve()) + + +def gh_api_list_paginated(endpoint, per_page=100, max_pages=None, with_metadata=False): + items = [] + page = 1 + truncated = False + while True: + sep = "&" if "?" in endpoint else "?" + page_endpoint = f"{endpoint}{sep}per_page={per_page}&page={page}" + payload = gh_json(["api", page_endpoint]) + if payload is None: + break + if not isinstance(payload, list): + raise GhCommandError(f"Unexpected paginated payload from gh api {endpoint}") + items.extend(payload) + if len(payload) < per_page: + break + if max_pages is not None and page >= max_pages: + truncated = True + break + page += 1 + if with_metadata: + return { + "items": items, + "truncated": truncated, + "pages": page, + "max_pages": max_pages, + } + return items + + +def search_issue_numbers(queries, limit): + numbers = {} + for query in queries: + page = 1 + while True: + payload = gh_json( + [ + "api", + "search/issues", + "-X", + "GET", + "-f", + f"q={query}", + "-f", + "per_page=100", + "-f", + f"page={page}", + ] + ) + if not isinstance(payload, dict): + raise GhCommandError("Unexpected payload from GitHub issue search") + items = payload.get("items") or [] + if not isinstance(items, list): + raise GhCommandError("Expected search `items` to be a list") + for item in items: + if not isinstance(item, dict): + continue + number = item.get("number") + if isinstance(number, int): + numbers[number] = str(item.get("updated_at") or "") + if len(items) < 100 or len(numbers) >= limit: + break + page += 1 + ordered = sorted( + numbers, key=lambda number: (numbers[number], number), reverse=True + ) + return ordered[:limit] + + +def fetch_issue(repo, number): + payload = gh_json(["api", f"repos/{repo}/issues/{number}"]) + if not isinstance(payload, dict): + raise GhCommandError(f"Unexpected issue payload for #{number}") + return payload + + +def fetch_comments(repo, number, since=None, max_pages=None): + endpoint = f"repos/{repo}/issues/{number}/comments" + if since is not None: + endpoint = f"{endpoint}?since={quote(format_timestamp(since), safe='')}" + return gh_api_list_paginated( + endpoint, + max_pages=max_pages, + with_metadata=True, + ) + + +def fetch_reactions_for_item(endpoint, item): + if reaction_summary(item)["total"] <= 0: + return [] + return gh_api_list_paginated(endpoint) + + +def fetch_comment_reactions(repo, comments): + reactions_by_comment_id = {} + for comment in comments: + comment_id = comment.get("id") + if comment_id in (None, ""): + continue + endpoint = f"repos/{repo}/issues/comments/{comment_id}/reactions" + reactions_by_comment_id[comment_id] = fetch_reactions_for_item( + endpoint, comment + ) + return reactions_by_comment_id + + +def extract_login(user_obj): + if isinstance(user_obj, dict): + return str(user_obj.get("login") or "") + return "" + + +def is_bot_login(login): + return bool(login) and login.lower().endswith("[bot]") + + +def is_human_user(user_obj): + login = extract_login(user_obj) + return bool(login) and not is_bot_login(login) + + +def label_names(issue): + labels = [] + for label in issue.get("labels") or []: + if isinstance(label, dict) and label.get("name"): + labels.append(str(label["name"])) + return sorted(labels, key=str.casefold) + + +def matching_labels(labels, requested): + labels_by_key = {label.casefold(): label for label in labels} + return [label for label in requested if label.casefold() in labels_by_key] + + +def area_labels(labels): + kind_keys = {label.casefold() for label in QUALIFYING_KIND_LABELS} + return [label for label in labels if label.casefold() not in kind_keys] + + +def attention_thresholds_for_window(window_hours): + if window_hours <= 0: + raise ValueError("window_hours must be > 0") + window_hours = round(window_hours, 6) + scale = window_hours / BASE_ATTENTION_WINDOW_HOURS + elevated = max(1, math.ceil(ONE_ATTENTION_INTERACTION_THRESHOLD * scale)) + very_high = max( + elevated + 1, math.ceil(TWO_ATTENTION_INTERACTION_THRESHOLD * scale) + ) + return { + "base_window_hours": BASE_ATTENTION_WINDOW_HOURS, + "window_hours": round(window_hours, 3), + "scale": round(scale, 3), + "elevated": elevated, + "very_high": very_high, + } + + +def attention_level_for(user_interactions, attention_thresholds=None): + thresholds = attention_thresholds or attention_thresholds_for_window( + BASE_ATTENTION_WINDOW_HOURS + ) + if user_interactions >= thresholds["very_high"]: + return 2 + if user_interactions >= thresholds["elevated"]: + return 1 + return 0 + + +def attention_marker_for(user_interactions, attention_thresholds=None): + return "🔥" * attention_level_for(user_interactions, attention_thresholds) + + +def reaction_summary(item): + reactions = item.get("reactions") + if not isinstance(reactions, dict): + return {"total": 0, "counts": {}} + counts = {} + for key in REACTION_KEYS: + value = reactions.get(key, 0) + if isinstance(value, int) and value: + counts[key] = value + total = reactions.get("total_count") + if not isinstance(total, int): + total = sum(counts.values()) + return {"total": total, "counts": counts} + + +def reaction_event_summary(reactions, since, until): + counts = {} + total = 0 + for reaction in reactions or []: + if not isinstance(reaction, dict): + continue + if not is_in_window(str(reaction.get("created_at") or ""), since, until): + continue + if not is_human_user(reaction.get("user")): + continue + content = str(reaction.get("content") or "") + if not content: + continue + counts[content] = counts.get(content, 0) + 1 + total += 1 + return { + "total": total, + "counts": counts, + "upvotes": counts.get("+1", 0), + } + + +def compact_text(value, limit): + text = re.sub(r"\s+", " ", str(value or "")).strip() + if limit <= 0: + return "" + if len(text) <= limit: + return text + return f"{text[: max(limit - 1, 0)].rstrip()}..." + + +def clean_title_for_description(title): + cleaned = re.sub(r"\s+", " ", str(title or "")).strip() + cleaned = re.sub( + r"^(codex(?: desktop| app|\.app| cli)?|desktop|windows codex app)\s*[:,-]\s*", + "", + cleaned, + flags=re.IGNORECASE, + ) + cleaned = re.sub(r"^on windows,\s*", "Windows: ", cleaned, flags=re.IGNORECASE) + cleaned = cleaned.strip(" -:;") + return compact_text(cleaned, 80) or "Issue needs owner review" + + +def issue_description(issue): + return clean_title_for_description(issue.get("title")) + + +def is_in_window(timestamp, since, until): + parsed = parse_timestamp(timestamp, "timestamp") + if parsed is None: + return False + return since <= parsed < until + + +def summarize_comment( + comment, comment_chars, reaction_events=None, since=None, until=None +): + reactions = reaction_summary(comment) + new_reactions = ( + reaction_event_summary(reaction_events, since, until) + if since is not None and until is not None + else {"total": 0, "counts": {}, "upvotes": 0} + ) + human_user_interaction = is_human_user(comment.get("user")) + return { + "id": comment.get("id"), + "author": extract_login(comment.get("user")), + "author_association": str(comment.get("author_association") or ""), + "created_at": str(comment.get("created_at") or ""), + "updated_at": str(comment.get("updated_at") or ""), + "url": str(comment.get("html_url") or ""), + "human_user_interaction": human_user_interaction, + "reactions": reactions["counts"], + "reaction_total": reactions["total"], + "new_reactions": new_reactions["total"], + "new_upvotes": new_reactions["upvotes"], + "new_reaction_counts": new_reactions["counts"], + "body_excerpt": compact_text(comment.get("body"), comment_chars), + } + + +def summarize_issue( + issue, + comments, + requested_labels, + since, + until, + body_chars, + comment_chars, + issue_reaction_events=None, + comment_reactions_by_id=None, + all_labels=False, + comments_hydration=None, + attention_thresholds=None, +): + labels = label_names(issue) + labels_by_key = {label.casefold() for label in labels} + kind_labels = [ + label for label in QUALIFYING_KIND_LABELS if label.casefold() in labels_by_key + ] + if all_labels: + owner_labels = area_labels(labels) or ["unlabeled"] + else: + owner_labels = matching_labels(labels, requested_labels) + if not kind_labels or not owner_labels: + return None + + updated_at = str(issue.get("updated_at") or "") + if not is_in_window(updated_at, since, until): + return None + + new_issue = is_in_window(str(issue.get("created_at") or ""), since, until) + comment_reactions_by_id = comment_reactions_by_id or {} + new_comments = [ + summarize_comment( + comment, + comment_chars, + reaction_events=comment_reactions_by_id.get(comment.get("id")), + since=since, + until=until, + ) + for comment in comments + if is_in_window(str(comment.get("created_at") or ""), since, until) + ] + new_comments.sort(key=lambda item: (item["created_at"], str(item["id"]))) + + issue_reactions = reaction_summary(issue) + issue_reaction_events_summary = reaction_event_summary( + issue_reaction_events, since, until + ) + comment_reaction_events_summary = reaction_event_summary( + [ + reaction + for reactions in comment_reactions_by_id.values() + for reaction in reactions + ], + since, + until, + ) + new_reactions = ( + issue_reaction_events_summary["total"] + + comment_reaction_events_summary["total"] + ) + new_upvotes = ( + issue_reaction_events_summary["upvotes"] + + comment_reaction_events_summary["upvotes"] + ) + all_comment_reaction_total = sum( + reaction_summary(comment)["total"] for comment in comments + ) + new_comment_reaction_total = sum( + comment["reaction_total"] for comment in new_comments + ) + new_issue_user_interaction = new_issue and is_human_user(issue.get("user")) + new_comment_user_interactions = sum( + 1 for comment in new_comments if comment["human_user_interaction"] + ) + user_interactions = ( + int(new_issue_user_interaction) + new_comment_user_interactions + new_reactions + ) + attention_level = attention_level_for(user_interactions, attention_thresholds) + attention_marker = attention_marker_for(user_interactions, attention_thresholds) + updated_without_visible_new_post = ( + not new_issue and not new_comments and new_reactions == 0 + ) + + engagement_score = ( + len(new_comments) * 3 + + new_reactions + + issue_reactions["total"] + + new_comment_reaction_total + + min(int(issue.get("comments") or len(comments) or 0), 10) + ) + + return { + "number": issue.get("number"), + "title": str(issue.get("title") or ""), + "description": issue_description(issue), + "url": str(issue.get("html_url") or ""), + "state": str(issue.get("state") or ""), + "author": extract_login(issue.get("user")), + "author_association": str(issue.get("author_association") or ""), + "created_at": str(issue.get("created_at") or ""), + "updated_at": updated_at, + "labels": labels, + "kind_labels": kind_labels, + "owner_labels": owner_labels, + "comments_total": int(issue.get("comments") or len(comments) or 0), + "comments_hydration": comments_hydration + or { + "fetched": len(comments), + "since": None, + "truncated": False, + "max_pages": None, + }, + "issue_reactions": issue_reactions["counts"], + "issue_reaction_total": issue_reactions["total"], + "comment_reaction_total": all_comment_reaction_total, + "new_comment_reaction_total": new_comment_reaction_total, + "new_issue_reactions": issue_reaction_events_summary["total"], + "new_issue_upvotes": issue_reaction_events_summary["upvotes"], + "new_comment_reactions": comment_reaction_events_summary["total"], + "new_comment_upvotes": comment_reaction_events_summary["upvotes"], + "new_reactions": new_reactions, + "new_upvotes": new_upvotes, + "user_interactions": user_interactions, + "attention": attention_level > 0, + "attention_level": attention_level, + "attention_marker": attention_marker, + "engagement_score": engagement_score, + "activity": { + "new_issue": new_issue, + "new_comments": len(new_comments), + "new_human_comments": new_comment_user_interactions, + "new_reactions": new_reactions, + "new_upvotes": new_upvotes, + "updated_without_visible_new_post": updated_without_visible_new_post, + }, + "body_excerpt": compact_text(issue.get("body"), body_chars), + "new_comments": new_comments, + } + + +def count_by_label(issues, labels): + out = {} + for label in labels: + matching = [issue for issue in issues if label in issue["owner_labels"]] + out[label] = { + "issues": len(matching), + "new_issues": sum( + 1 for issue in matching if issue["activity"]["new_issue"] + ), + "new_comments": sum( + issue["activity"]["new_comments"] for issue in matching + ), + } + return out + + +def count_by_kind(issues): + out = {} + for kind in QUALIFYING_KIND_LABELS: + matching = [issue for issue in issues if kind in issue["kind_labels"]] + out[kind] = { + "issues": len(matching), + "new_issues": sum( + 1 for issue in matching if issue["activity"]["new_issue"] + ), + "new_comments": sum( + issue["activity"]["new_comments"] for issue in matching + ), + } + return out + + +def hot_items(issues, limit=8): + ranked = sorted( + issues, + key=lambda issue: ( + issue["attention"], + issue["attention_level"], + issue["user_interactions"], + issue["engagement_score"], + issue["activity"]["new_comments"], + issue["issue_reaction_total"] + issue["comment_reaction_total"], + issue["updated_at"], + ), + reverse=True, + ) + return [ + { + "number": issue["number"], + "title": issue["title"], + "url": issue["url"], + "owner_labels": issue["owner_labels"], + "kind_labels": issue["kind_labels"], + "attention": issue["attention"], + "attention_level": issue["attention_level"], + "attention_marker": issue["attention_marker"], + "user_interactions": issue["user_interactions"], + "new_reactions": issue["new_reactions"], + "new_upvotes": issue["new_upvotes"], + "engagement_score": issue["engagement_score"], + "new_comments": issue["activity"]["new_comments"], + "reaction_total": issue["issue_reaction_total"] + + issue["comment_reaction_total"], + } + for issue in ranked[:limit] + if issue["engagement_score"] > 0 + ] + + +def ranked_digest_issues(issues): + return sorted( + issues, + key=lambda issue: ( + issue["attention"], + issue["attention_level"], + issue["user_interactions"], + issue["engagement_score"], + issue["activity"]["new_comments"], + issue["updated_at"], + ), + reverse=True, + ) + + +def digest_rows(issues, limit=10, ref_map=None): + ranked = ranked_digest_issues(issues) + if ref_map is None: + ref_map = {issue["number"]: ref for ref, issue in enumerate(ranked, start=1)} + rows = [] + for issue in ranked[:limit]: + ref = ref_map[issue["number"]] + reaction_total = issue["issue_reaction_total"] + issue["comment_reaction_total"] + rows.append( + { + "ref": ref, + "ref_markdown": f"[{ref}]({issue['url']})", + "marker": issue["attention_marker"], + "attention_marker": issue["attention_marker"], + "number": issue["number"], + "description": issue["description"], + "title": issue["title"], + "url": issue["url"], + "area": ", ".join(issue["owner_labels"]), + "kind": ", ".join(issue["kind_labels"]), + "state": issue["state"], + "interactions": issue["user_interactions"], + "user_interactions": issue["user_interactions"], + "new_reactions": issue["new_reactions"], + "new_upvotes": issue["new_upvotes"], + "current_reactions": reaction_total, + } + ) + return rows + + +def issue_ref_markdown(issue, ref_map): + ref = ref_map[issue["number"]] + return f"[{ref}]({issue['url']})" + + +def summary_inputs(issues, limit=80, ref_map=None): + ranked = ranked_digest_issues(issues) + if ref_map is None: + ref_map = {issue["number"]: ref for ref, issue in enumerate(ranked, start=1)} + rows = [] + for issue in ranked[:limit]: + rows.append( + { + "ref": ref_map[issue["number"]], + "ref_markdown": issue_ref_markdown(issue, ref_map), + "number": issue["number"], + "title": issue["title"], + "description": issue["description"], + "url": issue["url"], + "labels": issue["labels"], + "owner_labels": issue["owner_labels"], + "kind_labels": issue["kind_labels"], + "state": issue.get("state", ""), + "attention_marker": issue.get("attention_marker", ""), + "interactions": issue["user_interactions"], + "new_comments": issue["activity"].get("new_comments", 0), + "new_reactions": issue.get("new_reactions", 0), + "new_upvotes": issue.get("new_upvotes", 0), + "current_reactions": issue.get("issue_reaction_total", 0) + + issue.get("comment_reaction_total", 0), + } + ) + return rows + + +def collect_digest(args): + since, until = resolve_window(args) + window_hours = (until - since).total_seconds() / 3600 + attention_thresholds = attention_thresholds_for_window(window_hours) + requested_labels, all_labels = normalize_requested_labels( + args.labels, all_labels=args.all_labels + ) + queries = build_search_queries( + args.repo, requested_labels, since, all_labels=all_labels + ) + numbers = search_issue_numbers(queries, args.limit_issues) + gh_version_output = gh_text(["--version"]) + + issues = [] + max_comment_pages = None if args.max_comment_pages <= 0 else args.max_comment_pages + for number in numbers: + issue = fetch_issue(args.repo, number) + comments_since = None if args.fetch_all_comments else since + comments_payload = fetch_comments( + args.repo, + number, + since=comments_since, + max_pages=max_comment_pages, + ) + comments = comments_payload["items"] + issue_reaction_events = fetch_reactions_for_item( + f"repos/{args.repo}/issues/{number}/reactions", issue + ) + comment_reactions_by_id = fetch_comment_reactions(args.repo, comments) + comments_hydration = { + "fetched": len(comments), + "total": int(issue.get("comments") or len(comments) or 0), + "since": format_timestamp(comments_since) if comments_since else None, + "truncated": comments_payload["truncated"], + "max_pages": comments_payload["max_pages"], + "fetch_all_comments": args.fetch_all_comments, + } + summary = summarize_issue( + issue, + comments, + requested_labels, + since, + until, + args.body_chars, + args.comment_chars, + issue_reaction_events=issue_reaction_events, + comment_reactions_by_id=comment_reactions_by_id, + all_labels=all_labels, + comments_hydration=comments_hydration, + attention_thresholds=attention_thresholds, + ) + if summary is not None: + issues.append(summary) + + issues.sort( + key=lambda issue: (issue["updated_at"], int(issue["number"] or 0)), reverse=True + ) + totals = { + "candidate_issues": len(numbers), + "included_issues": len(issues), + "new_issues": sum(1 for issue in issues if issue["activity"]["new_issue"]), + "issues_with_new_comments": sum( + 1 for issue in issues if issue["activity"]["new_comments"] > 0 + ), + "new_comments": sum(issue["activity"]["new_comments"] for issue in issues), + "comments_fetched": sum( + issue["comments_hydration"]["fetched"] for issue in issues + ), + "issues_with_truncated_comment_hydration": sum( + 1 for issue in issues if issue["comments_hydration"]["truncated"] + ), + "updated_without_visible_new_post": sum( + 1 + for issue in issues + if issue["activity"]["updated_without_visible_new_post"] + ), + "issue_reactions_current_total": sum( + issue["issue_reaction_total"] for issue in issues + ), + "comment_reactions_current_total": sum( + issue["comment_reaction_total"] for issue in issues + ), + "new_reactions": sum(issue["new_reactions"] for issue in issues), + "new_upvotes": sum(issue["new_upvotes"] for issue in issues), + "user_interactions": sum(issue["user_interactions"] for issue in issues), + } + ranked = ranked_digest_issues(issues) + ref_map = {issue["number"]: ref for ref, issue in enumerate(ranked, start=1)} + filter_label = "all" if all_labels else requested_labels + + return { + "generated_at": format_timestamp(datetime.now(timezone.utc)), + "source": { + "repo": args.repo, + "skill": "codex-issue-digest", + "collector": skill_relative_path(), + "script_version": SCRIPT_VERSION, + "git_head": git_head(), + "gh_version": gh_version_output.splitlines()[0] + if gh_version_output + else None, + }, + "window": { + "since": format_timestamp(since), + "until": format_timestamp(until), + "hours": round(window_hours, 3), + }, + "attention_thresholds": attention_thresholds, + "filters": { + "owner_labels": filter_label, + "all_labels": all_labels, + "kind_labels": list(QUALIFYING_KIND_LABELS), + }, + "collection_notes": [ + "Issues are selected when they currently have bug or enhancement plus at least one requested owner label and were updated during the window.", + "By default, issue comments are fetched with since=window_start and a max page cap to avoid long historical threads; use --fetch-all-comments when exhaustive comment history is needed.", + "New issue comments are filtered by comment creation time within the window from the fetched comment set.", + "Reaction events are counted by GitHub reaction created_at timestamps for hydrated issues and fetched comments.", + "Current reaction totals are standing engagement signals; new_reactions and new_upvotes are windowed activity.", + "The collector does not assign semantic clusters; use summary_inputs as model-ready evidence for report-time clustering.", + "Pure reaction-only issues may be missed if GitHub issue search does not surface them via updated_at.", + "Issues updated during the window without a new issue body or new comment are retained because label/status edits can still be useful owner signals.", + ], + "totals": totals, + "by_owner_label": count_by_label( + issues, + sorted( + {area for issue in issues for area in issue["owner_labels"]}, + key=str.casefold, + ) + if all_labels + else requested_labels, + ), + "by_kind_label": count_by_kind(issues), + "hot_items": hot_items(issues), + "summary_inputs": summary_inputs(issues, ref_map=ref_map), + "digest_rows": digest_rows(issues, ref_map=ref_map), + "issues": issues, + } + + +def main(): + args = parse_args() + try: + digest = collect_digest(args) + except (GhCommandError, RuntimeError, ValueError) as err: + sys.stderr.write(f"collect_issue_digest.py error: {err}\n") + return 1 + sys.stdout.write(json.dumps(digest, indent=2, sort_keys=True) + "\n") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.codex/skills/codex-issue-digest/scripts/test_collect_issue_digest.py b/.codex/skills/codex-issue-digest/scripts/test_collect_issue_digest.py new file mode 100644 index 000000000000..1c283ea2f694 --- /dev/null +++ b/.codex/skills/codex-issue-digest/scripts/test_collect_issue_digest.py @@ -0,0 +1,614 @@ +import importlib.util +from datetime import timezone +from pathlib import Path + + +MODULE_PATH = Path(__file__).with_name("collect_issue_digest.py") +MODULE_SPEC = importlib.util.spec_from_file_location( + "collect_issue_digest", MODULE_PATH +) +collect_issue_digest = importlib.util.module_from_spec(MODULE_SPEC) +assert MODULE_SPEC.loader is not None +MODULE_SPEC.loader.exec_module(collect_issue_digest) + + +def test_build_search_queries_uses_each_owner_and_kind_label(): + since = collect_issue_digest.parse_timestamp("2026-04-25T12:34:56Z", "--since") + + queries = collect_issue_digest.build_search_queries( + "openai/codex", ["tui", "exec"], since + ) + + assert queries == [ + "repo:openai/codex is:issue updated:>=2026-04-25 label:tui label:bug", + "repo:openai/codex is:issue updated:>=2026-04-25 label:tui label:enhancement", + "repo:openai/codex is:issue updated:>=2026-04-25 label:exec label:bug", + "repo:openai/codex is:issue updated:>=2026-04-25 label:exec label:enhancement", + ] + + +def test_build_search_queries_can_scan_all_labels(): + since = collect_issue_digest.parse_timestamp("2026-04-25T12:34:56Z", "--since") + + queries = collect_issue_digest.build_search_queries( + "openai/codex", [], since, all_labels=True + ) + + assert queries == [ + "repo:openai/codex is:issue updated:>=2026-04-25 label:bug", + "repo:openai/codex is:issue updated:>=2026-04-25 label:enhancement", + ] + + +def test_normalize_requested_labels_accepts_all_area_phrases(): + assert collect_issue_digest.normalize_requested_labels(["all", "areas"]) == ( + [], + True, + ) + assert collect_issue_digest.normalize_requested_labels(["all-labels"]) == ( + [], + True, + ) + + +def test_summarize_issue_keeps_new_comments_and_reaction_signals(): + since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since") + until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until") + issue = { + "number": 123, + "title": "TUI does not redraw", + "html_url": "https://github.com/openai/codex/issues/123", + "state": "open", + "created_at": "2026-04-24T20:00:00Z", + "updated_at": "2026-04-25T10:00:00Z", + "user": {"login": "alice"}, + "author_association": "NONE", + "comments": 2, + "body": "The terminal freezes after resize.", + "labels": [{"name": "bug"}, {"name": "tui"}], + "reactions": {"total_count": 3, "+1": 2, "rocket": 1}, + } + comments = [ + { + "id": 1, + "created_at": "2026-04-25T11:00:00Z", + "updated_at": "2026-04-25T11:00:00Z", + "html_url": "https://github.com/openai/codex/issues/123#issuecomment-1", + "user": {"login": "bob"}, + "author_association": "MEMBER", + "body": "I can reproduce this on main.", + "reactions": {"total_count": 4, "heart": 1, "+1": 3}, + }, + { + "id": 2, + "created_at": "2026-04-24T11:00:00Z", + "updated_at": "2026-04-24T11:00:00Z", + "html_url": "https://github.com/openai/codex/issues/123#issuecomment-2", + "user": {"login": "carol"}, + "author_association": "NONE", + "body": "Older comment.", + "reactions": {"total_count": 1, "eyes": 1}, + }, + ] + + summary = collect_issue_digest.summarize_issue( + issue, + comments, + ["tui", "exec"], + since, + until, + body_chars=200, + comment_chars=200, + ) + + assert summary == { + "number": 123, + "title": "TUI does not redraw", + "description": "TUI does not redraw", + "url": "https://github.com/openai/codex/issues/123", + "state": "open", + "author": "alice", + "author_association": "NONE", + "created_at": "2026-04-24T20:00:00Z", + "updated_at": "2026-04-25T10:00:00Z", + "labels": ["bug", "tui"], + "kind_labels": ["bug"], + "owner_labels": ["tui"], + "comments_total": 2, + "comments_hydration": { + "fetched": 2, + "since": None, + "truncated": False, + "max_pages": None, + }, + "issue_reactions": {"+1": 2, "rocket": 1}, + "issue_reaction_total": 3, + "comment_reaction_total": 5, + "new_comment_reaction_total": 4, + "new_issue_reactions": 0, + "new_issue_upvotes": 0, + "new_comment_reactions": 0, + "new_comment_upvotes": 0, + "new_reactions": 0, + "new_upvotes": 0, + "user_interactions": 1, + "attention": False, + "attention_level": 0, + "attention_marker": "", + "engagement_score": 12, + "activity": { + "new_issue": False, + "new_comments": 1, + "new_human_comments": 1, + "new_reactions": 0, + "new_upvotes": 0, + "updated_without_visible_new_post": False, + }, + "body_excerpt": "The terminal freezes after resize.", + "new_comments": [ + { + "id": 1, + "author": "bob", + "author_association": "MEMBER", + "created_at": "2026-04-25T11:00:00Z", + "updated_at": "2026-04-25T11:00:00Z", + "url": "https://github.com/openai/codex/issues/123#issuecomment-1", + "human_user_interaction": True, + "reactions": {"+1": 3, "heart": 1}, + "reaction_total": 4, + "new_reactions": 0, + "new_upvotes": 0, + "new_reaction_counts": {}, + "body_excerpt": "I can reproduce this on main.", + } + ], + } + + +def test_summarize_issue_filters_non_owner_or_non_kind_labels(): + since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since") + until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until") + base_issue = { + "number": 1, + "title": "Question", + "created_at": "2026-04-25T01:00:00Z", + "updated_at": "2026-04-25T01:00:00Z", + "labels": [{"name": "question"}, {"name": "tui"}], + } + + assert ( + collect_issue_digest.summarize_issue( + base_issue, + [], + ["tui"], + since, + until, + body_chars=100, + comment_chars=100, + ) + is None + ) + + issue_without_owner = dict(base_issue) + issue_without_owner["labels"] = [{"name": "bug"}, {"name": "app"}] + + assert ( + collect_issue_digest.summarize_issue( + issue_without_owner, + [], + ["tui"], + since, + until, + body_chars=100, + comment_chars=100, + ) + is None + ) + + +def test_resolve_window_defaults_to_previous_hours(): + class Args: + since = None + until = "2026-04-26T12:00:00Z" + window_hours = 24 + + since, until = collect_issue_digest.resolve_window(Args()) + + assert since.isoformat() == "2026-04-25T12:00:00+00:00" + assert until.tzinfo == timezone.utc + + +def test_parse_duration_hours_accepts_common_phrases(): + assert collect_issue_digest.parse_duration_hours("past week") == 168 + assert collect_issue_digest.parse_duration_hours("48h") == 48 + assert collect_issue_digest.parse_duration_hours("2 days") == 48 + assert collect_issue_digest.parse_duration_hours("1w") == 168 + + +def test_attention_thresholds_scale_by_window_length(): + one_day = collect_issue_digest.attention_thresholds_for_window(24) + assert one_day["elevated"] == 10 + assert one_day["very_high"] == 20 + + half_day = collect_issue_digest.attention_thresholds_for_window(12) + assert half_day["elevated"] == 5 + assert half_day["very_high"] == 10 + + week = collect_issue_digest.attention_thresholds_for_window(168) + assert week["elevated"] == 70 + assert week["very_high"] == 140 + assert collect_issue_digest.attention_marker_for(69, week) == "" + assert collect_issue_digest.attention_marker_for(107, week) == "🔥" + assert collect_issue_digest.attention_marker_for(140, week) == "🔥🔥" + + +def test_fetch_comments_uses_since_filter_and_page_cap(monkeypatch): + calls = [] + + def fake_gh_json(args): + calls.append(args) + return [{"id": idx} for idx in range(100)] + + monkeypatch.setattr(collect_issue_digest, "gh_json", fake_gh_json) + since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since") + + payload = collect_issue_digest.fetch_comments( + "openai/codex", 123, since=since, max_pages=1 + ) + + assert len(payload["items"]) == 100 + assert payload["truncated"] is True + assert payload["max_pages"] == 1 + assert calls == [ + [ + "api", + "repos/openai/codex/issues/123/comments?since=2026-04-25T00%3A00%3A00Z&per_page=100&page=1", + ] + ] + + +def test_issue_description_prefers_title_over_body_noise(): + issue = { + "title": "Codex.app GUI: MCP child processes not reaped after task completion", + "body": "A later crash mention should not override the title-level symptom.", + "labels": [{"name": "app"}, {"name": "bug"}], + } + + description = collect_issue_digest.issue_description(issue) + assert "MCP child processes" in description + assert "crash" not in description.casefold() + + +def test_attention_markers_count_human_user_interactions(): + since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since") + until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until") + issue = { + "number": 456, + "title": "Agent context is exploding", + "html_url": "https://github.com/openai/codex/issues/456", + "state": "open", + "created_at": "2026-04-25T01:00:00Z", + "updated_at": "2026-04-25T12:00:00Z", + "user": {"login": "alice"}, + "labels": [{"name": "bug"}, {"name": "agent"}], + } + comments = [ + { + "id": idx, + "created_at": "2026-04-25T02:00:00Z", + "updated_at": "2026-04-25T02:00:00Z", + "user": {"login": f"user-{idx}"}, + "body": "same here", + } + for idx in range(9) + ] + comments.append( + { + "id": 99, + "created_at": "2026-04-25T02:00:00Z", + "updated_at": "2026-04-25T02:00:00Z", + "user": {"login": "github-actions[bot]"}, + "body": "duplicate bot note", + } + ) + + summary = collect_issue_digest.summarize_issue( + issue, + comments, + ["agent"], + since, + until, + body_chars=100, + comment_chars=100, + ) + + assert summary["user_interactions"] == 10 + assert summary["activity"]["new_human_comments"] == 9 + assert summary["attention"] is True + assert summary["attention_level"] == 1 + assert summary["attention_marker"] == "🔥" + + issue["created_at"] = "2026-04-24T01:00:00Z" + comments.extend( + { + "id": idx, + "created_at": "2026-04-25T03:00:00Z", + "updated_at": "2026-04-25T03:00:00Z", + "user": {"login": f"extra-user-{idx}"}, + "body": "also seeing this", + } + for idx in range(11) + ) + + summary = collect_issue_digest.summarize_issue( + issue, + comments, + ["agent"], + since, + until, + body_chars=100, + comment_chars=100, + ) + + assert summary["user_interactions"] == 20 + assert summary["attention_level"] == 2 + assert summary["attention_marker"] == "🔥🔥" + + +def test_reactions_count_toward_attention_markers(): + since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since") + until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until") + issue = { + "number": 789, + "title": "Support 1M token context", + "html_url": "https://github.com/openai/codex/issues/789", + "state": "open", + "created_at": "2026-04-24T01:00:00Z", + "updated_at": "2026-04-25T12:00:00Z", + "user": {"login": "alice"}, + "labels": [{"name": "enhancement"}, {"name": "context"}], + "reactions": {"total_count": 20, "+1": 20}, + } + comments = [ + { + "id": 1, + "created_at": "2026-04-25T02:00:00Z", + "updated_at": "2026-04-25T02:00:00Z", + "user": {"login": "commenter"}, + "body": "please", + "reactions": {"total_count": 2, "+1": 2}, + } + ] + issue_reactions = [ + { + "content": "+1", + "created_at": "2026-04-25T03:00:00Z", + "user": {"login": f"reactor-{idx}"}, + } + for idx in range(18) + ] + comment_reactions_by_id = { + 1: [ + { + "content": "heart", + "created_at": "2026-04-25T04:00:00Z", + "user": {"login": "human-reactor"}, + }, + { + "content": "+1", + "created_at": "2026-04-25T04:00:00Z", + "user": {"login": "github-actions[bot]"}, + }, + ] + } + + summary = collect_issue_digest.summarize_issue( + issue, + comments, + ["context"], + since, + until, + body_chars=100, + comment_chars=100, + issue_reaction_events=issue_reactions, + comment_reactions_by_id=comment_reactions_by_id, + ) + + assert summary["new_reactions"] == 19 + assert summary["new_upvotes"] == 18 + assert summary["user_interactions"] == 20 + assert summary["attention_level"] == 2 + assert summary["attention_marker"] == "🔥🔥" + assert summary["new_comments"][0]["new_reactions"] == 1 + assert summary["new_comments"][0]["new_upvotes"] == 0 + + +def test_digest_rows_are_table_ready_with_concise_descriptions(): + rows = collect_issue_digest.digest_rows( + [ + { + "number": 1, + "title": "Quiet bug", + "description": "Quiet bug", + "url": "https://github.com/openai/codex/issues/1", + "owner_labels": ["context"], + "kind_labels": ["bug"], + "state": "open", + "attention": False, + "attention_level": 0, + "attention_marker": "", + "user_interactions": 1, + "new_reactions": 0, + "new_upvotes": 0, + "engagement_score": 3, + "issue_reaction_total": 0, + "comment_reaction_total": 0, + "updated_at": "2026-04-25T01:00:00Z", + "activity": { + "new_issue": True, + "new_comments": 0, + "new_reactions": 0, + "updated_without_visible_new_post": False, + }, + }, + { + "number": 2, + "title": "Busy bug", + "description": "High-volume bug report", + "url": "https://github.com/openai/codex/issues/2", + "owner_labels": ["agent"], + "kind_labels": ["bug"], + "state": "open", + "attention": True, + "attention_level": 1, + "attention_marker": "🔥", + "user_interactions": 17, + "new_reactions": 3, + "new_upvotes": 2, + "engagement_score": 20, + "issue_reaction_total": 5, + "comment_reaction_total": 2, + "updated_at": "2026-04-25T02:00:00Z", + "activity": { + "new_issue": False, + "new_comments": 16, + "new_reactions": 3, + "updated_without_visible_new_post": False, + }, + }, + ] + ) + + assert rows[0] == { + "ref": 1, + "ref_markdown": "[1](https://github.com/openai/codex/issues/2)", + "marker": "🔥", + "attention_marker": "🔥", + "number": 2, + "description": "High-volume bug report", + "title": "Busy bug", + "url": "https://github.com/openai/codex/issues/2", + "area": "agent", + "kind": "bug", + "state": "open", + "interactions": 17, + "user_interactions": 17, + "new_reactions": 3, + "new_upvotes": 2, + "current_reactions": 7, + } + + +def test_summary_inputs_are_model_ready_without_preclustering(): + issues = [ + { + "number": 20, + "title": "Windows app Browser Use external navigation fails", + "description": "Browser Use navigation or app-server failure", + "url": "https://github.com/openai/codex/issues/20", + "labels": ["app", "bug"], + "owner_labels": ["app"], + "kind_labels": ["bug"], + "attention": False, + "attention_level": 0, + "attention_marker": "", + "user_interactions": 3, + "new_reactions": 1, + "engagement_score": 8, + "updated_at": "2026-04-25T04:00:00Z", + "activity": {"new_comments": 2}, + }, + { + "number": 21, + "title": "On Windows, cmake output waits until timeout", + "description": "Windows command timeout/capture problem", + "url": "https://github.com/openai/codex/issues/21", + "labels": ["app", "bug"], + "owner_labels": ["app"], + "kind_labels": ["bug"], + "attention": False, + "attention_level": 0, + "attention_marker": "", + "user_interactions": 3, + "new_reactions": 0, + "engagement_score": 7, + "updated_at": "2026-04-25T03:00:00Z", + "activity": {"new_comments": 3}, + }, + { + "number": 22, + "title": "Windows computer use tool fails to click buttons", + "description": "Computer-use workflow failure", + "url": "https://github.com/openai/codex/issues/22", + "labels": ["app", "bug"], + "owner_labels": ["app"], + "kind_labels": ["bug"], + "attention": False, + "attention_level": 0, + "attention_marker": "", + "user_interactions": 3, + "new_reactions": 0, + "engagement_score": 6, + "updated_at": "2026-04-25T02:00:00Z", + "activity": {"new_comments": 3}, + }, + ] + + rows = collect_issue_digest.summary_inputs(issues, ref_map={20: 1, 21: 2, 22: 3}) + + assert rows == [ + { + "ref": 1, + "ref_markdown": "[1](https://github.com/openai/codex/issues/20)", + "number": 20, + "title": "Windows app Browser Use external navigation fails", + "description": "Browser Use navigation or app-server failure", + "url": "https://github.com/openai/codex/issues/20", + "labels": ["app", "bug"], + "owner_labels": ["app"], + "kind_labels": ["bug"], + "state": "", + "attention_marker": "", + "interactions": 3, + "new_comments": 2, + "new_reactions": 1, + "new_upvotes": 0, + "current_reactions": 0, + }, + { + "ref": 2, + "ref_markdown": "[2](https://github.com/openai/codex/issues/21)", + "number": 21, + "title": "On Windows, cmake output waits until timeout", + "description": "Windows command timeout/capture problem", + "url": "https://github.com/openai/codex/issues/21", + "labels": ["app", "bug"], + "owner_labels": ["app"], + "kind_labels": ["bug"], + "state": "", + "attention_marker": "", + "interactions": 3, + "new_comments": 3, + "new_reactions": 0, + "new_upvotes": 0, + "current_reactions": 0, + }, + { + "ref": 3, + "ref_markdown": "[3](https://github.com/openai/codex/issues/22)", + "number": 22, + "title": "Windows computer use tool fails to click buttons", + "description": "Computer-use workflow failure", + "url": "https://github.com/openai/codex/issues/22", + "labels": ["app", "bug"], + "owner_labels": ["app"], + "kind_labels": ["bug"], + "state": "", + "attention_marker": "", + "interactions": 3, + "new_comments": 3, + "new_reactions": 0, + "new_upvotes": 0, + "current_reactions": 0, + }, + ]