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Version 0.9.0 MIT License 8 commands PRs Welcome

Turn GitHub Issues Into Structured, Agent-Ready Work Orders

Eight terminal commands that structure, analyze, triage, resolve, review, and self-check GitHub issue workflows — so any developer or AI agent can pick up an issue and ship a tested PR.

Get Started · What is IDD? · Intention-Driven Development · Why Good Issues & Commits Matter · Works With Any Tool


The Problem

Unstructured issues kill velocity — and destroy your project history.

Someone files "the login is broken on mobile." A developer — human or AI — spends 30 minutes figuring out which files to open before writing a line of code. An AI agent produces changes to the wrong files because the issue lacked context. Meanwhile, 47 open issues sit in the backlog with no dependency awareness and no execution order.

Worse: when issues are vague, the commits that resolve them are vague too. Six months later, git log reads like "fix stuff", "update", "WIP". Nobody can trace why a change was made, what problem it solved, or what decision led to that approach. The development history — which should be your project's most valuable knowledge base — becomes noise.

GitHub issues were designed for humans to read, not for agents to execute. And commit messages were designed to tell a story, not to fill a required field.

The Fix

gitissue turns every GitHub issue into a self-contained work order: typed, structured, and enriched with acceptance criteria. Then it resolves them — with commit messages and PR titles that link every line of code back to the intention that created it.

graph LR
    A["Describe a problem"] --> B["/issue-creator"]
    B --> C["Structured issue"]
    C --> D["/issue-triage"]
    D --> E["/issue-analysis N"]
    E --> F["/issue-resolver N"]
    F --> G["Tested PR that closes the issue"]
    D -.-> H["/auto-pilot"]
    H -.-> G

    style A fill:#4CAF50,color:#fff
    style G fill:#2196F3,color:#fff
    style H fill:#FF9800,color:#fff
Loading
Command What it does Version Effort
/issue-creator Classify type, generate acceptance criteria, create a structured issue 0.4.1 medium
/issue-analysis N Root cause, git history, implementation options, complexity and risk 0.4.1 high
/issue-resolver N 6-step pipeline: preflight, research, plan, implement, QA, deliver PR with Closes #N 0.7.1 max
/issue-triage Dependency graph, stale detection, already-fixed detection via commit/PR scanning, priority and execution order 0.5.2 medium
/init-gitissue Auto-detect language/framework/test runner, generate .gitissue.yml 0.3.2 low
/auto-pilot Triage → resolve → review → merge loop. Conservative-by-default merge modes (conservative/balanced/aggressive) and explicit issue lists for targeted runs 2.2.0 max
/issue-pr-review Review PR end-to-end: script pre-pass (lint/format/test auto-fix), per-criterion AC verification, five-dimension scoring (correctness, acceptance_criteria, traceability, maintainability, safety), reuses reviewer/fixer agents across cycles 0.5.0 high

Internal tooling

Internal-only skills are not published in the public skill index and are not built into dist/. They live alongside the source for repo maintainers to invoke directly from a working tree.

Skill Folder Purpose
/idd-doctor src/internal-skills/idd-doctor/ Read-only health check for IDD repository invariants — runs against the local src/ checkout; not distributed

How It Works

1. Create a structured issue

issue-creator terminal output

Describe a bug, feature, or improvement in plain text. gitissue classifies it, generates acceptance criteria, and creates a GitHub issue with labels.

2. Resolve it in one command

issue-resolver terminal output

Six steps run automatically: preflight, research, plan, implement, QA, and deliver a PR with Closes #N.

3. Triage the backlog

issue-triage terminal output

Dependency detection, priority suggestions, parallelizable work, stale issue warnings, and already-fixed detection — one command for the entire backlog.

4. Deep-dive on a single issue

  [1/8] Fetch          ✓ issue #42 loaded (bug)
  [2/8] Extract        ✓ 8 keywords, 2 file refs
  [3/8] Research       ✓ read 18 files, traced 12 deps
  [4/8] History        ✓ 5 related commits, 1 regression
  [5/8] Cross-refs     ✓ 2 related issues
  [6/8] Analysis       ✓ root cause identified
  [7/8] Options        ✓ 3 approaches proposed
  [8/8] Report         ✓ saved to .gitissue/analysis-42.json

5. Go hands-free with auto-pilot

  ◆ auto-pilot        starting (5 open issues)
  ┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄
  [1/5] #42            ✓ resolved → PR #51 merged
  [2/5] #38            ✓ resolved → PR #52 merged
  [3/5] #35            ✗ stopped — test failures after 2 fix cycles
  [4/5] #29            ✓ resolved → PR #53 merged
  [5/5] #21            ✓ resolved → PR #54 merged
  ┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄
  ◆ complete           4/5 resolved, 1 stopped

Triage, resolve, review, merge — repeated for every open issue. Up to three review-fix cycles per PR with a script pre-pass (lint/format/test auto-fix) before any LLM cycle is spent. Conservative by default: clean PRs are merged, partial PRs are left open for review unless autopilot.mode: aggressive is explicitly set. Supports explicit issue lists for targeted runs.


Works With Any Tool

IDD is a methodology, not a vendor lock-in. The structured issue format is plain GitHub markdown — any tool that reads GitHub issues can consume it. gitissue adds structure to your issues; your existing tools keep working exactly as before.

Tool How it works with gitissue
Claude Code Load skills directly — /issue-creator, /issue-resolver
Codex CLI gh issue view 42 --json body and pass to codex as context
Gemini CLI Pipe issue body to gemini for resolution
GitHub Copilot Structured issues give Copilot better context for suggestions
Any SKILL.md agent Load skills from src/skills/ directory
Human developers Read the issue — acceptance criteria and structure are right there

gitissue is complementary to your existing workflow. Use it alongside TDD, BDD, CI/CD pipelines, project management tools, or any AI coding agent. It fills one gap — structuring and triaging issues — and stays out of the way for everything else.


Get Started

Prerequisites

  • GitHub CLI (gh) 2.0+, authenticated via gh auth login
  • Claude Code or any SKILL.md-compatible agent
  • Git 2.30+

Install

Standalone is the recommended default. Each skill ships as a self-contained directory under dist/skills/<name>/, ready to drop into any SKILL.md-compatible harness — Claude Code, Codex CLI, or anything else that loads SKILL.md trees.

Recommended — Standalone via asm install

asm is the primary install tool. It pulls the skill straight from this repo (no clone required) and writes it where Claude Code looks for skills. Pick one skill or install them all:

# One skill (most common):
asm install github:luongnv89/idd#main:dist/skills/issue-resolver

# Every public skill in one loop:
for skill in issue-creator issue-analysis issue-resolver issue-triage \
             issue-pr-review auto-pilot init-gitissue; do
  asm install "github:luongnv89/idd#main:dist/skills/$skill" -p claude -y
done

# Shared Claude Code agents used by those skills:
mkdir -p "$HOME/.claude/agents"
for agent in code-reviewer codebase-researcher duplicate-detector \
             implementer issue-relationship-scanner synthesizer; do
  dst="$HOME/.claude/agents/$agent.md"
  if [ -f "$dst" ] && ! grep -q 'Managed by IDD installer' "$dst"; then
    echo "Skipping unmanaged existing agent: $dst"
    continue
  fi
  curl -fsSL "https://raw.githubusercontent.com/luongnv89/idd/main/dist/agents/$agent.md" -o "$dst"
done

asm is idempotent — re-running the same command updates the skill in place with no duplicate files. The agent loop is also safe to re-run: it only replaces IDD-managed agents and skips unmanaged same-name files. Target Claude Code explicitly with -p claude (or -p all for every supported tool).

Why a loop? A single asm install …:dist/skills --all would be nicer, but --all is currently ignored when the source includes a subpath (tracked in luongnv89/asm#251). The per-skill loop above exercises the working single-skill path. Once asm #251 lands we'll switch back to the one-liner. If you'd rather not loop, the from-source alternative installs all skills with a single command today.

After install, restart Claude Code so it picks up the new skill(s) and agents. The full list of skills lives under dist/skills/ and generated Claude Code agents live under dist/agents/ — substitute any skill name (issue-creator, issue-resolver, issue-triage, issue-pr-review, auto-pilot, issue-analysis, init-gitissue) into the command above.

From-source alternative (no asm required)

If you can't or don't want to install asm, clone the repo and run the bundled install script. One command, no manual tar/cp, idempotent on re-run:

git clone https://github.com/luongnv89/idd.git
cd idd
./scripts/install.sh                          # all standalone skills
# or: ./scripts/install.sh --skill issue-resolver

The script copies each dist/skills/<name>/ to ~/.claude/skills/<name>/ and each generated shared agent from dist/agents/*.md to ~/.claude/agents/. It replaces IDD-managed agents cleanly, removes stale managed agents on update, and skips unmanaged same-name agents unless you pass --force-agents (which backs them up before replacement). Use ./scripts/install.sh --target <dir> to target a different Claude root. Pure POSIX bash — no extra runtime needed.

You can also do the copy by hand if you prefer to see every file move:

mkdir -p ~/.claude/skills ~/.claude/agents
cp -r dist/skills/<name> ~/.claude/skills/
cp dist/agents/*.md ~/.claude/agents/

dist/skills/ and dist/agents/ are committed to the repository, so this path works on a fresh clone without running a build.

Plugin path (advanced — Claude Code only)

The plugin layout bundles every public skill under one ~/.claude/plugins/idd/ tree. It is the heavier option; pick it only if you want all skills installed atomically as a single Claude Code plugin.

Grab the idd-plugin-<tag>.tar.gz asset from the latest release and extract it into your Claude Code plugins directory:

mkdir -p ~/.claude/plugins/idd
# Download idd-plugin-<tag>.tar.gz from the release page (browser or `gh release download`)
tar -xzf idd-plugin-<tag>.tar.gz -C ~/.claude/plugins/idd

The tarball unpacks to .claude-plugin/plugin.json, agents/, skills/, shared/, and docs/ at its root. Restart Claude Code to load the new plugin.

Heads up — claude plugin install <tarball-url> is not supported. Claude Code's claude plugin install only accepts plugin@marketplace references and resolves them through configured marketplaces, not direct tarball URLs or paths. Running claude plugin install https://github.com/luongnv89/idd/releases/download/<tag>/idd-plugin-<tag>.tar.gz returns not found in any configured marketplace and does nothing. Use the manual tar -xzf extract above — that is the supported install path today. (Tracked in #79.)

If you cloned the repo, you can build the plugin tree locally and install it with the script: ./scripts/build.sh && ./scripts/install.sh --plugin. dist/plugin/ is generated only at release time and gitignored, so the build step is required.

Note: dist/skills/ and dist/agents/ are committed so the standalone paths work without running a build. dist/plugin/ is built fresh by ./scripts/build.sh and shipped as the release tarball above.

First issue in 30 seconds

Create a structured issue:

/issue-creator "Login fails on mobile when session cookie expires"

Resolve it:

/issue-resolver 42

Triage the backlog:

/issue-triage

Or go hands-free — triage, resolve, review, and merge everything:

/auto-pilot

Zero config required. Run /init-gitissue to customize.

Browse the authored source for each skill — these links point to src/ for reading; install copies come from dist/skills/<name>/ (see Install):

Skill Source
/issue-creator src/skills/issue-creator/
/issue-analysis src/skills/issue-analysis/
/issue-resolver src/skills/issue-resolver/
/issue-triage src/skills/issue-triage/
/auto-pilot src/skills/auto-pilot/
/issue-pr-review src/skills/issue-pr-review/
/init-gitissue src/skills/init-gitissue/
/idd-doctor (internal) src/internal-skills/idd-doctor/

What is IDD?

Issue-Driven Development — also Intention-Driven Development — treats GitHub issues as the atomic unit of all development work. Every change starts as a structured issue and ends as a PR linked to that issue.

The key idea: the gap between "someone describes a problem" and "someone ships a fix" is both a translation gap and an intention gap. IDD automates that translation — turning vague reports into structured work orders with acceptance criteria — while helping creators discover and articulate what they actually want through iterative refinement.

graph TD
    A["Problem described"] --> B["/issue-creator"]
    B --> C["Structured issue with acceptance criteria"]
    C --> D["/issue-triage"]
    D --> E["/issue-analysis N"]
    E --> F["/issue-resolver N"]
    F --> G["Fetch → Branch → Research → Plan → Execute → Verify → Ship"]
    G --> H["PR merges → Issue auto-closes"]
    D -.-> I["/auto-pilot"]
    I -.-> |"triage → resolve → review → merge loop"| H

    style A fill:#4CAF50,color:#fff
    style H fill:#2196F3,color:#fff
    style I fill:#FF9800,color:#fff
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IDD as Intention-Driven Development

IDD is also Intention-Driven Development — and that name captures something deeper about what it solves.

Expressing what you actually want is hard. Even experienced developers struggle to articulate a problem clearly enough for someone else — human or AI — to act on it. Vague descriptions lead to wrong assumptions, wasted effort, and solutions that miss the point.

/issue-creator changes this dynamic. When you describe a problem in plain language, it structures your input into a typed issue with reporter context and acceptance criteria — capturing intent only, never guessing affected files or implementation notes. But the real value isn't the output — it's the feedback loop:

  1. You describe the problem — loosely, incompletely, however it comes to mind
  2. The agent proposes a structured issue — classifying the type, filling in context, generating acceptance criteria
  3. You read back what it captured — and realize what you actually meant, what's missing, what's wrong
  4. You refine through conversation — correcting, adding detail, sharpening the intent until the issue says exactly what you want

This iterative process does something no template or form can do: it helps you discover your own intention. By the time the issue is finalized, it accurately represents what the creator wants — expressed in a way that both humans and AI agents can understand and execute.

This matters because understanding user requirements is one of the hardest steps in the software development lifecycle. Most bugs and missed features trace back to requirements that were never clearly stated. IDD makes that step explicit, collaborative, and repeatable — turning an informal complaint into a precise, agent-ready work order.

The structured issue becomes the single source of truth for what needs to happen. When /issue-resolver picks it up, there's no guesswork — the intention is already captured.

Why Good Issues and Commit Messages Matter

A well-defined issue and a well-written commit message are two halves of the same story. Together, they turn your git history into a searchable, navigable record of every decision your project has ever made.

The issue captures the why — what was broken, what was needed, what the user actually wanted. The commit captures the how — what was changed, which approach was chosen, and what trade-offs were made. The PR links them together — providing the full narrative from problem to solution.

When these artifacts are well-crafted:

  • Debugging becomes archaeology, not guesswork. git log --oneline tells you which issue motivated each change. git blame on any line links to the commit that changed it, which links to the PR, which links to the issue. You can trace any line of code back to the original problem report.
  • Onboarding accelerates. New team members read the git history and understand not just what the code does, but why it does it that way. The history teaches the project's evolution.
  • AI agents get better context. When /issue-analysis scans git history for related commits, structured commit messages with issue references surface relevant changes instantly. Vague commits like "fix bug" are invisible to this analysis.
  • Reverts and rollbacks are safe. When every commit is atomic and linked to an issue, you can revert a specific change with confidence — you know exactly what it did and why.
  • Changelogs write themselves. Conventional commit messages (feat:, fix:, refactor:) can be parsed automatically into release notes grouped by type.

gitissue enforces this discipline automatically. /issue-creator structures the issue. /issue-resolver creates branches, commits, and PRs that follow naming conventions — linking every artifact back to the issue that started it. The result: a development history that remains useful and valuable for the entire lifetime of the project.

IDD and other methodologies

IDD operates at a different layer than TDD, BDD, or spec-driven development. It structures the work before you write the code or tests. This makes it complementary, not competitive.

Aspect IDD TDD BDD Spec-Driven
Source of truth GitHub issue Test suite Feature specs Spec documents
Starts with Problem description Failing test User story Detailed spec
Agent-compatible Any agent Needs framework Needs framework Needs parser
Overhead Zero-config CLI Test setup Gherkin syntax Spec authoring
Best for Brownfield, mixed teams New code User-facing features Formal contracts

Use IDD with TDD: structure the issue first, then write tests during resolution. Use IDD with BDD: let acceptance criteria inform your Gherkin scenarios. They compose.


FAQ

Is it free? MIT licensed. No telemetry, no accounts, no cloud dependency.

Does it work without Claude Code? The skills are designed for Claude Code, but the structured issue format works with any AI agent or human developer. The issue is the interface.

Will it modify my existing issues? Only when you explicitly run /issue-creator N. A backup comment is posted before any changes. If the backup fails, it aborts.

How does it handle security issues? Issues labeled security, CVE, or vulnerability are automatically skipped during normalization. Use --force to override.

Can I use it with my existing tools? Yes. gitissue adds structure to issues. Your CI/CD, project boards, code review tools, and AI agents all keep working. Structured issues give them better input.

Does it work with private repos? Yes. It uses gh CLI authentication — whatever repos you can access via gh auth login will work.


Contributing

Contributions welcome. See CONTRIBUTING.md for guidelines.

MIT Licensed · View on GitHub


Commands Reference

/issue-creator -- Create and Normalize Issues

Invocation Mode Description
/issue-creator <text> Create Create a structured issue from text
/issue-creator <N> Normalize Structure existing issue #N with acceptance criteria
/issue-creator <N> --dry-run Preview Show normalization preview without applying
/issue-creator <N> --force Force Normalize even if issue has a security label
/issue-creator <multi-item text> Batch Extract and create multiple issues from one input

Create mode classifies the issue type (bug/feature/improvement), generates acceptance criteria, uploads screenshots to GitHub, and creates a structured issue.

Normalize mode restructures an existing issue: preserves original text in a Reporter Context blockquote, generates acceptance criteria, and posts a backup before editing.

Batch mode auto-detects multiple items (numbered lists, bullet points, planning documents) and creates them sequentially with a preview table and approval step.

/issue-analysis N -- Deep Issue Analysis

Analyzes issue #N in depth without making code changes:

[1/8] Fetch          — Load issue, classify type
[2/8] Extract        — Parse keywords, file refs, error messages
[3/8] Research       — Deep codebase scan (30 files, 3-level import tracing)
[4/8] History        — Git log: prior fix attempts, regressions, domain experts
[5/8] Cross-refs     — Related issues/PRs, duplicates, already resolved
[6/8] Analysis       — Root cause (bugs), architecture fit (features)
[7/8] Options        — 2-3 implementation approaches with pros/cons
[8/8] Report         — Terminal report + .gitissue/analysis-N.json

/issue-resolver N -- Resolve Issues

Resolves issue #N through a 6-step pipeline:

[0/5] Preflight    — Check issue status, verify not already resolved
[1/5] Research     — Scan codebase, trace dependencies
[2/5] Plan         — Propose 3 approaches, pick one
[3/5] Implement    — Write code + tests, atomic commits
[4/5] QA           — Code review + test + build + fix loop
[5/5] Deliver      — Push branch, create PR with "Closes #N"

/issue-triage -- Triage the Backlog

Dependency detection via codebase scanning, topological sort for execution order, parallelizable issue identification, stale issue detection (>14 days), already-fixed detection (scans commit history and merged PRs with confidence levels), priority suggestions.

/auto-pilot -- Automated Backlog Processing

Fully automated loop: triage open issues, pick the highest-priority task, resolve it end-to-end, review the PR with up to three review-fix cycles (script pre-pass first, LLM cycles only for issues the pre-pass can't resolve), merge, and repeat. Supports explicit issue lists to process specific issues in user-defined order. Stop conditions prevent runaway execution.

/auto-pilot                          # triage and resolve all open issues
/auto-pilot --issues 5,12,3          # resolve these issues in this order
/auto-pilot --limit 5                # cap to 5 iterations
/auto-pilot --dry-run                # show plan without resolving

Merge modes (configured under autopilot.mode in .gitissue.yml):

Mode Clean PR Partial PR (review issues remain)
conservative (default) merge leave open
balanced merge leave open + create follow-up issue
aggressive merge merge + create follow-up issue (requires merge_partial: true)

/idd-doctor -- Read-Only Health Check

Quick read-only diagnostic that verifies the IDD invariants of the current repo. Checks four things and reports per-check pass/fail with file/line evidence:

  1. /issue-creator keeps its intent-only contract (no affected_files / technical_notes / architecture_constraints in templates or output)
  2. Every gh invocation in skills uses --json with explicit field selection
  3. Each skill has a references/error-messages.md using the three-part format
  4. Repo merge strategy is set to squash so PR bodies land in git log (durable analysis-artifact rule from #35)

Read-only by design — never modifies files, branches, or remote state.

/idd-doctor                          # run all checks

/issue-pr-review -- PR Review Pipeline

Reviews a PR end-to-end: script pre-pass (lint/format/test auto-fix, zero LLM cost), code review with confidence-based filtering classified as fix (critical/high) vs note (medium), per-criterion acceptance-criteria verification, traceability checks (Closes link, Decision Record, AC Verification table), runs tests and build, checks CI status, fixes only fix issues, and repeats until clean. Soft-pass when zero fix issues remain. Supports auto-merge in auto-pilot mode.

/issue-pr-review 87              # review PR #87
/issue-pr-review                 # auto-detect PR for current branch

Pipeline:

[1/7] PR Info      ✓ PR #87: fix(auth): resolve redirect (#42)
[2/7] Pre-pass     ✓ lint clean, format clean, 17 tests passed
[3/7] Review       ● analyzing changes...
[4/7] Test         ✓ 17 tests passed, build ok
[5/7] CI Status    ✓ all checks passed
[6/7] Fix          ○ no issues to fix
[7/7] Report       ✓ PR is clean — ready to merge

Max 3 review-fix cycles with stagnation detection.

/init-gitissue -- Generate Config

Scans your repository and generates .gitissue.yml with sensible defaults: detects language, framework, test runner, existing templates, and adjusts timeouts based on repo size.

Configuration

gitissue works with zero configuration. All settings have sensible defaults.

To customize, create .gitissue.yml in your repo root (or run /init-gitissue):

platform: github                # github | gitlab (future)

issue:
  auto_normalize: true          # auto-normalize in /issue-resolver
  template: default             # default | path to custom template dir
  labels_auto_suggest: true     # auto-suggest labels based on content
  normalize_comment: true       # add comment when normalizing

resolve:
  approval_gate: auto           # auto | comment-and-wait
  branch_prefix: "auto"         # type-based: fix/42-description, feat/15-description
  auto_test: true               # run tests before creating PR
  test_timeout: 300             # abort verify phase after N seconds
  pr_auto_link: true            # include "Closes #N" in PR body
  max_commits: 10               # warn if resolve produces >N commits

triage:
  stale_threshold_days: 14      # flag issues with no activity
  auto_priority: true           # suggest priorities based on type + age + deps
  include_closed: false         # include recently closed issues in triage
  scan_timeout_per_issue: 30    # max seconds to scan codebase per issue

autopilot:
  mode: conservative            # conservative | balanced | aggressive
  merge_partial: false          # only meaningful when mode: aggressive
  max_iterations: 10
  review_cycles: 3              # max LLM review-fix cycles per PR
  skip_labels: ["wontfix", "blocked", "do-not-merge"]
  critical_labels: ["critical", "priority:critical"]

review:
  require_traceability_check: true        # block soft-pass if Closes #N is missing (covers Decision Record presence)
  require_acceptance_criteria_check: true # block soft-pass if AC Verification fails

Full schema: docs/config-schema.md

Issue Templates

Three default templates:

  • Bug -- current vs expected behavior, reproduction context
  • Feature -- user story, acceptance criteria
  • Improvement -- current state, proposed change

Each normalized issue includes:

  • <!-- gitissue:normalized v1 --> marker (invisible in GitHub UI)
  • Reporter's original text in a > Reporter Context blockquote
  • Acceptance criteria derived from the reporter's intent

/issue-creator is intent-only — it never scans the codebase, never lists "affected files", and never proposes implementation notes. Codebase analysis is performed at execution time by /issue-resolver, /issue-triage, and /issue-analysis, always against current code.

Project Structure
src/
├── shared/
│   └── agents/                    # Shared agent definitions (used by multiple skills)
│       ├── codebase-researcher.md # Deep codebase scan + solution research
│       ├── synthesizer.md         # Analysis + implementation options
│       ├── implementer.md         # Code + tests implementation
│       ├── code-reviewer.md       # Confidence-based code review
│       ├── duplicate-detector.md  # Issue dedup scoring
│       └── issue-relationship-scanner.md  # File deps + already-fixed detection
│
├── skills/
│   ├── auto-pilot/         # /auto-pilot
│   ├── issue-analysis/     # /issue-analysis N
│   ├── issue-creator/      # /issue-creator (+ templates/)
│   ├── issue-resolver/     # /issue-resolver N
│   ├── issue-triage/       # /issue-triage
│   ├── issue-pr-review/    # /issue-pr-review — review, test, CI, fix, merge
│   └── init-gitissue/      # /init-gitissue
│       (each skill has SKILL.md, README.md, references/)
│
├── internal-skills/
│   └── idd-doctor/         # /idd-doctor — read-only repo health check
│
├── deprecated-skills/
│   └── issue-pr-review-fix-loop/  # Retained deprecated source only; not distributed
│
└── (no docs/ — see below)

docs/                             # Single docs tree (issue #81)
├── config-schema.md              # ↓ Runtime docs — bundled into each skill
├── idd-methodology.md            #   at build time via transitive-closure scan
├── naming-conventions.md         #   on bare `docs/X.md` tokens
├── sync-conventions.md
├── github-projects-sync.md       # ↑
├── ARCHITECTURE.md               # ↓ Project docs — humans only, not bundled
├── CHANGELOG.md
├── DEVELOPMENT.md                # ↑
├── decisions/                    # Decision records
├── experiments/                  # Experimental design notes
└── release-notes/                # Per-release smoke-test reports
IDD Methodology

Full documentation: docs/idd-methodology.md

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