David Blass 5518890b18 learnings: TOC + section taxonomy + 100k cap, hygiene rules, tool-quirk descriptions (#717)
* audit learnings: reshape reflection prompt + bake tool quirks into descriptions (#619)

Cross-repo audit of the 48 repos with non-null learnings turned up two
recurring failure modes:

1. ~25-30% of bullets across the most-active repos are pullfrog-tool
   quirks ("shell timeout is in milliseconds", "git args must be a JSON
   array", "create_pull_request_review drops out-of-hunk comments",
   "push_branch may report timeout when push succeeded", "checkout_pr
   shallow.lock retries", "commit_id needs full 40-char SHA"). These are
   universal across repos and should live in tool descriptions, not be
   rediscovered and stored 48 times. Tool descriptions now surface them.

2. Bullets are routinely 200-1000 chars (paragraph-length), and 12 of 48
   repos are at the 10k cap. The reflection prompt now: caps bullets at
   ~240 chars (one specific fact), bans PR/review/commit/date-anchored
   facts that decay within weeks, bans tool-quirk learnings, and tells
   the agent that cap pressure means compress+prune existing bullets,
   not skip new findings.

Co-authored-by: Cursor <cursoragent@cursor.com>

* learnings: add server-generated TOC, fixed section taxonomy, raise cap to 100k (#707)

Cap goes 10k → 100k. Reads stay bounded because the seeded file now
opens with a server-generated table of contents listing every `## `
section's line range — agents read the TOC, then `read_file offset/limit`
just the sections relevant to the current task instead of slurping the
whole file.

## Section taxonomy (fixed)

`## Build & test`, `## CI`, `## Conventions`, `## Architecture`,
`## Gotchas`. Free-form `### ` sub-headings inside a section are fine.
Pre-taxonomy free-text rows get wrapped in a `## Legacy` carve-out on
first seed so they remain visible while the agent gradually re-curates
them during reflection turns.

## Storage shape unchanged

`Repo.learnings` still holds raw markdown (no schema migration). The TOC
is a pure read-side affordance: prepended at seed time, stripped from
the agent-edited file before persist. Markers
`<!-- pullfrog-learnings-toc:* -->` delimit the strip region. Agent
edits inside the markers are discarded.

## Round-trip semantics

`seedLearningsFile` now returns `{ path, canonicalSeed }` where
`canonicalSeed` is the post-TOC body — same shape `readLearningsFile`
returns at end-of-run, so `persistLearnings` byte-compares them
directly to skip the no-op PATCH. Empty-repo first runs end up with the
section scaffold both as seed and as read-back, so untouched runs still
short-circuit cleanly.

## Reflection prompt

Adds explicit section-placement guidance (place each new bullet under
the most relevant `## `; do NOT add new top-level headings; do NOT
edit anything between the TOC markers). Carries forward the bullet
hygiene from the previous commit: ≤240 chars per bullet, no
pullfrog-tool quirks (those belong in tool descriptions), no
PR/review/commit/date references. The "near cap" framing is replaced
with "compress and prune within a section when it grows noisy" since
the cap pressure that drove cramming is gone.

Co-authored-by: Cursor <cursoragent@cursor.com>

* anneal round 1: line-anchored taxonomy detect, partial-merge, line-boundary truncation, scaffold-empty UI

Multi-lens review of the TOC + taxonomy diff surfaced a cluster of
correctness and operational bugs. Fixes:

- `hasAnyTaxonomyHeading` used `String.includes("## X")` which
  false-positives on `### X` (the `## ` substring sits inside `### `),
  prose containing `## CI`, fenced code documenting markdown, etc.
  Replaced with a line-anchored predicate that reuses `parseHeadings`
  so detection and TOC construction stay consistent.

- The "any heading present → pass through verbatim" rule meant a body
  with one taxonomy heading would seed without the other four. Worse,
  requiring all five would flip a body back into Legacy when the agent
  legitimately pruned a section to empty. New `partial` kind: keep
  existing content in place, append missing sections in canonical order
  so the agent always has the full scaffold without losing pruning
  intent.

- `stripLearningsToc` collapsed `\n{3,}` globally; `canonicalSeed`
  doesn't, so an untouched body with intentional triple-newline spacing
  would compare unequal and burn a spurious LearningsRevision row each
  run. Drop the global collapse — only the leading newlines that the
  strip itself introduces are normalized.

- 100k truncation via `slice(0, 100_000)` could cut mid-line, breaking
  `parseHeadings` (whole-line `^## `) on the next seed and flipping a
  cut body back into Legacy. New `truncateAtLineBoundary` cuts at the
  last newline before the cap.

- `LearningsSection.tsx` rendered a scaffold-only body as "has
  learnings" instead of the empty placeholder. Added a
  `hasOnlyEmptyScaffold` guard so the console behaves the same as
  pre-PR for the empty case.

- Seed log line distinguishes `kind=structured/partial/legacy-wrapped/
  empty` instead of `existing=yes/no`, so operators can spot legacy
  migration activity in logs.

- New tests cover: substring false-positive (`### Build & test`,
  in-prose mentions), partial-taxonomy merge (no Legacy wrap),
  full-taxonomy structured pass-through, last-newline truncation,
  triple-newline preservation.

Deferred (documented in PR body): deploy-ordering footgun (action
before API), rollback for rows >10k, Gemini sanitizer dropping
`description` on `anyOf` branches, reflection-on-failed-runs.

Co-authored-by: Cursor <cursoragent@cursor.com>

* anneal r2: hard-truncate fallback when line boundary discards >4k

Round-2 review caught a regression in `truncateAtLineBoundary`: when the
only newline within the first 100k chars sits near the start (e.g. one
heading + 100k+ char single line — pathological pasted log dumps), the
line-boundary cut discards almost all of the body. losing one partial
line is preferable to losing kilobytes; threshold the fallback at 4k.

Co-authored-by: Cursor <cursoragent@cursor.com>

* move TOC out of file: prompt-side rendering, server-parsed headings

drops the in-file TOC + fixed taxonomy in favor of:
- file on disk = verbatim Repo.learnings (no markers, no scaffold)
- server parses headings (mdast-util-from-markdown) at run-context time
  and returns them as RepoSettings.learningsHeadings
- action renders heading TOC into the LEARNINGS prompt section as
  parenthesized line ranges like `Build & test (L1-L42)` with hierarchy
  via 2-space indent off the shallowest depth
- reflection prompt teaches agent-curated structure with a soft 300-line
  per-section cap and explicit guidance to restructure flat legacy lists

cuts 8 helpers (ensureSections, stripLearningsToc, assembleFile,
buildTocBlock, parseHeadings, buildSectionScaffold, hasAnyTaxonomyHeading,
LEARNINGS_SECTIONS) and the canonicalSeed round-trip dance.

action seedLearningsFile is now { path } only; main.ts byte-compares the
trimmed read-back against (current ?? "").trim() to gate the persist
PATCH. truncateAtLineBoundary kept for safety.

new tests:
- test/learningsToc.test.ts (11 parser cases incl. fenced-code, blockquote,
  arbitrary h1-h6 nesting, startLine-points-at-heading invariant)
- action/utils/learningsTocRender.test.ts (7 renderer cases)

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Colin McDonnell <colinmcd94@gmail.com>
2026-05-13 20:14:26 +00:00
2026-01-16 08:00:16 +00:00
2026-03-12 05:22:51 +00:00
2025-08-27 16:53:48 -07:00
2026-01-19 08:41:56 +00:00
2026-05-13 18:23:45 +00:00
2026-03-12 05:22:51 +00:00

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Pullfrog is a GitHub bot that brings the full power of your favorite coding agents into GitHub. It's open source and powered by GitHub Actions.

  • Tag @pullfrog — Tag @pullfrog in a comment anywhere in your repo. It will pull in any relevant context using the action's internal MCP server and perform the appropriate task.
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Standalone Usage

You can also use pullfrog/pullfrog as a step in your own workflows. The action exposes a result output that can be consumed by subsequent steps.

Example: Auto-generate release notes on new tags

name: Release
on:
  push:
    tags: ['v*']

permissions:
  contents: write

jobs:
  release:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - name: Generate release notes
        id: notes
        uses: pullfrog/pullfrog@v0
        with:
          prompt: |
            Generate release notes for ${{ github.ref_name }}.
            Compare commits between this tag and the previous tag.
            Format as markdown: summary paragraph, then ### Features, ### Fixes, ### Breaking Changes sections.
            Omit empty sections. Be concise.
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

      # write to file to avoid shell escaping issues with special characters
      - name: Create GitHub release
        run: |
          notesfile="$RUNNER_TEMP/release-notes-$GITHUB_RUN_ID.md"
          printf '%s' "$NOTES" > "$notesfile"
          gh release create ${{ github.ref_name }} --title "${{ github.ref_name }}" --notes-file "$notesfile"
        env:
          GH_TOKEN: ${{ github.token }}
          NOTES: ${{ steps.notes.outputs.result }}

Example: Structured Output with Zod Schema

You can force the agent to return structured JSON output by providing a JSON schema. This allows you to reliably parse and use the agent's response in subsequent workflow steps.

You can define your JSON schema directly or uou can use any validation library that converts to JSON Schema. Here's an example using Zod:

name: Release Check
on:
  pull_request:
    types: [closed]

jobs:
  check-release:
    if: github.event.pull_request.merged == true
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install dependencies
        run: npm install --no-save --no-package-lock zod @actions/core

      - name: Generate Schema
        id: schema
        run: |
          node -e '
            import { z } from "zod";
            import { setOutput } from "@actions/core";
            const schema = z.object({
              version: z.string().describe("Semantic version number (e.g. 1.0.0)"),
              isBreaking: z.boolean().describe("Whether this release contains breaking changes"),
              changelog: z.array(z.string()).describe("List of changes in this release"),
            });
            setOutput("schema", JSON.stringify(z.toJSONSchema(schema)));
          '

      - name: Analyze PR
        id: analysis
        uses: pullfrog/pullfrog@v0
        with:
          prompt: |
            Analyze this PR and determine semantic versioning impact.
            Return a JSON object matching the provided schema.
          output_schema: ${{ steps.schema.outputs.schema }}
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

      - name: Process Result
        run: |
          # Parse the JSON result using fromJSON()
          echo "Version: ${{ fromJSON(steps.analysis.outputs.result).version }}"
          echo "Breaking: ${{ fromJSON(steps.analysis.outputs.result).isBreaking }}"
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