Vercel Optimize Audit
vercel-labs/agent-skills
Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
Fleet skill observability with two views - health audits per-skill metrics and files/resolves issues in memory/issues/; analytics ranks the fleet by 7d runs, success rates, and anomaly flags.
$ npx skills add aeonfun/aeon --skill skill-health -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aeonfun/aeon skill-health --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-health .claude/skills/skill-health && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "skill-health" agent skill from https://github.com/aeonfun/aeon/tree/main/skills/skill-health into .claude/skills/skill-health/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-health", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aeonfun/aeon/tree/main/skills/skill-healthType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aeonfun/aeon --skill skill-health -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aeonfun/aeon skill-health --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/skill-health .agents/skills/skill-health && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "skill-health" agent skill from https://github.com/aeonfun/aeon/tree/main/skills/skill-health into .agents/skills/skill-health/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-health", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aeonfun/aeon --skill skill-health -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aeonfun/aeon skill-health --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/skill-health .cursor/skills/skill-health && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "skill-health" agent skill from https://github.com/aeonfun/aeon/tree/main/skills/skill-health into .cursor/skills/skill-health/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-health", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aeonfun/aeon.git --path skills/skill-health--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aeonfun/aeon --skill skill-health -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aeonfun/aeon skill-health --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/skill-health .gemini/skills/skill-health && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "skill-health" agent skill from https://github.com/aeonfun/aeon/tree/main/skills/skill-health into .gemini/skills/skill-health/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-health", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aeonfun/aeon skill-healthInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aeonfun/aeon --skill skill-health -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/skill-health .github/skills/skill-health && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "skill-health" agent skill from https://github.com/aeonfun/aeon/tree/main/skills/skill-health into .github/skills/skill-health/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-health", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aeonfun/aeon --skill skill-health -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aeonfun/aeon skill-health --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/skill-health .opencode/skills/skill-health && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "skill-health" agent skill from https://github.com/aeonfun/aeon/tree/main/skills/skill-health into .opencode/skills/skill-health/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "skill-health", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
skill-healthFleet skill observability with two views - health audits per-skill metrics and files/resolves issues in memory/issues/; analytics ranks the fleet by 7d runs, success rates, and anomaly flags.
Skill Health is an agent skill from aeonfun/aeon. Fleet skill observability with two views - health audits per-skill metrics and files/resolves issues in memory/issues/; analytics ranks the fleet by 7d runs, success rates, and anomaly flags.
Its SKILL.md is about 7.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Observability. The repository describes itself as: The most autonomous AI agent framework: runs unattended on GitHub Actions, self-healing skills, drives Claude Code, Grok, Codex & more. No approval loops. Configure once, forget… The licence is MIT.
12 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit df013db. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
ghpython3From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
GITHUB_TOKENFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Skill Health loads about 7.8k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 2,739 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from aeonfun/aeon at commit df013db, republished under its MIT licence (© aeonfun). 2,739 words, ~7,847 tokens.
.claude/skills/skill-health/SKILL.md (or your agent's skills folder).${var} — View selector.
- empty → health check across all scheduled skills (default).
- a skill slug (e.g.
token-movers) → health check for that one skill.analyticsormetrics(optionallyanalytics:HOURS, e.g.metrics:72) → fleet metrics view over the last HOURS (default 168 = 7d, cap 720).- a bare integer (e.g.
168) → metrics view with that window in hours (legacy shorthand).
<!-- autoresearch: variation C — more robust: memory/issues integration per CLAUDE.md health-skill contract, state-change-gated notifications, graceful missing-data; folds in B's TL;DR+action-directives+top-5 and A's skill-runs fallback. Analytics view absorbed from skill-analytics: ranked fleet view, exit-taxonomy distribution, significance-gated notify + article + dashboard JSON. -->
This skill provides two views over the same GitHub-Actions skill-run data. They share a preamble but branch into distinct logic:
memory/issues/, and a state-change-gated notification. This is the load-bearing self-healing view — its issue contract, memory/skill-health/ scoring, and ### skill-health log shape are depended on by the health loop and other skills. Do not weaken it.heartbeat gives binary ok/not-ok per run and the health view audits skills one degradation-band at a time; the analytics view is the only place the operator sees the entire fleet ranked side-by-side.memory/MEMORY.md for high-level context and scan the last ~3 days of memory/logs/ for recent activity — drop anything already reported so you don't re-report the same signal.${today} (UTC date, YYYY-MM-DD).${var} → selector (trim whitespace first):VIEW=health, TARGET=all (all scheduled skills).analytics or metrics → VIEW=analytics. Parse an optional window argument after a : or a space (analytics:72, metrics 336): if it is a positive integer, WINDOW_HOURS = min(that, 720); otherwise WINDOW_HOURS = 168.168) → VIEW=analytics, WINDOW_HOURS = min(that, 720) (legacy skill-analytics shorthand).VIEW=health, TARGET=<that slug> (single-skill health check).VIEW=health, run Health view; if VIEW=analytics, run Analytics view.VIEW=health. Audit skill quality metrics, detect API degradation, file issues for new failures and resolve them when skills recover, and notify only when fleet health state actually changes. If TARGET is a single skill slug, only check that skill.
memory/cron-state.json — Per-skill quality metrics (as before).memory/skill-health/*.json — Per-skill quality analysis (Haiku post-run).memory/skill-health/last-report.json — Last run's classification snapshot (this skill writes it). Used to dedup notifications and detect flapping.aeon.yml — Enabled skills and schedules.memory/issues/INDEX.md and memory/issues/ISS-*.md — Open issues tracker. Check before filing, update on recovery../scripts/skill-runs --hours 168 --failures --json — Fallback source for failures that never wrote to cron-state (runs that crashed before writing, etc.). Run once, parse JSON.memory/logs/YYYY-MM-DD.md (last 3 days) — Grep for SKILL_*_ERROR or EMPTY signatures keyed to skills missing from skill-health/*.json.aeon.yml → list of enabled skills with schedules. If TARGET is a single skill, filter to just that skill.memory/cron-state.json (if missing or unparseable, treat as empty — first run, not failure).memory/skill-health/*.json (except last-report.json).memory/skill-health/last-report.json if present → prev_report. If missing, prev_report = {}../scripts/skill-runs --hours 168 --failures --json 2>/dev/null || echo '{}' → extract any skill with failures in the last 7d that isn't in cron-state (runs that failed before writing state).memory/issues/INDEX.md → extract open issues with detected_by: skill-health and their affected skills. If missing, treat as empty.For each enabled skill, assign one status using the first matching rule:
| Status | Trigger |
|---|---|
| CRITICAL | consecutive_failures >= 3 OR (status==failed AND days_since_last_success >= 3) |
| DEGRADED | success_rate < 0.6 OR (latest skill-health/*.json avg_score < 2.5 over ≥3 runs) |
| FLAPPING | 3+ status transitions (success↔failed) in last 7 days per cron-state history or skill-runs output |
| WARNING | success_rate < 0.8 OR consecutive_failures >= 1 |
| HEALTHY | success_rate >= 0.8 AND consecutive_failures == 0 AND (no skill-health data OR avg_score >= 3) |
| NO DATA | no entry in cron-state AND never seen in skill-runs |
Compute severity score for sorting: consecutive_failures × (1 + days_since_last_success/7). Ties broken by days_since_last_success desc.
For each CRITICAL/DEGRADED/FLAPPING skill, record:
last_error (from cron-state or nearest log signature)api_host if the error clearly names one (e.g. api.coingecko.com, api.github.com)suggested_action — one of: FIX CONFIG (missing secret, bad arg), WAIT-API (rate limit, 5xx, timeout on third-party host), INVESTIGATE (unrecognised error), DISPATCH-SKILL (NO DATA but scheduled — scheduler gap)Group non-HEALTHY skills by shared api_host OR shared last_error signature. If ≥2 skills share one:
SYSTEMIC: callout (e.g. SYSTEMIC: 3 skills failing on api.coingecko.com (rate_limit)).Precondition guard: only perform issue filing/resolution if memory/issues/INDEX.md already exists. If it is missing, the operator has not opted into the issue-tracker contract yet — log SKILL_HEALTH_ISSUE_TRACKER_MISSING to memory/logs/${today}.md, skip this entire step (and the reconciliation side of step 5), and continue with classification + notification only. Do not auto-create INDEX.md.
For each CRITICAL or FLAPPING skill, check if an issue with status: open or status: fix-pending already has this skill in affected_skills AND a matching root_cause signature:
Update YYYY-MM-DD: new signature: <error>. Do not file a new issue.Reconcile fix-pending issues first. skill-repair sets status: fix-pending with a fix_pr when it opens a repair PR - it cannot know whether that PR merges, so it deliberately does not claim resolved. This step closes that loop. For each issue with status: fix-pending and a non-null fix_pr, check the PR's real state (gh pr view <fix_pr> --json state,mergedAt) and reconcile:
mergedAt non-null) → the fix shipped. Set status: resolved, resolved_at: <mergedAt>, move the row from Open to Resolved in INDEX.md.state: CLOSED, mergedAt: null) → the fix did not ship. Set status: open, set fix_pr: null, and append Update <YYYY-MM-DD>: fix PR <url> was closed unmerged; issue reopened. to the body. The skill is still broken; leaving it fix-pending would hide that.If gh is unavailable or the lookup errors, leave the issue untouched and log it - never resolve on an unverified assumption.
For each skill now HEALTHY whose name appears in any status: open issue's affected_skills:
status: fix-pending issues. Those wait for the reconcile step above; a lucky HEALTHY classification must not close a repair that has not merged.affected_skills. If the list becomes empty, set status: resolved, set resolved_at: <now ISO>, and move the row from Open to Resolved in INDEX.md.For each skill in a status: open critical issue that is still DEGRADED or WARNING only because of historical metrics, pipe its cron-state JSON object to python3 scripts/skill_health_recovery.py '<detected_at>'. If it prints recovered, remove the skill from affected_skills and resolve an empty issue exactly as above. A successful run after detection proves that specific failure incident recovered even when lifetime success_rate remains low. Do not apply this shortcut to FLAPPING/high issues (one successful run does not prove flapping stopped) or to status: fix-pending issues (those wait for the reconcile step above). Invalid or missing state/timestamps print active and fail closed.
Filing a new issue:
memory/issues/ISS-*.md, take max NNN, add 1. Format as zero-padded 3 digits (ISS-042).memory/issues/ISS-NNN.md with YAML frontmatter:---
id: ISS-NNN
title: <skill> <concise failure>
status: open # open | fix-pending | resolved. fix-pending = repair PR open, unmerged. Health resolves fix-pending only after merge (or reopens if closed unmerged). HEALTHY recovery still resolves status: open issues with no pending PR.
severity: critical | high | medium | low # critical=CRITICAL status, high=FLAPPING, medium=DEGRADED
category: rate-limit | timeout | missing-secret | config | api-change | sandbox-limitation | unknown
detected_by: skill-health
detected_at: <ISO-8601 UTC, e.g. 2026-09-03T12:00:00Z> # must carry Z or a +00:00 offset; a naive stamp fails closed to active
affected_skills: [<skill>, ...] # may grow later
root_cause: <error signature, 1 line>
fix_pr: null
---
## What happened
<2-3 line summary>
## Signal
- consecutive_failures: N
- days_since_last_success: N
- last_error: "<error>"
- related skills: <list or "none">memory/issues/INDEX.md under Open: | ISS-NNN | title | severity | category | YYYY-MM-DD | skill-a, skill-b |.All issue writes must be atomic per file — never partial updates mid-run.
Build a stable signature from the current classification: sorted list of CRITICAL+FLAPPING+DEGRADED skill names + SYSTEMIC callouts. SHA-256 it → current_hash.
current_hash == prev_report.hash AND now - prev_report.last_notified_at < 24h → do not notify. State unchanged.Always write memory/skill-health/last-report.json:
{
"hash": "<current_hash>",
"last_notified_at": "<ISO if notified this run, else previous value>",
"last_run_at": "<ISO now>",
"classification": { "critical": [...], "degraded": [...], "flapping": [...], "warning": [...], "healthy_count": N, "no_data": [...] }
}Top line: HEALTH: OK | HEALTH: WARNING(W) | HEALTH: DEGRADED(D) | HEALTH: CRITICAL(C) — most severe wins.
Body (notify-channel format, max 1 message):
*Skill Health — ${today}*
HEALTH: CRITICAL(2) [systemic: api.coingecko.com rate_limit — 3 skills]
🔴 CRITICAL
- token-movers — 5 fails, 3d down — WAIT-API (rate_limit) → ISS-042
- defi-monitor — 4 fails, 2d down — WAIT-API (rate_limit) → ISS-042
🟡 DEGRADED / FLAPPING
- digest — 52% success (14d), avg quality 2.1 — INVESTIGATE → ISS-043
⚪ NO DATA (2): skill-x, skill-y — DISPATCH-SKILL
🟢 HEALTHY: 34
Open issues: 2 · Resolved this run: 1 (rss-digest)Rules for formatting:
+N more — see memory/issues/INDEX.md.Open issues: X · Resolved this run: Y.HEALTH: OK — N skills healthy../notify "<report body>". Update last_notified_at in last-report.json to now../notify. Log to memory/logs/${today}.md:### skill-health
- view: health
- SKILL_HEALTH_NOOP — state unchanged since <prev_run_at>, hash=<short>On notify, log to memory/logs/${today}.md:
### skill-health
- view: health
- HEALTH: <OK|WARNING|DEGRADED|CRITICAL>
- filed: [ISS-NNN, ...]
- resolved: [ISS-NNN, ...]
- open: N
- systemic: <pattern or none>If all skills healthy, the body-only shortcut from step 6 still fires (once per 24h, per gate) so the operator gets confirmation the audit actually ran — but suppress if last-report.json shows a notify <24h ago with the same OK hash.
(skill, root_cause) pair — always check INDEX.md first.related: ISS-NNN) in the body.memory/issues/INDEX.md Resolved section except to move rows into it; never delete rows.VIEW=analytics. Generate a fleet-level performance view of every Aeon skill that has run in the window. The point of this view is to answer four questions in one report: which skills run most, which fail most, which are silently skipping (new exit taxonomy from the autoresearch-evolution rewrites), and which scheduled skills haven't fired at all.
heartbeat runs daily and emits a per-skill ✓/✗. The health view (above) files issues for skills that breach degradation thresholds. Neither produces a ranked, fleet-wide view. The 80 autoresearch-evolution rewrites (aeon PRs #46–#136) introduced new exit taxonomies — SKIP_UNCHANGED, NEW_INFO, SKIP_QUIET — that classify quiet-but-correct runs separately from failures. Existing health checks treat any non-*_OK exit as worth attention; the analytics view makes the actual distribution visible so a skill running mostly SKIP_UNCHANGED reads as healthy-quiet, not silently broken.
WINDOW_HOURS was set by the selector parse (default 168 = 7 days; a positive integer from analytics:N, metrics N, or a bare integer; capped at 720 = 30 days — anything longer slows the gh api paginate).WINDOW_LABEL (e.g. "last 7d" or "last 72h")../scripts/skill-runs --json --hours $WINDOW_HOURS > output/.chains/skill-analytics-runs.json 2>/dev/nullIf the script fails (auth, rate limit, network error) or the JSON is empty:
SKILL_ANALYTICS_NO_DATA — skill-runs returned empty (gh api / network error?) to memory/logs/${today}.md (under the ### skill-health heading, see step 13) and stop with no notification. A silent fleet view is correct on data-fetch failure — fall back rather than guess.The script's JSON shape (see scripts/skill-runs):
{
"period": {"since": "...", "until": "...", "hours": 168},
"summary": {"total": N, "succeeded": N, "failed": N, "cancelled": N, "in_progress": N},
"skills": [{"skill": "name", "total": N, "success": N, "failure": N, "cancelled": N, "in_progress": N, "last_run": "...", "last_conclusion": "..."}],
"anomalies": {"duplicates": [...], "failing": [...]}
}Read aeon.yml and build SCHEDULED_SKILLS: dict {skill_name -> {enabled: bool, schedule: str}} for every entry under skills:. Treat schedule: "workflow_dispatch" and schedule: "reactive" as exempt from the "no runs in window" anomaly — those are dispatched on demand, not by cron.
For every skill in SCHEDULED_SKILLS where enabled: true AND schedule is a valid cron expression AND the skill is not present in the snapshot's skills array, mark silent_scheduled: true (zero runs in window despite an active schedule).
Load memory/cron-state.json if present (missing → empty dict, not failure). For each skill in the snapshot, attach:
consecutive_failures (0 if missing)last_status ("unknown" if missing)Used to compute the consecutive-failure anomaly without a second gh api round-trip.
For each daily log file memory/logs/YYYY-MM-DD.md whose date falls in the window, scan for these markers (one match per skill section):
_OK → success (excluding _OK_SILENT)_OK_SILENT / _QUIET / SKIP_QUIET → quiet-successSKIP_UNCHANGED → skip-unchanged (autoresearch-evolution exit)NEW_INFO → new-info (autoresearch-evolution exit)_SKIP* (other) → skip-other_ERROR / _FAILED → error_PARTIAL → partialBuild EXIT_DIST[skill] = {ok: N, quiet: N, skip_unchanged: N, new_info: N, skip_other: N, error: N, partial: N, uncategorized: N}. The dominant bucket per skill is the one with the largest count; ties broken in the order listed above. If a skill has no log markers in the window, dominant bucket is "uncategorized".
This step is best-effort — the markers are regex-grepped from human-written logs, not parsed from a contract. A miss-rate of 10–20% is expected and acceptable; the GitHub Actions success/failure counts from step 2 remain the ground truth for pass/fail. The taxonomy distribution is a secondary signal.
For each skill in the snapshot OR silent_scheduled, assign at most one anomaly flag, first match wins:
| Flag | Trigger |
|---|---|
🔴 SILENT | silent_scheduled: true (enabled cron skill, zero runs in window) |
🔴 ALL_FAIL | total >= 2 AND failure == total |
🟠 CONSECUTIVE_FAILURES | consecutive_failures >= 3 (from cron-state) |
🟠 LOW_SUCCESS | total >= 3 AND success / total < 0.80 |
🟡 ALL_SKIP | total >= 3 AND EXIT_DIST.ok + EXIT_DIST.quiet + EXIT_DIST.new_info == 0 AND EXIT_DIST.skip_unchanged + EXIT_DIST.skip_other > 0 (every run skipped — possibly correct, possibly stuck) |
🟡 DUPLICATE_RUNS | total > 2 × expected_runs(schedule, window) (more runs than the cron should produce — manual reruns or scheduler glitch) |
expected_runs(schedule, window) is a coarse estimate — for a cron "0 H * * *" over 7 days, expect 7; for "0 H,H,H * * *", expect 21; for weekly "0 H * * D", expect 1. If the schedule string is unparseable, skip the duplicate check for that skill (do not flag false positives).
A skill with no flag is considered HEALTHY for analytics purposes.
total_runs: sum of every skill's total
distinct_skills: count of skills with total >= 1
overall_success_pct: snapshot.summary.succeeded / (succeeded + failed) × 100 (cancelled + in_progress excluded)
anomaly_count: count of skills with any flag in step 6
silent_scheduled_count: count of SILENT flags
exit_dominant: top 3 dominant exit buckets across the fleet, e.g. "ok (42), skip_unchanged (18), error (3)"Pick the strongest single claim, in priority:
🔴 SILENT exists → "${N} scheduled skill(s) didn't run this window — ${first_skill}"🔴 ALL_FAIL exists → "${first_skill} failed every run (${N}/${N}) — investigate"🟠 CONSECUTIVE_FAILURES exists → "${first_skill} on ${N}-run failure streak"🟠 LOW_SUCCESS exists → "${first_skill} ${pct}% success over ${total} runs — degraded"🟡 ALL_SKIP exists → "${N} skill(s) only emitting skip-class exits this window — verify intent""All ${distinct_skills} active skills healthy — ${overall_success_pct}% success across ${total_runs} runs"Notify only if anomaly_count >= 1. Silent run = correct (no anomalies in fleet) = no notification. Following the autoresearch-evolution / fork-digest pattern: noisy skills break trust faster than missing pings.
If gate says skip, still write the article and JSON spec, and log SKILL_ANALYTICS_QUIET (no anomalies). The dashboard widget refreshes regardless; only the push notification is gated.
Path: output/articles/skill-analytics-${today}.md. Overwrite if it exists (idempotent same-day reruns).
# Skill Analytics — ${today}
**Verdict:** ${verdict_line}
*Window: ${WINDOW_LABEL} · ${total_runs} runs across ${distinct_skills} skills · ${overall_success_pct}% success · ${anomaly_count} anomalies*
## Anomalies
| Flag | Skill | Detail | Action |
|------|-------|--------|--------|
| 🔴 SILENT | name | scheduled `<cron>` but zero runs in window | run **aeon-doctor** — zero-runs-despite-schedule is usually a static config bug (unquoted `schedule:`, dup key) it can't be seen in run data |
| 🔴 ALL_FAIL | name | N/N failed | investigate root cause |
| 🟠 CONSECUTIVE_FAILURES | name | N-run streak (last_error: "...") | see health view for filed issue |
| 🟠 LOW_SUCCESS | name | N% over M runs | review failures |
| 🟡 ALL_SKIP | name | M runs, all skip-class | confirm SKIP_UNCHANGED is the intent |
| 🟡 DUPLICATE_RUNS | name | M runs, expected ~K | check for manual reruns |
(If `anomaly_count == 0`: write `No anomalies — fleet healthy across ${distinct_skills} skills.`)
## Top runners (by run count)
| # | Skill | Runs | Success | Last status | Dominant exit |
|---|-------|------|---------|-------------|---------------|
| 1 | name | N | XX% | success | ok |
| 2 | name | N | XX% | success | skip_unchanged |
...
(Top 15 by total runs desc. If fewer than 15 active skills, list all.)
## Failure rate (sorted, ≥1 failure)
| Skill | Runs | Failures | Success rate | Last conclusion |
|-------|------|----------|--------------|-----------------|
(All skills with `failure >= 1`, sorted by `failure / total` desc. If none: "Zero failures across ${distinct_skills} skills this window.")
## Exit taxonomy distribution
| Bucket | Count | % | Top skills |
|--------|-------|---|------------|
| ok | N | XX% | a, b, c |
| skip_unchanged | N | XX% | d, e |
| new_info | N | XX% | f |
| quiet | N | XX% | g |
| error | N | XX% | h |
| partial | N | XX% | |
| uncategorized | N | XX% | |
(Sourced from `memory/logs/*.md` — best-effort regex grep, see Step 5. Cell-aligns to summary cells above where available.)
## Silent scheduled skills (enabled, zero runs)
${list of {skill, schedule} pairs OR "none — every enabled cron skill ran at least once."}
> **Root-cause pointer:** an enabled cron skill with *zero* runs is most often a **static config** fault the run data can't reveal — an unquoted `schedule:` the scheduler regex silently skips, a duplicate `aeon.yml` key, or an entry pointing at a missing `SKILL.md`. That class is `aeon-doctor`'s job; dispatch it (`./aeon skills run aeon-doctor`) to pinpoint the cause instead of guessing at the scheduler.
## Source status
- skill-runs JSON: ${ok|empty|fetch_error}
- Window: ${WINDOW_HOURS}h (${period.since} → ${period.until})
- aeon.yml: ${ok|missing}
- cron-state.json: ${ok|missing — first run for this fork?}
- Daily logs scanned: ${N_LOG_FILES}/${expected_log_files} for exit taxonomy
---
*The analytics view of `skill-health` (per-skill issue filing lives in the health view) and a companion to `heartbeat` (per-run pulse). Fleet-wide observability is the gap this view closes. Methodology: GitHub Actions run history is ground truth for pass/fail; daily-log markers are best-effort secondary signal for exit taxonomy.*Path: apps/dashboard/outputs/skill-analytics.json. Use the catalog components (Card / Stack / Heading / Text / Badge / Table).
{
"version": "1",
"generated_at": "${ISO timestamp}",
"skill": "skill-analytics",
"title": "Skill Analytics — ${today}",
"spec": {
"type": "Stack",
"props": {"direction": "vertical", "gap": "md"},
"children": [
{"type": "Heading", "props": {"level": 2, "children": "Skill Analytics — ${today}"}},
{"type": "Text", "props": {"variant": "muted", "children": "${verdict_line}"}},
{"type": "Grid", "props": {"columns": 4, "gap": "sm"}, "children": [
{"type": "Card", "props": {"children": [
{"type": "Text", "props": {"variant": "muted", "children": "Total runs"}},
{"type": "Heading", "props": {"level": 3, "children": "${total_runs}"}}
]}},
{"type": "Card", "props": {"children": [
{"type": "Text", "props": {"variant": "muted", "children": "Active skills"}},
{"type": "Heading", "props": {"level": 3, "children": "${distinct_skills}"}}
]}},
{"type": "Card", "props": {"children": [
{"type": "Text", "props": {"variant": "muted", "children": "Success rate"}},
{"type": "Heading", "props": {"level": 3, "children": "${overall_success_pct}%"}}
]}},
{"type": "Card", "props": {"children": [
{"type": "Text", "props": {"variant": "muted", "children": "Anomalies"}},
{"type": "Heading", "props": {"level": 3, "children": "${anomaly_count}"}}
]}}
]},
{"type": "Heading", "props": {"level": 3, "children": "Top runners"}},
{"type": "Table", "props": {
"columns": [
{"key": "rank", "header": "#"},
{"key": "skill", "header": "Skill"},
{"key": "runs", "header": "Runs"},
{"key": "success", "header": "Success"},
{"key": "exit", "header": "Dominant exit"}
],
"rows": [
{"rank": "1", "skill": "name", "runs": "N", "success": "XX%", "exit": "ok"}
]
}}
]
}
}If anomaly_count >= 1, prepend an Alert block before the verdict:
{"type": "Alert", "props": {"variant": "destructive", "children": "${anomaly_count} anomaly flag(s) raised — see Anomalies section"}}If the file write fails (filesystem read-only, missing directory), log a warning but do not abort — the article is the canonical artifact, the JSON spec is a dashboard convenience.
Via ./notify:
*Skill Analytics — ${today}*
${verdict_line}
Window: ${WINDOW_LABEL} · ${total_runs} runs · ${distinct_skills} skills · ${overall_success_pct}% success
Anomalies: ${anomaly_count}
${If 🔴 flags (cap top 3):}
🔴 Critical:
- ${skill} — ${flag}: ${detail}
${If 🟠 flags (cap top 3):}
🟠 Degraded:
- ${skill} — ${flag}: ${detail}
${If 🟡 flags (top 3, only if no 🔴/🟠 already filled the slots):}
🟡 Watch:
- ${skill} — ${flag}: ${detail}
Top by runs: ${top_3_skills_by_run_count_with_counts}
Full: output/articles/skill-analytics-${today}.mdKeep the message body tight for signal. Drop the "Top by runs" line first if it runs long; flags are higher signal. (./notify auto-chunks, so length is about signal, not transport.)
memory/logs/${today}.mdLog under the shared ### skill-health heading (the health loop parses this shape), with a view: analytics discriminator:
### skill-health
- view: analytics
- **Window**: ${WINDOW_LABEL} (${WINDOW_HOURS}h)
- **Total runs**: ${total_runs} across ${distinct_skills} skills
- **Overall success rate**: ${overall_success_pct}%
- **Anomalies**: ${anomaly_count} (🔴 ${red_count}, 🟠 ${orange_count}, 🟡 ${yellow_count})
- **Silent scheduled**: ${silent_scheduled_count} skills (${comma list capped at 5})
- **Top runner**: ${top_skill} (${top_runs} runs)
- **Exit dominant**: ${exit_dominant_summary}
- **Verdict**: ${verdict_line}
- **Article**: output/articles/skill-analytics-${today}.md
- **Dashboard**: apps/dashboard/outputs/skill-analytics.json
- **Notification sent**: ${yes|no — quiet (no anomalies)}
- **Status**: SKILL_ANALYTICS_OK | SKILL_ANALYTICS_QUIET | SKILL_ANALYTICS_NO_DATA| Status | Meaning | Notify? |
|---|---|---|
SKILL_ANALYTICS_OK | snapshot fetched, ≥1 anomaly flagged | Yes |
SKILL_ANALYTICS_QUIET | snapshot fetched, zero anomalies | No (article + JSON written, log only) |
SKILL_ANALYTICS_NO_DATA | skill-runs returned empty / fetch failed | No (log only, no article overwrite) |
./notify is silent.skill-runs returns empty, exit SKILL_ANALYTICS_NO_DATA — do not synthesise data from cron-state alone (cron-state's view is per-skill, not chronologically ordered, and would produce a misleading "top runners" table).uncategorized and continue.memory/issues/ — that contract belongs to the health view. Anomalies surface here as flags; persistence and resolution live in the health view's domain.This skill fetches no URLs directly — all data is local or via gh / ./scripts/skill-runs (which uses gh api, so auth comes from GITHUB_TOKEN with no secret ever on the command line). No curl fallback needed.
./scripts/skill-runs fails, log SKILL_HEALTH_PARTIAL — skill-runs unavailable and continue with cron-state only.gh api is rate-limited or the runner's network is degraded, ./scripts/skill-runs exits non-zero; catch that and fall through to SKILL_ANALYTICS_NO_DATA rather than emitting a partial fleet view that would mislead.© aeonfun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/skill-health of aeonfun/aeon.
Open the folder on GitHubat commit df013db
Skill Health next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Skill Health this skillaeonfun/aeon | 770 | — | ~7.8k | Automated safety check: Pass | MIT | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 8 repos | ~4.3k | Automated safety check: Pass | None | |
| Kubeshark Installerkubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | |
| Kubeshark KFL2 Filter Referencekubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| KubeSphere ServiceMesh Managerkubesphere/kubesphere | 17k | — | ~2.4k | Automated safety check: Pass | Custom licence | |
| Kubernetes Network Root Cause Analysiskubeshark/kubeshark | 12k | — | ~5.3k | Automated safety check: Pass | Apache-2.0 |
vercel-labs/agent-skills
Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
kubeshark/kubeshark
Installs and configures Kubeshark on a Kubernetes cluster, choosing between the quick CLI path and a Helm install with custom values.
kubeshark/kubeshark
Syntax reference for KFL2, the CEL-based display filter language used to search Kubernetes network traffic captured by Kubeshark, loaded before any filter is written.
kubesphere/kubesphere
Installs, checks and troubleshoots the KubeSphere ServiceMesh extension (Istio, Kiali, Jaeger), including grayscale release, sidecar injection, topology and tracing issues.
kubeshark/kubeshark
Investigates past Kubernetes incidents from Kubeshark traffic snapshots: takes captures, dissects API calls, extracts PCAPs and compares traffic over time.
JuliusBrussee/caveman
Routes every LLM call in a repository through the Caveman Cloud gateway in record mode, so requests and costs are measured without changing behavior.
aeonfun/aeon
Browses open tasks on the TaskMarket agent-worker market and, with explicit operator approval, creates tasks, tracks submissions and submits finished work.
aeonfun/aeon
Sets up and manages an Aeon agent instance that runs skills on a schedule through GitHub Actions: starting, rescheduling, debugging, editing skills and mining chat history.
aeonfun/aeon
Reads a Base Account's address, portfolio and transaction history through the Base MCP server, and stays strictly read-only in unattended Aeon runs, reporting only changes.
aeonfun/aeon
Audits every page of a site each day from its sitemap, scores on-page and technical SEO, checks duplicates across pages and reports what changed since the last run.
aeonfun/aeon
5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates
aeonfun/aeon
Static linter for an Aeon instance's configuration that catches silent failures such as unquoted schedules, duplicate keys, unconfigured skills and broken MCP references.
Categories
Fleet skill observability with two views - health audits per-skill metrics and files/resolves issues in memory/issues/; analytics ranks the fleet by 7d runs, success rates, and anomaly flags. Skill Health is an agent skill from aeonfun/aeon. Fleet skill observability with two views - health audits per-skill metrics and files/resolves issues in memory/issues/; analytics ranks the fleet by 7d runs, success rates, and anomaly flags.
Skill Health fits situations like: tasks that involve Observability.
Run `npx skills add aeonfun/aeon --skill skill-health -a claude-code`. Or copy the skill folder (skills/skill-health in aeonfun/aeon) into .claude/skills/skill-health in your project. Claude Code loads it when a task matches its description.
Run `npx skills add aeonfun/aeon --skill skill-health -a codex`. Or copy the skill folder (skills/skill-health in aeonfun/aeon) into .agents/skills/skill-health in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aeonfun/aeon --skill skill-health -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-health, .gemini/skills/skill-health, .github/skills/skill-health and .opencode/skills/skill-health in your project.
Going by SKILL.md and its folder, Skill Health needs the command-line tools its instructions call (gh and python3) and credentials named GITHUB_TOKEN. Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Skill Health is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 7.8k tokens (SKILL.md is roughly 31k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Skill Health: Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars), Kubeshark Installer (kubeshark/kubeshark, 12k stars), Kubeshark KFL2 Filter Reference (kubeshark/kubeshark, 12k stars) and KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
aeonfun (a GitHub organization) maintains it in aeonfun/aeon, which has 770 GitHub stars. The repository holds 82 skills in this directory. The repository was last updated on October 10, 2026.
Source: aeonfun/aeon on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.