## 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>
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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:
ghis 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:
- Read the workflow or failing command to identify the real gate.
- Reproduce locally where reasonable.
- Make the smallest correct fix.
- Run focused verification first, then broader verification if needed.
- Commit in a logical commit.
- 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.