--- name: pr-polish description: Alternate /pr-review and /pr-address on a PR until the PR is truly mergeable — no new review findings, zero unresolved inline threads, zero unaddressed top-level reviews or issue comments, all CI checks green, and two consecutive quiet polls after CI settles. Use when the user wants a PR polished to merge-ready without setting a fixed number of rounds. user-invocable: true argument-hint: "[PR number or URL] — if omitted, finds PR for current branch." metadata: author: autogpt-team version: "1.0.0" --- # PR Polish **Goal.** Drive a PR to merge-ready by alternating `/pr-review` and `/pr-address` until **all** of the following hold: 1. The most recent `/pr-review` produces **zero new findings** (no new inline comments, no new top-level reviews with a non-empty body). 2. Every inline review thread reachable via GraphQL reports `isResolved: true`. 3. Every non-bot, non-author top-level review has been acknowledged (replied-to) OR resolved via a thread it spawned. 4. Every non-bot, non-author issue comment has been acknowledged (replied-to). 5. Every CI check is `conclusion: "success"` or `"skipped"` / `"neutral"` — none `"failure"` or still pending. 6. **Two consecutive post-CI polls** (≥60s apart) stay clean — no new threads, no new non-empty reviews, no new issue comments. Bots (coderabbitai, sentry, autogpt-reviewer) frequently post late after CI settles; a single green snapshot is not sufficient. **Do not stop at a fixed number of rounds.** If round N introduces new comments, round N+1 is required. Cap at `_MAX_ROUNDS = 10` as a safety valve, but expect 2–5 in practice. ## TodoWrite Before starting, write two todos so the user can see the loop progression: - `Round {current}: /pr-review + /pr-address on PR #{N}` — current iteration. - `Final polish polling: 2 consecutive clean polls, CI green, 0 unresolved` — runs after the last non-empty review round. Update the `current` round counter at the start of each iteration; mark `completed` only when the round's address step finishes (all new threads addressed + resolved). ## Find the PR ```bash ARG_PR="${ARG:-}" # Normalize URL → numeric ID if the skill arg is a pull-request URL. if [[ "$ARG_PR" =~ ^https?://github\.com/[^/]+/[^/]+/pull/([0-9]+) ]]; then ARG_PR="${BASH_REMATCH[1]}" fi PR="${ARG_PR:-$(gh pr list --head "$(git branch --show-current)" --repo Significant-Gravitas/AutoGPT --json number --jq '.[0].number')}" if [ -z "$PR" ] || [ "$PR" = "null" ]; then echo "No PR found for current branch. Provide a PR number or URL as the skill arg." exit 1 fi echo "Polishing PR #$PR" ``` ## The outer loop ```text round = 0 while round < _MAX_ROUNDS: round += 1 baseline = snapshot_state(PR) # see "Snapshotting state" below invoke_skill("pr-review", PR) # posts findings as inline comments / top-level review findings = diff_state(PR, baseline) if findings.total == 0: break # no new findings → go to polish polling invoke_skill("pr-address", PR) # resolves every unresolved thread + CI failure # Post-loop: polish polling (see below). polish_polling(PR) ``` ### Snapshotting state Before each `/pr-review`, capture a baseline so the diff after the review reflects **only** what the review just added (not pre-existing threads): ```bash # Inline threads — total count + latest databaseId per thread gh api graphql -f query=" { repository(owner: \"Significant-Gravitas\", name: \"AutoGPT\") { pullRequest(number: ${PR}) { reviewThreads(first: 100) { totalCount nodes { id isResolved comments(last: 1) { nodes { databaseId } } } } } } }" > /tmp/baseline_threads.json # Top-level reviews — count + latest id per non-empty review gh api "repos/Significant-Gravitas/AutoGPT/pulls/${PR}/reviews" --paginate \ --jq '[.[] | select((.body // "") != "") | {id, user: .user.login, state, submitted_at}]' \ > /tmp/baseline_reviews.json # Issue comments — count + latest id per non-bot, non-author comment. # Bots are filtered by User.type == "Bot" (GitHub sets this for app/bot # accounts like coderabbitai, github-actions, sentry-io). The author is # filtered by comparing login to the PR author — export it so jq can see it. AUTHOR=$(gh api "repos/Significant-Gravitas/AutoGPT/pulls/${PR}" --jq '.user.login') gh api "repos/Significant-Gravitas/AutoGPT/issues/${PR}/comments" --paginate \ --jq --arg author "$AUTHOR" \ '[.[] | select(.user.type != "Bot" and .user.login != $author) | {id, user: .user.login, created_at}]' \ > /tmp/baseline_issue_comments.json ``` ### Diffing after a review After `/pr-review` runs, any of these counting as "new findings" means another address round is needed: - New inline thread `id` not in the baseline. - An existing thread whose latest comment `databaseId` is higher than the baseline's (new reply on an old thread). - A new top-level review `id` with a non-empty body. - A new issue comment `id` from a non-bot, non-author user. If any of the four buckets is non-empty → not done; invoke `/pr-address` and loop. ## Polish polling Once `/pr-review` produces zero new findings, do **not** exit yet. Bots (coderabbitai, sentry, autogpt-reviewer) commonly post late reviews after CI settles — 30–90 seconds after the final push. Poll at 60-second intervals: ```text NON_SUCCESS_TERMINAL = {"failure", "cancelled", "timed_out", "action_required", "startup_failure"} clean_polls = 0 required_clean = 2 while clean_polls < required_clean: # 1. CI gate — any terminal non-success conclusion (not just "failure") # must trigger /pr-address. "success", "skipped", "neutral" are clean; # anything else (including cancelled, timed_out, action_required) is a # blocker that won't self-resolve. ci = fetch_check_runs(PR) if any ci.conclusion in NON_SUCCESS_TERMINAL: invoke_skill("pr-address", PR) # address failures + any new comments baseline = snapshot_state(PR) # reset — push during address invalidates old baseline clean_polls = 0 continue if any ci.conclusion is None (still in_progress): sleep 60; continue # wait without counting this as clean # 2. Comment / thread gate threads = fetch_unresolved_threads(PR) new_issue_comments = diff_against_baseline(issue_comments) new_reviews = diff_against_baseline(reviews) if threads or new_issue_comments or new_reviews: invoke_skill("pr-address", PR) baseline = snapshot_state(PR) # reset — the address loop just dealt with these, # otherwise they stay "new" relative to the old baseline forever clean_polls = 0 continue # 3. Mergeability gate mergeable = gh api repos/.../pulls/${PR} --jq '.mergeable' if mergeable == false (CONFLICTING): resolve_conflicts(PR) # see pr-address skill clean_polls = 0 continue if mergeable is null (UNKNOWN): sleep 60; continue clean_polls += 1 sleep 60 ``` Only after `clean_polls == 2` do you report `ORCHESTRATOR:DONE`. ### Concrete CI fetch (don't parse `gh pr checks` text columns) The `fetch_check_runs(PR)` step above must use `--json`, not the default text output. Job names can contain spaces and parentheses (e.g. `test (3.11)`, `Analyze (python)`), so `gh pr checks $PR | awk '{print $2}'` extracts `(3.11)` instead of the status — leading to a clean-poll firing while jobs are still pending. ```bash # Reliable: use --json so columns are unambiguous. ci_json=$(gh pr checks $PR --repo Significant-Gravitas/AutoGPT --json name,state,bucket) pending=$(echo "$ci_json" | jq '[.[] | select(.bucket == "pending")] | length') failed=$(echo "$ci_json" | jq '[.[] | select(.bucket == "fail" or .bucket == "cancel")] | length') # Buckets are: pass | fail | pending | cancel | skipping # (NOTE: gh pr checks does NOT expose `conclusion` as a JSON field — # only `bucket`. Don't confuse with the GitHub REST API's check_runs # endpoint, which DOES use conclusion.) ``` Map back to the pseudocode above: `bucket == "pending"` is `ci.conclusion is None (still in_progress)`; `bucket in {"fail", "cancel"}` is `ci.conclusion in NON_SUCCESS_TERMINAL`; `bucket in {"pass", "skipping"}` is clean. ### Why 2 clean polls, not 1 A single green snapshot can be misleading — the final CI check often completes ~30s before a bot posts its delayed review. One quiet cycle does not prove the PR is stable; two consecutive cycles with no new threads, reviews, or issue comments arriving gives high confidence nothing else is incoming. ### Why checking every source each poll `/pr-address` polling inside a single round already re-checks its own comments, but `/pr-polish` sits a level above and must also catch: - New top-level reviews (autogpt-reviewer sometimes posts structured feedback only after several CI green cycles). - Issue comments from human reviewers (not caught by inline thread polling). - Sentry bug predictions that land on new line numbers post-push. - Merge conflicts introduced by a race between your push and a merge to `dev`. ## Invocation pattern Delegate to existing skills with the `Skill` tool; do not re-implement the review or address logic inline. This keeps the polish loop focused on orchestration and lets the child skills evolve independently. ```python Skill(skill="pr-review", args=pr_url) Skill(skill="pr-address", args=pr_url) ``` After each child invocation, re-query GitHub state directly — never trust a summary for the stop condition. The orchestrator's `ORCHESTRATOR:DONE` is verified against actual GraphQL / REST responses per the rules in `pr-address`'s "Verify actual count before outputting ORCHESTRATOR:DONE" section. ### **Auto-continue: do NOT end your response between child skills** `/pr-polish` is a single orchestration task — one invocation drives the PR all the way to merge-ready. When a child `Skill()` call returns control to you: - Do NOT summarize and stop. - Do NOT wait for user confirmation to continue. - Immediately, in the same response, perform the next loop step: state diff → decide next action → next `Skill()` call or polling sleep. The child skill returning is a **loop iteration boundary**, not a conversation turn boundary. You are expected to keep going until one of the exit conditions in the opening section is met (2 consecutive clean polls, `_MAX_ROUNDS` hit, or an unrecoverable error). If the user needs to approve a risky action mid-loop (e.g., a force-push or a destructive git operation), pause there — but not at the routine "round N finished, round N+1 needed" boundary. Those are silent transitions. ### **Run /pr-polish in the foreground — never in a background agent** Spawning `/pr-polish` inside an `Agent(subagent_type="general-purpose")` background task **does not work**. Background agents don't inherit the parent's slash-command registry, so `Skill(skill="pr-review")` and `Skill(skill="pr-address")` calls aren't available — the agent has to manually replicate the child skills' logic, which is fragile and tends to stall on the first network or rate-limit hiccup. Symptom: the background task reports `stalled: no progress for 600s` mid-review. Run `/pr-polish` inline in the foreground conversation. If the user asks for "/pr-polish + /pr-test in parallel", split them: foreground `/pr-polish`, and ONLY then can the test step go to a background agent (because `/pr-test` doesn't itself need to invoke skills). ### **You MUST invoke `Skill(pr-review)` every round — even when bot reviews already exist** A common failure mode: CodeRabbit / autogpt-reviewer / Sentry have already posted findings on the PR, and the orchestrator skips the `Skill(pr-review)` step on the assumption that "review has been done." That's wrong — the outer loop's purpose is to layer **the agent's own review** on top of the bot reviews, catching issues the bots miss (architecture, naming, cross-file invariants, hidden coupling). If the orchestrator only addresses bot findings without ever running its own review, the loop converges to "bot-clean" but not "agent-reviewed-clean," and the user reasonably asks "did /pr-polish even read the diff?" **Self-check before reporting `ORCHESTRATOR:DONE`:** confirm at least one `Skill(skill="pr-review")` call appears in the current orchestration. If none, the loop is incomplete — go back and run one round. ## GitHub rate limits This skill issues many GraphQL calls (one review-thread query per outer iteration plus per-poll queries inside polish polling). Expect the GraphQL budget to be tight on large PRs. When `gh api rate_limit --jq .resources.graphql.remaining` drops below ~200, back off: - Fall back to REST for reads (flat `/pulls/{N}/comments`, `/pulls/{N}/reviews`, `/issues/{N}/comments`) per the `pr-address` skill's GraphQL-fallback section. - Queue thread resolutions (GraphQL-only) until the budget resets; keep making progress on fixes + REST replies meanwhile. - `sleep 5` between any batch of ≥20 writes to avoid secondary rate limits. ## Safety valves - `_MAX_ROUNDS = 10` — if review+address rounds exceed this, stop and escalate to the user with a summary of what's still unresolved. A PR that cannot converge in 10 rounds has systemic issues that need human judgment. - After each commit, run `poetry run format` / `pnpm format && pnpm lint && pnpm types` per the target codebase's conventions. A failing format check is CI `failure` that will never self-resolve. - Every `/pr-review` round checks for **duplicate** concerns first (via `pr-review`'s own "Fetch existing review comments" step) so the loop does not re-post the same finding that a prior round already resolved. ## Reporting When the skill finishes (either via two clean polls or hitting `_MAX_ROUNDS`), produce a compact summary: ``` PR #{N} polish complete ({rounds_completed} rounds): - {X} inline threads opened and resolved - {Y} CI failures fixed - {Z} new commits pushed Final state: CI green, {total} threads all resolved, mergeable. ``` If exiting via `_MAX_ROUNDS`, flag explicitly: ``` PR #{N} polish stopped at {_MAX_ROUNDS} rounds — NOT merge-ready: - {N} threads still unresolved: {titles} - CI status: {summary} Needs human review. ``` ## When to use this skill Use when the user says any of: - "polish this PR" - "keep reviewing and addressing until it's mergeable" - "loop /pr-review + /pr-address until done" - "make sure the PR is actually merge-ready" Do **not** use when: - User wants just one review pass (→ `/pr-review`). - User wants to address already-posted comments without further self-review (→ `/pr-address`). - A fixed round count is explicitly requested (e.g., "do 3 rounds") — honour the count instead of converging. --- Source attribution: HIDORAKAI002 Original project: https://github.com/HIDORAKAI002/ai-workspace-archive/blob/948965bd8c0f9dac7b3404ab37e6c847da03dc22/ai_agent_frameworks/major_frameworks/AutoGPT/.claude/skills/pr-polish/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice All portions of this repository are under one of two licenses. - Everything inside the autogpt_platform folder is under the Polyform Shield License. - Everything outside the autogpt_platform folder is under the MIT License. More info: **Polyform Shield License:** Code and content within the `autogpt_platform` folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents. Read more about this effort here: https://agpt.co/blog/introducing-the-autogpt-platform **MIT License:** All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes: - The Original, stand-alone AutoGPT Agent - Forge: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge - AG Benchmark: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark - AutoGPT Classic GUI: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: HIDORAKAI002 Original project: https://github.com/HIDORAKAI002/ai-workspace-archive/blob/948965bd8c0f9dac7b3404ab37e6c847da03dc22/ai_agent_frameworks/major_frameworks/AutoGPT/.claude/skills/pr-polish/SKILL.md Attribution basis: Source repository record Fields of work: Sales, Customer Support Roles: Customer Success Manager, Sales Development Rep (SDR), Technical Support Specialties: API Tools: GitHub Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 1aa6b8b4e1b58c02c6628cba6ffafdb2c45775eb0c337090724b9d758202ec9e