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feat(llma): add ai-failures and online-evals skills - #67707

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feat(llma): add ai-failures and online-evals skills#67707
carlos-marchal-ph merged 4 commits into
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@carlos-marchal-ph

@carlos-marchal-ph carlos-marchal-ph commented Jul 2, 2026

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Adds two new skills, bundled in a single commit because they have a dependency and I tested them together:

  • exploring-ai-failures explores the failure space of AI traces and sample and understands what goes wrong with existing traces, creating a taxonomy of failures (based on current best practices)
  • creating-online-evaluations sets up new evals in an evidence based manner, that fit the user's domain. Leverages the exploring-ai-failures skill when the user is not clear on what to focus the evaluation on

It additionally bundles a fix that prevented the evaluation creation tools from working by being too strict with UUID input validation.

Sample run of the skill, which I used to create this eval and this other eval:

 ▐▛███▜▌   Claude Code v2.1.198
▝▜█████▛▘  Opus 4.8 · Claude Max
  ▘▘ ▝▝    ~/Code/posthog

 ▎ Fable 5 is back.
 ▎ Until July 7, you can use up to 50% of your plan's weekly usage limit on Fable 5. If you hit your limit, you can continue on Fable 5 with usage credits. Fable 5 draws down usage faster than Opus 4.8. Learn more
   +1 more · /status

❯ Help me create one or two evals for my LLMs                                   

⏺ I'll invoke the skill that's designed exactly for this. It grounds evals in a
  real failure mode, so it'll also point me at finding failures first.

⏺ Skill(creating-online-evaluations)
  ⎿  Successfully loaded skill

⏺ The skill is grounded in a real, observed failure mode — not a generic metric.
  Since you haven't named a specific failure yet, I want to anchor on that
  before writing anything. A couple of quick decisions:

⏺ User answered Claude's questions:
  ⎿  · Which AI product/use case should these evals cover, and where's its data?
     → This project (PostHog prod, id 2)
     · How should we pick the failure mode to evaluate? → Explore failures first
     (Recommended)

⏺ Good — this project (PostHog prod), and we'll explore failures first. Let me
  pull in the exploration skill.

⏺ Skill(exploring-ai-failures)
  ⎿  Successfully loaded skill

⏺ Let me load the PostHog MCP tools so I can query the production LLM traffic.

⏺ Let me read the finding-traces reference and inspect the key tools in
  parallel.

  Read 1 file, called posthog-prod 2 times (ctrl+o to expand)
  ⎿  Loaded .claude/rules/semantic-line-breaks.md

⏺ Now let me discover the LLM trace taxonomy in this project — volume, and how
  traffic splits.

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ Massive volume — ~[redacted] generations/week. The named traffic is clearly Max AI
  (PostHog's product assistant): MaxChatAnthropic (~[redacted]/wk) and MaxChatOpenAI
  (~[redacted]/wk). There's also a huge unnamed bucket ([redacted]). Let me quickly see what
  that unnamed bucket is before scoping.

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ The unnamed bucket is a grab-bag across ~[redacted] models (gpt-4.1-nano at [redacted],
  gemini, various claudes) — many different internal features mixed together,
  not one use case. Max AI chat is the coherent, flagship target: mostly
  claude-sonnet-4-6 (~[redacted]) and gpt-4.1 (~[redacted]), with a very low error rate
  (~[redacted]) — meaning the failures that matter here are silent.

  Let me scope to Max and pull traces to read: a random sample plus the error
  ones and any with negative user feedback.

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ The random-sample list came back huge (full conversation states inline). I'll
  have a subagent read that file so it stays out of my context. Meanwhile, [redacted]
  feedback events in 30 days — all with text — is a high-yield failure signal.
  Let me pull those and kick off the trace reading in parallel.

  Called posthog-prod (ctrl+o to expand)

⏺ Agent(Read Max AI random trace sample)
  ⎿  Done (10 tool uses · 55.6k tokens · 2m 21s)
  (ctrl+o to expand)

⏺ Two key findings. First, the traces-list only returned span telemetry — the
  actual prompt/response content lives on posthog.ai_events and needs
  query-llm-trace per trace. Second, and more valuable: the [redacted] feedback texts 
  are a strong, real failure signal, and clear patterns already jump out.
  Reading them as a reviewer:

  - A large "failed to generate an answer" / not working / crashing cluster
  around 2026-06-09 — looks like an infra incident (loud, not a good eval
  target).
  - Recurring "0 days" complaints from many separate users (8b0079b0, 7b2c89ad,
  f36f4b9c, ad397c2a, 6921109d…) — Max reporting a 0-day / empty window.
  - Wrong property selected — "Used wrong property", "$entry_utm_source instead
  of $utm_source".
  - Wrong tools / nonexistent slash commands — "calling the wrong tools and
  passing wrong arguments", proposes /ticket that doesn't exist.
  - Hallucinated / false claims about data — "completely false claim… not
  supported by the session it references", "it's false, I can see 4 matching
  people".
  - Specific formatting bug — "Always creates charts with %% as the postfix".

  The skill is firm that I confirm by reading real traces, not trust the labels.
  Let me check query-llm-trace's shape, then have subagents deep-read the
  traces behind the most checkable modes.

  Called posthog-prod (ctrl+o to expand)
⏺ Now I'll deep-read the traces behind the most promising, checkable failure
  modes — in parallel, so the heavy trace content stays out of my context and
  each subagent returns only coded notes with verbatim quotes and a checkability
  verdict.

⏺ 4 agents finished (ctrl+o to expand)
   ├ Read Max "0 days" traces · 10 tool uses · 65.7k tokens
   │ ⎿  Done
   ├ Read Max wrong-property traces
   ├ Read Max wrong-tool traces · 10 tool uses · 65.8k tokens
   │ ⎿  Done
   └ Read Max hallucination traces · 10 tool uses · 66.0k tokens
     ⎿  Done

⏺ Agent "Read Max wrong-property traces" finished · 5m 26s

⏺ I've now deep-read the traces behind each candidate mode. The reads corrected 
  several of my initial labels — which is exactly why the skill insists on
  reading. Here's the confirmed, ranked picture (setting aside the loud infra
  incident around 2026‑06‑09 — a wave of "failed to generate an answer" — and
  the /ticket handler SynchronousOnlyOperation crash; those are bugs to file, 
  not evals).

  Confirmed failure modes (from reading real traces)

  1. Ungrounded / hallucinated claims — highest pain, most recurring. Max takes
  clean tool output and asserts a confident conclusion the data doesn't support.
  Confirmed across every trace I read for it:
  - cf8447ee — invented a causal mechanism: "The app is programmatically 
  relaunching the language modal every time it comes to the foreground… 
  preference is not being persisted" — the session events show the locale
  persisted throughout.
  - 23495fe0 — over-attribution: "Found it. Your person record has the email 
  [redacted-email]" — one of 10 unrelated rows from a query with no
  identity filter.
  - 0989bf06 — quoted a funnel "6→6→3 (50%)" that no query in the trace ever 
  ran.
  - bd2294ae — fabricated that gpt‑5.4 "maps to" gpt‑4.1, then advised on the
  wrong model.

  The grounding data (tool results) sits in the same generation's input, so an
  llm_judge can actually verify it. Not hog-checkable.

  2. Generated query doesn't match the user's intent — the "0 days" and "wrong 
  property" complaints collapse into this. The reads killed my mechanical
  hypotheses (no zero-length windows exist; every property Max used does exist
  in the taxonomy). The real defect is subtler:
  - 73e8e29a — "users in Brentwood area" → built $pageview DAU instead of a
  person count → returned 0.
  - fe93479d — session-scoped question got an event-scoped breakdown
  ($referring_domain, type event) instead of $entry_utm_source, type session.
  - 8b0079b0 / f36f4b9c — dropped the user's established time window / omitted
  the requested time series ("0 days" = "no time dimension").

  Needs an llm_judge (a hog proxy misfires on legitimately all-time questions).

  3. Invented capabilities / wrong app URLs — partly deterministic but brittle.
  b82aee76: wrong routes (/activity/live-events vs /activity/live), and handed
  the user JavaScript for a Hog editor. The "invented slash command" angle
  turned out weaker — /ticket is actually a real command that crashed, not a
  hallucination. A hog regex over emitted URL paths is possible but needs a
  hand-maintained known-bad list.

  ---
  Per the skill, evals should be one mode each, and it's your call which to turn
  into evals. My recommendation: build #1 (groundedness) as an llm_judge — it's
  the biggest, most painful, and cleanly judgeable — and pair it with one more.
  Which would you like?

⏺ User answered Claude's questions:
  ⎿  · Which failure mode(s) should I turn into online evals? (one eval each) → 
     Groundedness (llm_judge) — Recommended, Query matches intent (llm_judge)

⏺ Both are llm_judge, so I need a usable provider key first. Let me read the
  exact payload reference and run the gate checks (provider keys + valid judge
  models) in parallel.

  Read 1 file, called posthog-prod 2 times (ctrl+o to expand)

⏺ The key-list tool is named differently here. The config tools are
  llma-evaluation-config-get (active/available keys) and
  llma-evaluation-judge-models. Let me inspect and call both.

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ Now the gate calls:

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ There's an active OpenAI key in ok state
  (019b217f-bcc7-0000-664b-e229e01ded70, "Production key", created by you). Note
  the trial eval quota is exhausted (trial_evals_remaining: 0), but that's fine
  — with a real active key, evals run on your own key, not the trial. Valid
  judge models include gpt-5-mini (good balance for judging).

  Before creating, let me confirm the scope volume so I can pick a sane sampling
  rate — Max chat is high-traffic and each llm_judge call bills against that
  key.

  Called posthog-prod (ctrl+o to expand)

⏺ ~[redacted] Max chat generations/day — far too many to judge at 100%. I'll create
  both disabled with a conservative 1% sample (~[redacted] judged/day each), scoped to
  Max chat generations excluding errors, and we'll confirm the rate before
  enabling.

  Creating the two evals now:

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ The key's UUID (019b217f-bcc7-0000-...) has a 0000 version group that the
  tool's strict UUID validator rejects. That's fine — the active team key is
  that same "Production key", so omitting provider_key_id falls back to exactly
  it. Recreating without the pin:

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ Both created and disabled (status: paused):
  - Groundedness — 019f2226-16bd-0000-c1fb-4e008f33fb1e
  - Query matches intent — 019f2226-2a45-0000-a198-22aeca92b303

  Before proposing we enable, let me spot-run each judge against a known-failing
  generation to confirm it fires and returns a sensible verdict. Let me check
  how llma-evaluation-run takes its target.

  Called posthog-prod (ctrl+o to expand)

⏺ llma-evaluation-run needs a specific generation event UUID + timestamp. Let me
  grab the generation events for two known-failing traces — cf8447ee
  (hallucinated causal claim, for groundedness) and 73e8e29a (Brentwood
  DAU-vs-person-count, for query-intent) — so I can target the spot-runs. This
  also validates a real scoping concern: whether the query Max generates
  actually lives on the MaxChat generation my conditions target.

  Called posthog-prod (ctrl+o to expand)

⏺ out_len is null because the heavy output lives on ai_events, not
  events.properties — expected; the eval engine reads it from there. Each trace
  has many MaxChat generations (the tool-calling loop); the last is the final
  answer. Let me spot-run both judges against the relevant generation and then
  read the $ai_evaluation results. This is the real end-to-end check — including
  a genuine open question: whether the query-intent judge can see the generated
  query from the orchestrator MaxChat turn (Max may generate queries in an
  unlabeled sub-agent turn my scope doesn't catch).

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ Both spot-run workflows started (async). Let me give them a moment and query
  the resulting $ai_evaluation events.

  Ran 1 shell command (ctrl+o to expand)

⏺ While that runs, let me confirm the $ai_evaluation result event's property
  names so I query the right fields.

  Called posthog-prod (ctrl+o to expand)
⏺ The result event carries $ai_evaluation_result, $ai_evaluation_applicable (the
  N/A signal), and $ai_evaluation_reasoning. Let me query the two spot-run
  verdicts.

  Called posthog-prod (ctrl+o to expand)

⏺ Both judges validated correctly against the known-bad generations:

  Eval: Groundedness
  Target: cf8447ee (hallucinated modal cause)
  Verdict: Fail (applicable)
  Judge's reasoning: "unsupported causal claims about why the modal reopens, why
   
    RTL fails… none directly established by the provided data"
  ────────────────────────────────────────
  Eval: Query matches intent
  Target: 73e8e29a (Brentwood)
  Verdict: Fail (applicable)
  Judge's reasoning: "defaulted to 'last 30 days' … $pageview with DAU … 
  measures
     active pageview users, not the total distinct persons in  Brentwood… the 
    measured entity diverges"

  Both caught the real failure with accurate reasoning and correctly marked it
  applicable (not N/A). Importantly, this resolves my scoping worry: the
  query-intent judge could see and assess the generated query from the MaxChat
  generation, so scoping to MaxChatAnthropic/MaxChatOpenAI works — I don't need
  to chase the unlabeled sub-agent turns.

  Where things stand:
  - Both evals exist, disabled, validated, judge gpt-5-mini, scoped to 1% of 
  non-error Max chat generations (~[redacted]/day → ~[redacted] judged/day each).
  - One thing to flag: you already have an eval "PostHog AI - product-fact 
  answers: grounding triage" running. Mine is broader (every Max claim vs. its
  in-context tool results, not just product-fact answers) — worth deciding later
  whether they're redundant.

  I've deliberately not enabled them — that's the last step, it fires on live
  prod and bills your key, and you're mid-fixing the keys. How do you want to
  proceed?

⏺ User answered Claude's questions:
  ⎿  · How should I handle enabling the two evals? → Enable both now at 1%

⏺ Enabling both now at 1%.

  Called posthog-prod 2 times (ctrl+o to expand)

⏺ Both are now live (status: active), pinned to your Production key
  (019b217f-…), judge model gpt-5-mini.

  Done — two online evals live on Max AI

  Both scoped to a 1% sample of non-error MaxChatAnthropic/MaxChatOpenAI
  generations (~[redacted] judged/day each), allows_na: true:

  - Max answer is grounded in tool results — eval — catches the
  hallucination/unsupported-claim class (the top failure mode from your
  feedback).
  - Max query matches the user's intent — eval — catches the "0 days" /
  wrong-property / DAU-vs-person-count class.

  They'll now score new matching generations going forward, emitting
  $ai_evaluation events ($ai_evaluation_result = pass/fail, plus reasoning).

  What to keep an eye on

  - First 24h: confirm they're firing and not erroring — a quick check is the
  pass/fail/N/A split. If almost everything is N/A, the scope is catching too
  many non-applicable turns and we should tighten conditions.
  - Rollout: 1% was a conservative exploration rate. Once you see the fail rate,
  dial rollout up (more signal) or down (less cost).
  - Overlap: decide whether my groundedness eval supersedes the existing
  "product-fact answers: grounding triage" one, or if they serve different
  purposes.
  - Pair with fixes: these came from real, unaddressed failures — as the
  underlying prompt/tooling improves, the pass rate rising is your confirmation
  the fix landed.

  Not evals — bugs to file separately

  The exploration also surfaced two loud issues that are bugs, not eval targets:
  the 2026‑06‑09 "failed to generate an answer" wave, and the /ticket handler 
  crashing with SynchronousOnlyOperation (a real command, async-context bug).

  Want me to draft the query for a quick daily pass/fail dashboard, or leave it
  here?

✻ Worked for 3m 31s

※ recap: Goal: create online evals for Max AI from real failures. Both are built, validated, and live at 1% sampling: a groundedness judge and a query-intent judge. Next: watch the first 24h of results to confirm they fire and check the fail/N/A split. (disable recaps in /config)

How did you test this code?

Automated tests I actually ran:

  • Added unit tests for stripUuidFormat in schema-transforms.test.ts, including the nullable array-form type: ["string","null"] case that this fix targets. Ran vitest — all pass.
  • Regenerated schemas with hogli build:openapi-schema + hogli build:openapi-mcp and confirmed provider_key_id now generates as .string(), with the diff limited to .uuid().string() on nullable fields.
  • MCP typecheck (tsgo --noEmit) passes; oxlint clean on the changed source.

The skills themselves are markdown plus a Jinja reference template, so there's no runtime path to exercise beyond authoring.

Automatic notifications

  • Publish to changelog?
  • Alert Sales and Marketing teams?

Docs update

Skills only, no posthog.com docs change.

🤖 Agent context

Autonomy: Human-driven (agent-assisted)

I drove this; Claude Code (Opus 4.8) did the code. The skills were authored first (this branch's original wip). The MCP fix came out of debugging why pinning a provider key failed during a spot-run of the eval skill.

Claude root-caused the rejection to stripUuidFormat's exact-string type check missing the nullable array form, rather than to the serializer. It chose to relocate the transform into the shared schema-transforms.mjs lib (next to stripEnumMinLength) instead of patching it inline, so it could be unit-tested. Skills invoked: /writing-tests (gate for the new unit test).

@carlos-marchal-ph
carlos-marchal-ph requested a review from a team July 2, 2026 10:16
@carlos-marchal-ph carlos-marchal-ph self-assigned this Jul 2, 2026
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Reviews (1): Last reviewed commit: "feat: add failures and evals skills" | Re-trigger Greptile

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Size Change: 0 B

Total Size: 70.2 MB

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@tests-posthog

tests-posthog Bot commented Jul 2, 2026

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Query snapshots: Backend query snapshots updated

Changes: 16 snapshots (16 modified, 0 added, 0 deleted)

What this means:

  • Query snapshots have been automatically updated to match current output
  • These changes reflect modifications to database queries or schema

Next steps:

  • Review the query changes to ensure they're intentional
  • If unexpected, investigate what caused the query to change

Review snapshot changes →


For each trace, note in plain language what went wrong — and jot down the trace's earliest-event timestamp
alongside the note (it's right there in the trace you just read, and in `query-llm-traces-list`'s
`first_timestamp`). That timestamp and the trace ID is all you need to build a resolvable deep link in Step 4,

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Suggested change
`first_timestamp`). That timestamp and the trace ID is all you need to build a resolvable deep link in Step 4,
`createdAt`). That timestamp and the trace ID is all you need to build a resolvable deep link in Step 4,

"name": "Output is valid JSON",
"description": "Fails when the assistant message can't be parsed as JSON",
"evaluation_type": "hog",
"evaluation_config": { "source": "try { jsonParseStr(event.properties.$ai_output_choices[1].content); return true; } catch { return false; }" },

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Doesn't the Hog function get access to a normalized output? Doing event.properties.$ai_output_choices[1].content seems wrong.

Generated-By: PostHog Code
Task-Id: c43291cb-8f62-4c94-82f2-ed8cbc999b97
@carlos-marchal-ph
carlos-marchal-ph merged commit b1f79cd into master Jul 3, 2026
178 checks passed
@carlos-marchal-ph
carlos-marchal-ph deleted the posthog-code/aiobs-creating-online-evals-skill branch July 3, 2026 08:13
@deployment-status-posthog

deployment-status-posthog Bot commented Jul 3, 2026

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Deploy status

Environment Status Deployed At Workflow
dev ✅ Deployed 2026-07-03 08:36 UTC Run
prod-us ✅ Deployed 2026-07-03 08:48 UTC Run
prod-eu ✅ Deployed 2026-07-03 08:50 UTC Run

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