Colin McDonnell d6de1c369a learnings: edit-in-place tmpfile (drop update_learnings tool) (#635)
* learnings: edit-in-place tmpfile (drop update_learnings tool)

learnings now follow the PR-summary file pattern: server seeds
`pullfrog-learnings.md` from `Repo.learnings` at startup, agent reads
it as part of context, may edit in place during the post-run reflection
turn, server reads back at end-of-run and PATCHes if changed.

motivation: `update_learnings` required the agent to pass the FULL
merged list as a string parameter — an output-token tax that grew
linearly with the learnings size, and a constant prompt-context
expansion since the contents were also inlined into the LEARNINGS
section. for repos with mature learnings the prompt was getting
visibly noisy in CI logs.

key changes:
- new `action/utils/learnings.ts` (seed/read helpers + 10k cap)
- `main.ts`: always seed; `persistLearnings` mirrors `persistSummary`
  (success path, error path, exit-signal handler, idempotent guard,
  byte-trim equality skip); forwards `model` for `LearningsRevision.model`
- `LEARNINGS` prompt section now contains only the file path + a
  one-line "read it" instruction (no contents inlined)
- `update_learnings` MCP tool deleted; `action/mcp/learnings.ts` removed
- reflection turn (`buildLearningsReflectionPrompt`) reframed around
  file editing with explicit prune-stale + leave-alone-if-nothing-new
  framing
- `learningsStep` removed from every mode checklist — surface lives only
  in the LEARNINGS prompt section + the reflection turn now

* learnings: harden seed step + refresh stale docs (review feedback)

Three findings from PR review, all implemented:

1. wrap learnings seed in best-effort try/catch (action/main.ts) —
   the always-on seed block ran unconditionally and an unwrapped
   `seedLearningsFile` (mkdir + writeFile) failure (ENOSPC, EACCES,
   hostile sandbox) would unwind into the outer main() catch and flip
   an otherwise-successful run to " Pullfrog failed" before the
   agent even started. asymmetric with `persistLearnings`'s own
   best-effort contract. wrap and log on failure; downstream
   consumers (`persistLearnings`, agent harnesses, `resolveInstructions`)
   already handle `learningsFilePath: undefined` cleanly.

2. refresh wiki/main.md — `resolveInstructions` parameter renamed
   from `learnings` to `learningsFilePath` in this PR; the data-flow
   diagram and the resolver dependency table both still showed the
   pre-refactor signature.

3. drop deleted `learnings.ts` from ROADMAP.md + RESEARCH.md
   "missing MCP tool tests" bullets — `action/mcp/learnings.ts` was
   removed in this PR; the bullets are otherwise still accurate.
2026-05-08 22:45:26 +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
2026-05-08 21:32:06 +00:00
2026-03-12 05:22:51 +00:00

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Pullfrog

Bring your favorite coding agent into GitHub


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What is Pullfrog?

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.
  • Prompt from the web — Trigger arbitrary tasks from the Pullfrog dashboard
  • Automated triggers — Configure Pullfrog to trigger agent runs in response to specific events. Each of these triggers can be associated with custom prompt instructions.
    • issue created
    • issue labeled
    • PR created
    • PR review created
    • PR review requested
    • and more...

Pullfrog is the bridge between your preferred coding agents and GitHub. Use it for:

  • 🤖 Coding tasks — Tell @pullfrog to implement something and it'll spin up a PR. If CI fails, it'll read the logs and attempt a fix automatically. It'll automatically address any PR reviews too.
  • 🔍 PR review — Coding agents are great at reviewing PRs. Using the "PR created" trigger, you can configure Pullfrog to auto-review new PRs.
  • 🤙 Issue management — Via the "issue created" trigger, Pullfrog can automatically respond to common questions, create implementation plans, and link to related issues/PRs. Or (if you're feeling lucky) you can prompt it to immediately attempt a PR addressing new issues.
  • Literally whatever — Want to have the agent automatically add docs to all new PRs? Cut a new release with agent-written notes on every commit to main? Pullfrog lets you do it.

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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