review-reviewers

name	review-reviewers
description	Hourly outcome-based analysis of tend's CI behavior — checks whether tend's outputs were accepted or rejected, escalating to session logs only when outcomes look wrong.
argument-hint	<owner/repo>
metadata	{"internal":true}

Review Reviewers

Analyze tend's CI behavior on the target repo over the past hour. Focus on outcomes — what the bot produced publicly and whether it was accepted — rather than internal session mechanics. Create PRs or issues on tend when outcomes reveal behavioral problems.

First steps

Load /tend-ci-runner:running-in-ci first — it contains CI security rules, PR/comment formatting (line wrapping, heredoc hazards), and polling conventions. This skill opens PRs and issue comments on tend, so those rules apply.

Cost discipline: cheap subagents for exploration

Session log parsing and outcome checking are token-heavy. Delegate all broad exploration to a cheap subagent (e.g. Haiku / gpt-mini). Keep the main agent for judgment: evaluating findings against gates, deciding whether to act, and drafting PRs.

Pattern:

1. Main agent sets up context (bot identity, repo guidance, run list)
2. Main agent spawns cheap subagent to survey outcomes across all runs → receives structured summary
3. Main agent evaluates the summary against gates
4. If needed, main agent spawns another cheap subagent to investigate specific session logs → receives diagnosis
5. Main agent drafts fix PR if warranted

Core principle: outcomes over internals

The bot's job is to produce useful outputs: reviews, triage comments, fix commits, issue responses. The cheapest way to evaluate quality is to check whether those outputs were accepted (merged, kept, acted on) or rejected (reverted, closed, corrected, disagreed with).

Session logs are expensive to download and parse. Only escalate to session-log inspection when outcome signals indicate a real problem worth diagnosing.

Core principle: repo-specific guidance is primary

Each adopter repo has its own guidance (running-tend skill or equivalent) that shapes how the bot should behave in that repo. This repo-specific guidance takes precedence over tend's default rules. The bot's job is to follow the repo-specific guidance first, falling back to tend's defaults only where the repo doesn't specify.

Non-issues: do not flag these

Some patterns look suspicious but are intentional. Before drafting a finding, check this list — flagging expected behaviors creates maintainer churn and costs trust.

• tend-review re-approving after the bot pushed a fix commit. The reviewer role is independent of commit and PR authorship. Re-reviewing (and re-approving) after tend-notifications, tend-ci-fix, or a mention run pushes a fix is expected behavior, not a re-approval loop. Authorship-keyed guards (skipping re-review when the bot pushed) are not the accepted shape — do not propose them. If you observe stacked approvals from concurrent runs that raced with concurrency-group cancellation, that is a concurrency issue (the cancelled runs managed to POST before the SIGTERM arrived), not a review-rule problem.

• tend-review exiting silent on a genuinely self-authored PR with no concerns. The bundled review skill explicitly authorizes "stay silent and skip to step 6" on self-authored PRs (PR_AUTHOR == BOT_LOGIN) without findings (review/SKILL.md, "Self-authored PRs") — GitHub rejects self-approvals, so APPROVE isn't an option. Treat silent first-reviews on bot-authored PRs as expected behavior, not "silent omission" or "divergence". Before citing an "APPROVE-on-bot-authored-PR pattern" as a counter-precedent, verify each cited PR's literal author with gh pr view <n> --json author --jq '.author.login': owner-authored PRs (e.g. max-sixty) are reviewed and approved normally and are not a "bot-authored APPROVE" precedent.

• tend-review staying silent on a draft PR with no actionable findings. GitHub's state field reports draft PRs as OPEN, so "no review on an open PR" reads as omission when it is expected behavior. The bundled review skill's draft-mode (review/SKILL.md, "Draft mode") runs a lighter COMMENT-only review, never APPROVE (GitHub blocks approving drafts), and explicitly authorizes "Stay silent if there's nothing actionable; don't post a 'looks fine' comment." Before flagging a silent tend-review — or escalating to session logs to explain it — check gh api repos/OWNER/REPO/pulls/N --jq '.draft': a silent first-review on a draft: true PR with clean findings is skill-designed silence, the sibling of the self-authored case above, not omission or divergence. This also covers a silent re-review after a human pushes to a still-draft PR.

• tend-mention firing on the bot's own comments and exiting silently. When the bot comments on an issue or PR where it has previously participated (including its own tracking issues such as review-reviewers-tracking and review-runs-tracking), the issue_comment event fires tend-mention; the prompt's self-conversation guard then detects the self-trigger and exits silently after a few Claude turns. The same shape occurs when the bot's own tend-review review mirrors back as a pull_request_review event and fires tend-mention. A deterministic verify-gate skip of the bot's own comments (issue_comment / pull_request_review_comment where comment.user.login == bot) is the accepted shape here and is implemented in the verify job — it suppresses no legitimate work, since the bot never needs to respond to its own comment. This is distinct from the authorship-keyed guards rejected for tend-review, where the bot's fix-push must still be re-reviewed (skipping it drops real work). The pull_request_review self-review path is not covered by that skip — it carries intentional actionable-signal handling (the empty-body-APPROVED gate) — so treat a lingering no-op on that path as expected, not a gap. For any new loop risk beyond the bot's own comments, prefer a label-based skip (e.g. skipping tend-outage-labeled issues); propose an authorship/sender filter only when the loop can't be expressed with a label and the skipped events carry no legitimate work.

• tend-mention firing a second time on issue_comment.edited after the commenter edits their own comment shortly after posting. The created and edited events both trigger tend-mention; when the created run exits silently (no engagement) and the commenter then refines the comment within seconds, the edited retrigger runs the agent again and pays full context-load before silent-exiting via dedup-recheck. Do not propose a commenter-self-edit skip (e.g. sender == comment.user.login + short updated_at - created_at window): an edit can change relevance — adding substantive content that would now merit a response — and skipping the retrigger means the agent never re-evaluates the new body. The dedup-recheck handles "bot already responded"; it cannot handle "the edited content would now merit a response", and there is no recovery path once the retrigger is dropped. The recheck-before-posting silent-exit is the correct cost; record as carry observations and move on.

• tend-notifications mark-reading a cross-repo CheckSuite ci_activity notification from an abandoned fork and exiting silently. A fork that doesn't sync upstream keeps running its own scheduled tend-* workflows; when those fail, the canonical bot receives reason: ci_activity CheckSuite notifications about another repo's CI. The notifications skill classifies them as cross-repo not-actionable and marks them read, but the agent still spins up. The accepted shape is the workflow-level github.repository_owner fork-owner guard, which causes synced forks to no-op on the cron; the residual cost of abandoned-fork notifications is small enough not to justify a permanent pre-check layer. Do not propose a pre-check filter keyed on reason, subject_type, or sender, and do not maintain a per-month occurrence threshold for this notification shape — record as carry observations and move on.

Target repo

Target repo: $ARGUMENTS

Analysis targets an adopter repo whose CI runs are analyzed. Findings result in PRs/issues on the current repo (tend) to improve skills and workflows.

Use -R $ARGUMENTS for commands that access the target repo (querying runs, PRs, issues). Commands without -R default to tend.

@review-gates.md

Evidence accumulation

Each run only sees a window of CI sessions, but patterns emerge over days or weeks. Evidence for this skill lives in secret gists owned by the bot — one per (target repo, month) pair. A monthly tracking issue on tend labeled review-reviewers-tracking lists the gists via bot comments, so maintainers can discover them.

Secret gists are URL-unlisted but readable by anyone with the URL; they are at least as private as the current public tracking issues, and give a single structured file that accumulates per-target findings without hitting the 65 KB comment limit.

Setup

MONTH=$(date +%Y-%m)
TRACKING_LABEL="review-reviewers-tracking"
TARGET="$ARGUMENTS"
GIST_DESC="review-reviewers evidence: $TARGET $MONTH"

Finding or creating the tracking issue

The tracking issue lives on tend (the current repo). It indexes gists via one comment per new gist — no per-run comments, no body edits.

The workflow's init-tracking job runs before the matrix and creates the monthly tracking issue if absent, so matrix legs always find an existing one. The find-or-create logic below remains the fallback for ad-hoc invocations and as a safety net; sort lowest-numbered first in case a race ever does produce duplicates. gh issue create prints the new issue's URL; parse the number from its basename.

TRACKING_NUMBER=$(gh issue list --state open --label "$TRACKING_LABEL" \
  --json number,title --jq ".[] | select(.title | contains(\"$MONTH\")) | .number" \
  | sort -n | head -1)

if [ -z "$TRACKING_NUMBER" ]; then
  cat > /tmp/tracking-body.md << 'EOF'
Monthly tracking issue for `review-reviewers`. Per-target evidence lives in secret gists owned by the bot. A comment below is posted when each target's gist is first created.

**Do not close manually** — a new issue is created each month.
EOF
  TRACKING_URL=$(gh issue create \
    --title "$TRACKING_LABEL: $MONTH" \
    --label "$TRACKING_LABEL" \
    -F /tmp/tracking-body.md)
  if [ -z "$TRACKING_URL" ]; then
    echo "ERROR: gh issue create failed" >&2
    exit 1
  fi
  TRACKING_NUMBER=$(basename "$TRACKING_URL")
fi

Finding or creating the evidence gist

Search the bot's own gists by description. Descriptions are our stable key — GitHub does not let us pick gist IDs.

GIST_ID=$(gh api /gists --paginate \
  --jq ".[] | select(.description == \"$GIST_DESC\") | .id" | head -1)

if [ -z "$GIST_ID" ]; then
  # The gist file takes its name from the local file's basename; later reads
  # and PATCHes target `findings.md`, so the seed must live at that basename.
  mkdir -p /tmp/gist-seed
  # Use the Write tool to author /tmp/gist-seed/findings.md (substituting
  # $TARGET and $MONTH from the environment). Content:
  #
  #   # review-reviewers evidence — <target> — <YYYY-MM>
  #
  #   Secret gist. Append-only log of below-threshold findings used for gate evaluation.
  GIST_URL=$(gh gist create --desc "$GIST_DESC" /tmp/gist-seed/findings.md)
  if [ -z "$GIST_URL" ]; then
    echo "ERROR: gh gist create failed — TEND_BOT_TOKEN likely lacks 'gist' scope (see install-tend)" >&2
    exit 1
  fi
  GIST_ID=$(basename "$GIST_URL")
  # First time this month for this target — announce the gist on the tracking issue
  gh issue comment "$TRACKING_NUMBER" \
    --body "Evidence gist for \`$TARGET\`: $GIST_URL"
else
  GIST_URL="https://gist.github.com/$GIST_ID"
fi

The TEND_BOT_TOKEN needs gist scope (see install-tend). Without it, gh gist create fails with 403 Forbidden and the skill exits before posting a broken tracking-issue comment.

Reading historical evidence

Before applying the gates, read the current month's gist for this target. Pass --raw so gh emits the file content verbatim instead of a TTY-rendered form. The recording step below appends to this same file, so fetch once:

gh gist view "$GIST_ID" -f findings.md --raw > /tmp/current.md

Also check last month's gist for recent carry-over. Compute last month by subtracting a day from the first of the current month — date -d 'last month' on the 31st can return the current month on GNU date, silently skipping the prior month's evidence:

FIRST=$(date -u +%Y-%m-01)
LAST_MONTH=$(date -u -d "$FIRST -1 day" +%Y-%m 2>/dev/null || date -u -v-1d -jf %Y-%m-%d "$FIRST" +%Y-%m)
LAST_DESC="review-reviewers evidence: $TARGET $LAST_MONTH"
LAST_GIST_ID=$(gh api /gists --paginate \
  --jq ".[] | select(.description == \"$LAST_DESC\") | .id" | head -1)
[ -n "$LAST_GIST_ID" ] && gh gist view "$LAST_GIST_ID" -f findings.md --raw > /tmp/last-month-findings.md

Recording below-threshold findings

Append a ## Run <RUN_ID> heading every run, even when no problem finding exceeded a gate threshold. For all-clear hours, record a single Low-evidence "all-clear" entry as the body — runs analyzed, outcomes checked, no concerning signals. The heading per run is the audit trail that prior runs read to count cumulative occurrences and confirm which hours were analyzed; missing entries leave gaps that erode gate evaluation across runs.

After applying the gates, write each run's new findings (format in @review-gates.md) to /tmp/findings.md, then append them to the gist's findings.md. Reuse the current content already fetched into /tmp/current.md in "Reading historical evidence", concatenate, and PATCH via the API (--rawfile preserves trailing newlines that command substitution would strip):

# Verify the run heading references this run's $GITHUB_RUN_ID literally —
# fabricated round numbers produce dead Workflow links, see @review-gates.md.
grep -qF "$GITHUB_RUN_ID" /tmp/findings.md || {
  echo "ERROR: /tmp/findings.md does not contain \$GITHUB_RUN_ID=$GITHUB_RUN_ID — refusing to PATCH gist" >&2
  exit 1
}
cat /tmp/current.md /tmp/findings.md > /tmp/combined.md
jq -n --rawfile content /tmp/combined.md \
  '{files: {"findings.md": {content: $content}}}' \
  | gh api "/gists/$GIST_ID" -X PATCH --input -

Never replace wholesale — prior entries contain per-run evidence needed for gate evaluation. See @review-gates.md for the per-finding format.

Step 1: Setup

Resolve the target repo's bot login and load repo-specific guidance upfront — both are needed throughout. gh api user returns the analysis bot (e.g., tend-agent when review-reviewers runs on tend), which is typically not the target repo's bot — filtering reviews/comments by the wrong login produces false "no bot output" negatives. Read bot_name from the target repo's .config/tend.yaml:

BOT_LOGIN=$(gh api "repos/$ARGUMENTS/contents/.config/tend.yaml" --jq '.content' 2>/dev/null \
  | base64 -d 2>/dev/null \
  | yq '.bot_name // ""' 2>/dev/null)
if [ -z "$BOT_LOGIN" ]; then
  echo "ERROR: could not resolve bot_name from $ARGUMENTS/.config/tend.yaml" >&2
  exit 1
fi
echo "BOT_LOGIN=$BOT_LOGIN (target: $ARGUMENTS)"

Read the target repo's repo-specific guidance to understand what the bot was told to do:

gh api "repos/$ARGUMENTS/contents/.claude/skills/running-tend/SKILL.md" \
  --jq '.content' | base64 -d

If the file doesn't exist, try common alternatives (.claude/skills/running-tend.md, .claude/CLAUDE.md). Understanding the repo's guidance is essential context for evaluating outcomes — without it, you'll misjudge authorized behavior as a violation.

Then list recently completed tend CI runs on the target repo:

TARGET_REPO=$ARGUMENTS ${CLAUDE_PLUGIN_ROOT}/scripts/list-recent-runs.sh

The script discovers tend-* workflows by default. Pass additional prefixes as arguments to include other workflows (e.g., review-reviewers when analyzing tend itself).

If empty, record the run as all-clear per "Recording below-threshold findings" above, then skip to Step 6.

Step 2: Survey outcomes via cheap subagent

Spawn a cheap subagent to check outcomes across all runs from Step 1. The subagent does the token-heavy work of mapping runs to PRs/issues and checking acceptance signals.

Use a cheap subagent (e.g. Haiku / gpt-mini) and a prompt like:

Survey bot outcomes on $ARGUMENTS for the following runs: [run IDs from Step 1]. The bot's login is $BOT_LOGIN.

For each run, determine:

1. Did the bot produce visible output (review, comment, issue action, commit)?
2. If yes, was the output accepted or rejected?

How to map runs to outputs:

• tend-review: gh -R $ARGUMENTS run view <run-id> --json headBranch → find PR via gh -R $ARGUMENTS pr list --head <branch> --state all → check bot reviews via gh api repos/$ARGUMENTS/pulls/<pr>/reviews
• tend-notifications: check for recent bot comments/issue-close events in the past hour
• tend-mention: map run to issue/PR from triggering comment, check for bot replies
• tend-ci-fix: map run → PR via headBranch, check for bot commits

Negative outcome signals — report any sign the bot's output was rejected, corrected, or ignored. Common shapes (use judgment for signals not listed):

• Human reviewer posted CHANGES_REQUESTED after bot approved
• PR closed without merge shortly after bot approved
• Bot posted no review despite a tend-review run completing on an open PR
• Subsequent commits reversed changes the bot approved
• Bot-closed issue was reopened
• Fix commit was reverted or CI still failing after bot pushed
• Human replied to bot with correction or complaint
• Bot comment contains corruption (literal ${, unescaped bangs, backslash-backticks, broken heredoc markers)

Corruption-scan recipe. Save bot bodies to a file, then scan with grep:

mkdir -p /tmp/bot-output && : > /tmp/bot-output/all.txt
# Issue/PR comments (issue_comment endpoint)
for n in <pr-or-issue-numbers>; do
  gh api "repos/$ARGUMENTS/issues/$n/comments?per_page=100" \
    --jq ".[] | select(.user.login == \"$BOT_LOGIN\" and .created_at > \"<window-start>\") | \"=== #$n issue-comment \(.id) ===\n\(.body)\n\"" \
    >> /tmp/bot-output/all.txt
done
# Issue bodies (when bot opened the issue this window)
for n in <bot-opened-issues>; do
  gh api "repos/$ARGUMENTS/issues/$n" \
    --jq "select(.user.login == \"$BOT_LOGIN\" and .created_at > \"<window-start>\") | \"=== ISSUE #$n body ===\n\(.body)\n\"" \
    >> /tmp/bot-output/all.txt
done
# PR bodies (only when bot opened the PR this window)
for n in <bot-opened-prs>; do
  gh api "repos/$ARGUMENTS/pulls/$n" \
    --jq "select(.user.login == \"$BOT_LOGIN\" and .created_at > \"<window-start>\") | \"=== PR #$n body ===\n\(.body)\n\"" \
    >> /tmp/bot-output/all.txt
done
# PR reviews + inline review comments — any PR the bot reviewed/commented on, not just
# bot-opened. tend-review's output ships on human-authored PRs (the most common surface)
# which would never appear in <bot-opened-prs>.
for n in <pr-numbers-bot-reviewed>; do
  gh api "repos/$ARGUMENTS/pulls/$n/reviews" \
    --jq ".[] | select(.user.login == \"$BOT_LOGIN\" and .submitted_at > \"<window-start>\") | \"=== PR #$n review \(.id) state=\(.state) ===\n\(.body)\n\"" \
    >> /tmp/bot-output/all.txt
  gh api "repos/$ARGUMENTS/pulls/$n/comments?per_page=100" \
    --jq ".[] | select(.user.login == \"$BOT_LOGIN\" and .created_at > \"<window-start>\") | \"=== PR #$n inline-comment \(.id) ===\n\(.body)\n\"" \
    >> /tmp/bot-output/all.txt
done
grep -nF '${' /tmp/bot-output/all.txt        # literal ${...} interpolation failure
grep -nP '\\!' /tmp/bot-output/all.txt       # backslash-bang corruption
grep -nP '\\`' /tmp/bot-output/all.txt       # backslash-backtick corruption
grep -nE 'blob/main/.*#L[0-9]' /tmp/bot-output/all.txt  # un-pinned line links
grep -nF 'anthropics/' /tmp/bot-output/all.txt         # wrong-owner URL

Cover all four bot-output surfaces: issue comments, issue bodies, PR bodies, and reviews/inline review comments. Comments-only scans miss corruption that ships in a survey-issue or PR body.

Report format — return a structured summary:

## Runs with no bot output (skipped)
- <run-id>: <workflow> — <reason> (e.g., "no artifacts", "notification no-op")

## Runs with accepted output
- <run-id>: <workflow> on PR #N — bot reviewed, PR merged

## Runs with concerning output
- <run-id>: <workflow> on PR #N — <signal> (e.g., "human posted CHANGES_REQUESTED")

## Sanity check
<note if zero bot activity found across all runs — may indicate systemic failure>

Review the subagent's summary. If all outputs are accepted and no sanity-check flags, skip to Step 6 (summary). If concerning outcomes exist, continue to Step 3.

Step 3: Investigate concerning outcomes via cheap subagent

For runs with negative outcome signals (or suspicious lack of output), spawn another cheap subagent to download and inspect the specific session logs.

Use a cheap subagent (e.g. Haiku / gpt-mini) and a prompt like:

Investigate session logs for run on $ARGUMENTS.

Download: gh run download <run-id> -R $ARGUMENTS --pattern 'claude-session-logs*' --pattern 'codex-session-logs*' --dir /tmp/session-logs/<run-id>/ (both patterns are passed because the artifact prefix depends on the target repo's harness — Claude uploads claude-session-logs*, Codex uploads codex-session-logs*)

The concerning outcome was: <signal from Step 2>.

JSONL parsing — each line has a type field (user, assistant, system). Key queries:

# Tool calls in order
jq -r 'select(.type == "assistant") | .message.content[]? | select(.type == "tool_use") | "\(.name): \(.input | tostring | .[0:120])"' FILE
# Assistant reasoning
jq -r 'select(.type == "assistant") | .message.content[]? | select(.type == "text") | .text' FILE
# Bash commands executed
jq -r 'select(.type == "assistant") | .message.content[]? | select(.type == "tool_use" and .name == "Bash") | .input.command' FILE

Focus narrowly: what decision did the bot make that led to this bad outcome? Trace the decision chain in the JSONL for the specific problematic action. Don't parse the entire session. CI polling (sleep loops checking gh pr checks) in session logs is expected bot behavior — do not flag it.

Report: what the bot decided, what evidence it used, and what went wrong.

Evaluate the subagent's diagnosis against the repo-specific guidance from Step 1. Determine whether the failure is structural (same conditions always produce this failure) or stochastic (probabilistic model behavior that might not recur).

Step 4: Deduplicate

Before creating issues or PRs, check exhaustively for existing ones:

gh issue list --state open --label claude-behavior --json number,title,body
gh issue list --state open --json number,title,body  # also check unlabeled issues
gh pr list --state open --json number,title,headRefName,body
gh issue list --state closed --label claude-behavior --json number,title,closedAt --limit 30

Search titles AND bodies for related keywords. Only comment on existing issues if you have material new cases that would change the approach or increase prioritization. Do not comment with progress updates, fix-PR status, or re-statements of evidence already in the issue.

Step 5: Act on findings

Prefer PRs over issues. A PR with a clear description is immediately actionable.

• PR (default): Branch hourly/review-$GITHUB_RUN_ID, fix, commit, push, create with label claude-behavior. Put full analysis in PR description (run ID, outcome evidence, root cause, gate assessment including historical evidence count). Don't also create a separate issue.
• Issue (fallback): Only for problems too large or ambiguous to fix directly. Include run ID, outcome evidence, root cause analysis.

Group multiple findings by broad theme. Limit to at most 2 PRs per run — if you have more findings, pick the highest-confidence ones and record the rest in the evidence gist.

PR/issue bodies should link to the evidence gist ($GIST_URL) so reviewers can see the accumulated history behind the finding.

Do not poll CI after creating a PR. The tend-review and tend-ci-fix workflows handle PRs independently. Exit after pushing and creating the PR.

Step 6: Summary

Report results in the conversation log and save a markdown summary to /tmp/claude/step-summary.md (a post-Claude step copies this into the GitHub Actions step summary). Use the Write tool. Include $GIST_URL at the top so maintainers viewing the run page can click through to the full evidence log:

mkdir -p /tmp/claude
# Then use the Write tool to author /tmp/claude/step-summary.md, starting:
#
#   ## Review-reviewers summary
#
#   Evidence: <value of $GIST_URL>
#
#   ...

If no problems found (or none passed the gates), report "all clear" with: runs analyzed, outcomes checked, brief quality assessment, and a link to the evidence gist for any below-threshold findings recorded this run.