* review prompt: tighten body-section bar + add inline technical-details
Two layers of tightening to the Review/IncrementalReview prompts in
PR_SUMMARY_FORMAT (and the per-mode aggregate-&-draft step):
1. Reframe inline-vs-body split. Body `### ` sections are now reserved
for concerns that genuinely have no line to anchor to — absence,
sequencing, design decisions, scope questions, architectural risk.
Drop the "cross-cutting concerns" framing (misled the agent into
either filing nothing in the body or filing multi-file anchored
findings there).
2. Add a "Hunt for non-anchored concerns" sub-step to both Review (step
6) and IncrementalReview (step 8) aggregate phases. Diagnosis from
PR #767's auto-review: on substantial PRs the agent surfaced
findings but routed all of them inline, producing reviews with zero
`### ` body sections even on diffs where non-anchored concerns
clearly existed.
3. Replace the abstract `### ` example with a concrete non-anchored
one ("Legacy `opencode.ts` has no documented deletion plan") so the
agent pattern-matches the absence-shaped finding, not a line-bug.
4. Add an "Inline technical details" subsection to PR_SUMMARY_FORMAT
so inline comments can carry a `<details>Technical details</details>`
block when the fix has cross-file implications. Rename the existing
"Agent details" inline collapsible to "Technical details" for
consistency with body sections.
5. (Carried over from prior uncommitted work) Restructure the review
metadata block from `<details>Review metadata</details>` into an
HTML comment + an italic TL;DR commit-range line. The HTML comment
keeps the metadata addressable for downstream agents without
eating user-visible review real estate.
No tests touched.
* wiki: document multi-model end-to-end eval pattern
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:Releaseon:push:tags:['v*']permissions:contents:writejobs:release:runs-on:ubuntu-lateststeps:- name:Checkoutuses:actions/checkout@v4with:fetch-depth:0- name:Generate release notesid:notesuses:pullfrog/pullfrog@v0with: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 releaserun:| 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:${{ steps.notes.outputs.result }}
Example: Structured Output with Zod Schema
You can force the agent to return structured JSON output by providing a JSON schema. This allows you to reliably parse and use the agent's response in subsequent workflow steps.
You can define your JSON schema directly or uou can use any validation library that converts to JSON Schema. Here's an example using Zod:
name:Release Checkon:pull_request:types:[closed]jobs:check-release:if:github.event.pull_request.merged == trueruns-on:ubuntu-lateststeps:- uses:actions/checkout@v4- name:Install dependenciesrun:npm install --no-save --no-package-lock zod @actions/core- name:Generate Schemaid:schemarun:| node -e '
import { z } from "zod";
import { setOutput } from "@actions/core";
const schema = z.object({
version: z.string().describe("Semantic version number (e.g. 1.0.0)"),
isBreaking: z.boolean().describe("Whether this release contains breaking changes"),
changelog: z.array(z.string()).describe("List of changes in this release"),
});
setOutput("schema", JSON.stringify(z.toJSONSchema(schema)));
'- name:Analyze PRid:analysisuses:pullfrog/pullfrog@v0with:prompt:| Analyze this PR and determine semantic versioning impact.
Return a JSON object matching the provided schema.output_schema:${{ steps.schema.outputs.schema }}env:ANTHROPIC_API_KEY:${{ secrets.ANTHROPIC_API_KEY }}- name:Process Resultrun:| # Parse the JSON result using fromJSON()
echo "Version: ${{ fromJSON(steps.analysis.outputs.result).version }}"
echo "Breaking: ${{ fromJSON(steps.analysis.outputs.result).isBreaking }}"