Files
PaperClipAI/.agents/skills/prcheckloop/SKILL.md
T
DottaandPaperclip 953b315dfb Shorten skill frontmatter descriptions (#9353)
## Thinking Path

> - Paperclip is the open source app people use to manage AI agents for
work.
> - Paperclip agents can load repository and catalog skills, and Codex
renders skill names and frontmatter descriptions into startup context.
> - Long descriptions consume the fixed skill metadata budget before
Codex can use the progressively disclosed skill bodies.
> - The repo `.agents/skills` descriptions and a few shipped catalog
descriptions had grown into operational documentation instead of short
trigger metadata.
> - This pull request keeps the strongest trigger language in
frontmatter while leaving detailed procedures in each skill body.
> - The benefit is lower prompt overhead, more reliable skill
triggering, and a regression guard that prevents description drift from
returning.

## Linked Issues or Issue Description

No public GitHub issue found for this maintenance item.

### Pre-submission checklist

- [x] I have searched existing open and closed issues and this is not a
duplicate.
- [x] I am working against `master`.
- [x] I have confirmed the issue originates in Paperclip's shipped skill
metadata, not in a local agent adapter or provider.

### What happened?

Codex startup renders discovered skill names and frontmatter
descriptions into a fixed skill metadata budget. Several repository
skill descriptions and one shipped catalog description had grown into
long-form operational guidance, which can force Codex to truncate
descriptions before the model has enough trigger signal to select the
right skill.

### Expected behavior

Skill frontmatter descriptions should stay short trigger summaries: one
capability sentence plus a “use when” clause. Detailed procedures should
stay in the skill body and load only after the skill triggers.

### Steps to reproduce

1. Inspect `.agents/skills/*/SKILL.md` and
`packages/skills-catalog/catalog/**/SKILL.md` frontmatter descriptions.
2. Measure folded YAML `description` values.
3. Observe descriptions above the intended short-trigger range,
including descriptions above 300 characters.
4. Run the new shipped catalog test to verify future descriptions stay
capped.

### Paperclip version or commit

Reproduced on `master` at `cc81eefb6047d8eaf57faf785f421c03dc97073c`.

### Deployment mode

Local dev / source checkout metadata inspection. This is not
database-related.

### Installation method

Built from source.

### Agent adapter(s) involved

Codex, because Codex startup uses the skill metadata prompt budget. The
metadata source itself is core repository/catalog content.

### Database mode

Not database-related.

### Access context

Not applicable; this is static repository metadata.

### Node.js version

`v22.22.2` in the verification environment.

### Operating system

Linux container environment.

### Relevant logs or output

Final measurement after this PR: 29 source `SKILL.md` files, max
description length 215 chars, 5,449 total description chars, estimated
1,363 description tokens at 4 chars/token.

### Relevant config

None.

### Additional context

The shipped catalog manifest was regenerated so the generated package
metadata matches the edited catalog `SKILL.md` sources.

### Privacy checklist

- [x] I have reviewed all pasted output for PII and redacted where
necessary.

## What Changed

- Shortened long `.agents/skills/*/SKILL.md` frontmatter descriptions to
concise capability plus use-when trigger clauses.
- Shortened the over-budget shipped skills catalog descriptions for
wireframe, Paperclip capsules, and reflection coach.
- Regenerated `packages/skills-catalog/generated/catalog.json` so
shipped metadata matches source skill frontmatter.
- Added a Vitest regression guard that caps repo skill source
descriptions and generated catalog descriptions at 300 characters.

## Verification

- `pnpm --filter @paperclipai/skills-catalog build:manifest`
- `pnpm --filter @paperclipai/skills-catalog test` — 5 files passed, 19
tests passed
- `pnpm --filter @paperclipai/skills-catalog typecheck`
- Final measurement: 29 source `SKILL.md` files, max description length
215 chars, 5,449 total description chars, estimated 1,363 description
tokens at 4 chars/token.

Note: the clean PR worktree was created from `origin/master` and
contains only this commit, but it does not have `node_modules`; running
`pnpm --filter @paperclipai/skills-catalog test` there failed at
tool/package resolution (`vitest`, `tsc`, `@paperclipai/shared`). The
dependency-equipped workspace passed the commands above before the
commit was cherry-picked onto the clean branch.

## Risks

Low risk. This changes skill metadata and tests only. The main risk is
over-trimming a useful trigger phrase, mitigated by keeping explicit
“use when” clauses and leaving detailed guidance in the skill bodies.

> For core feature work, check [`ROADMAP.md`](ROADMAP.md) first and
discuss it in `#dev` before opening the PR. Feature PRs that overlap
with planned core work may need to be redirected — check the roadmap
first. See `CONTRIBUTING.md`.

## Model Used

OpenAI GPT-5 Codex coding agent via Paperclip/Codex, with shell and
file-edit tool use. Exact API model ID and context window were not
exposed in the runtime.

## Checklist

- [x] I have included a thinking path that traces from project context
to this change
- [x] I have specified the model used (with version and capability
details)
- [x] I have checked ROADMAP.md and confirmed this PR does not duplicate
planned core work
- [x] I have searched GitHub for duplicate or related PRs and linked
them above
- [x] I have either (a) linked existing issues with `Fixes: #` / `Closes
#` / `Refs #` OR (b) described the issue in-PR following the relevant
issue template
- [x] I have not referenced internal/instance-local Paperclip issues or
links (only public GitHub `#NNN` / `github.com/paperclipai/paperclip`
URLs)
- [x] My branch name describes the change (e.g. `docs/...`, `fix/...`)
and contains no internal Paperclip ticket id or instance-derived details
- [x] I have run tests locally and they pass
- [x] I have added or updated tests where applicable
- [x] I have updated relevant documentation to reflect my changes
- [x] I have considered and documented any risks above
- [ ] All Paperclip CI gates are green
- [ ] Greptile is 5/5 with no open P2s, recommendations, or follow-ups
- [x] I will address all Greptile and reviewer comments before
requesting merge

---------

Co-authored-by: Paperclip <noreply@paperclip.ing>
2026-07-10 08:46:20 -05:00

5.7 KiB

name, description
name description
prcheckloop Iterate on a GitHub PR until latest-head checks are green or a precise blocker is named. Use when a PR still has failing or pending checks after review fixes, including after greploop.

PRCheckloop

Get a GitHub PR to a fully green check state, or exit with a concrete blocker.

Scope

  • GitHub PRs only. If the repo is GitLab, stop and use check-pr.
  • Focus on checks for the latest PR head SHA, not old commits.
  • Focus on CI/status checks, not review comments or PR template cleanup.
  • If the user also wants review-comment cleanup, pair this with check-pr.

Inputs

  • PR number (optional): If not provided, detect the PR for the current branch.
  • Max iterations: default 5.

Workflow

1. Identify the PR

If no PR number is provided, detect it from the current branch:

gh pr view --json number,headRefName,headRefOid,url,isDraft

If needed, switch to the PR branch before making changes.

Stop early if:

  • gh is not authenticated
  • there is no PR for the branch
  • the repo is not hosted on GitHub

2. Track the latest head SHA

Always work against the current PR head SHA:

PR_JSON=$(gh pr view "$PR_NUMBER" --json number,headRefName,headRefOid,url)
HEAD_SHA=$(echo "$PR_JSON" | jq -r .headRefOid)
PR_URL=$(echo "$PR_JSON" | jq -r .url)

Ignore failing checks from older SHAs. After every push, refresh HEAD_SHA and restart the inspection loop.

3. Inventory checks for that SHA

Fetch both GitHub check runs and legacy commit status contexts:

gh api "repos/{owner}/{repo}/commits/$HEAD_SHA/check-runs?per_page=100"
gh api "repos/{owner}/{repo}/commits/$HEAD_SHA/status"

For a compact PR-level view, this GraphQL payload is useful:

gh api graphql -f query='
query($owner:String!, $repo:String!, $pr:Int!) {
  repository(owner:$owner, name:$repo) {
    pullRequest(number:$pr) {
      headRefOid
      url
      statusCheckRollup {
        contexts(first:100) {
          nodes {
            __typename
            ... on CheckRun { name status conclusion detailsUrl workflowName }
            ... on StatusContext { context state targetUrl description }
          }
        }
      }
    }
  }
}' -F owner=OWNER -F repo=REPO -F pr="$PR_NUMBER"

4. Wait for checks to actually run

After a new push, checks can take a moment to appear. Poll every 15-30 seconds until one of these is true:

  • checks have appeared and every item is in a terminal state
  • checks have appeared and at least one failed
  • no checks appear after a reasonable wait, usually 2 minutes

Treat these as terminal success states:

  • check runs: SUCCESS, NEUTRAL, SKIPPED
  • status contexts: SUCCESS

Treat these as pending:

  • check runs: QUEUED, PENDING, WAITING, REQUESTED, IN_PROGRESS
  • status contexts: PENDING

Treat these as failures:

  • check runs: FAILURE, TIMED_OUT, CANCELLED, ACTION_REQUIRED, STARTUP_FAILURE, STALE
  • status contexts: FAILURE, ERROR

If no checks appear for the latest SHA, inspect .github/workflows/, workflow path filters, and branch protection expectations. If the missing check cannot be caused or fixed from the repo, escalate.

5. Investigate failing checks

For GitHub Actions failures, inspect runs and failed logs for the current SHA:

gh run list --commit "$HEAD_SHA" --json databaseId,workflowName,status,conclusion,url,headSha
gh run view <RUN_ID> --json databaseId,name,workflowName,status,conclusion,jobs,url,headSha
gh run view <RUN_ID> --log-failed

For each failing check, classify it:

Failure type Action
Code/test regression Reproduce locally, fix, and verify
Lint/type/build mismatch Run the matching local command from the workflow and fix it
Flake or transient infra issue Rerun once if evidence supports flakiness
External service/status app failure Escalate with the details URL and owner guess
Missing secret/permission/branch protection issue Escalate immediately

Only rerun a failed job once without code changes. Do not loop on reruns.

6. Fix actionable failures

If the failure is actionable from the checked-out code:

  1. Read the workflow or failing command to identify the real gate.
  2. Reproduce locally where reasonable.
  3. Make the smallest correct fix.
  4. Run focused verification first, then broader verification if needed.
  5. Commit in a logical commit.
  6. Push before re-checking the PR.

Do not stop at a local fix. The loop is only complete when the remote PR checks for the new head SHA are green.

7. Push and repeat

After each fix:

git push
sleep 5

Then refresh the PR metadata, get the new HEAD_SHA, and restart from Step 3.

Exit the loop only when:

  • all checks for the latest head SHA are green, or
  • a blocker remains after reasonable repair effort, or
  • the max iteration count is reached

8. Escalate blockers precisely

If you cannot get the PR green, report:

  • PR URL
  • latest head SHA
  • exact failing or missing check names
  • details URLs
  • what you already tried
  • why it is blocked
  • who should likely unblock it
  • the next concrete action

Good blocker examples:

  • external status app outage
  • missing GitHub secret or permission
  • required check name mismatch in branch protection
  • persistent flake after one rerun
  • failure needs credentials or infrastructure access you do not have

Output

When the skill completes, report:

  • PR URL and branch
  • final head SHA
  • green/pending/failing check summary
  • fixes made and verification run
  • whether changes were pushed
  • blocker summary if not fully green

Notes

  • This skill is intentionally narrower than check-pr: it is a repair loop for PR checks.
  • This skill complements greploop: Greptile can be perfect while CI is still red.