Agent skill

Skill Health

by aeonfun in 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.

MITAuto-check passedDevOps & Cloud

Install Skill Health

skills CLI
$ npx skills add aeonfun/aeon --skill skill-health -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aeonfun/aeon skill-health --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
skill-health
GitHub stars
770
Token cost
~7.8k tokens
SKILL.md length
2,739 words
Files
1
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 12 steps: Gather state → Classify each enabled skill → Detect systemic patterns → …
  • Tasks that involve Observability
  • SKILL.md covers Overview, Shared preamble (run for…, Data sources and Steps, plus 4 more sections
  • Calls gh and python3; needs GITHUB_TOKEN

What it does

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.

When your agent uses it

  • Tasks that involve Observability

Example prompts

  • “/skill-health”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Gather state
  2. Classify each enabled skill
  3. Detect systemic patterns
  4. Reconcile with memory/issues/
  5. Decide whether to notify
  6. Format the report
  7. Notify and log
  8. Determine the window
  9. Pull the run snapshot
  10. Cross-reference with cron schedule
  11. Cross-reference with cron-state.json
  12. Mine exit taxonomy from logs

What it can do on your machine

Read from SKILL.md and the folder at commit df013db. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • gh
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    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.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GITHUB_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~7.8k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from aeonfun/aeon at commit df013db, republished under its MIT licence (© aeonfun). 2,739 words, ~7,847 tokens.

Download SKILL.mdSave it as .claude/skills/skill-health/SKILL.md (or your agent's skills folder).
name
skill-health
description
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.
scorable
false
metadata.title
Skill Health
metadata.category
evolution
metadata.tags
meta

${var} — View selector.

  • empty → health check across all scheduled skills (default).
  • a skill slug (e.g. token-movers) → health check for that one skill.
  • analytics or metrics (optionally analytics: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. -->

Overview

This skill provides two views over the same GitHub-Actions skill-run data. They share a preamble but branch into distinct logic:

  • health (default): per-skill classification, issue filing/resolution against 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.
  • analytics (metrics): a fleet-wide ranked view — top runners, failure rates, exit-taxonomy distribution, silent-scheduled detection, and anomaly flags — with a significance-gated notification plus an article and a dashboard JSON spec. 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.

Shared preamble (run for either view)

  1. Read 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.
  2. Compute ${today} (UTC date, YYYY-MM-DD).
  3. Parse ${var} → selector (trim whitespace first):
    • empty → VIEW=health, TARGET=all (all scheduled skills).
    • lowercase first token is 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.
    • a bare positive integer (e.g. 168) → VIEW=analytics, WINDOW_HOURS = min(that, 720) (legacy skill-analytics shorthand).
    • anything else (a non-keyword, non-integer slug) → VIEW=health, TARGET=<that slug> (single-skill health check).
  4. Dispatch: if VIEW=health, run Health view; if VIEW=analytics, run Analytics view.

Health 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.

Data sources

  1. memory/cron-state.json — Per-skill quality metrics (as before).
  2. memory/skill-health/*.json — Per-skill quality analysis (Haiku post-run).
  3. memory/skill-health/last-report.json — Last run's classification snapshot (this skill writes it). Used to dedup notifications and detect flapping.
  4. aeon.yml — Enabled skills and schedules.
  5. memory/issues/INDEX.md and memory/issues/ISS-*.md — Open issues tracker. Check before filing, update on recovery.
  6. ./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.
  7. memory/logs/YYYY-MM-DD.md (last 3 days) — Grep for SKILL_*_ERROR or EMPTY signatures keyed to skills missing from skill-health/*.json.

Steps

1. Gather state
  • Parse aeon.yml → list of enabled skills with schedules. If TARGET is a single skill, filter to just that skill.
  • Load memory/cron-state.json (if missing or unparseable, treat as empty — first run, not failure).
  • Load every memory/skill-health/*.json (except last-report.json).
  • Load memory/skill-health/last-report.json if present → prev_report. If missing, prev_report = {}.
  • Run ./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).
  • Parse memory/issues/INDEX.md → extract open issues with detected_by: skill-health and their affected skills. If missing, treat as empty.
2. Classify each enabled skill

For each enabled skill, assign one status using the first matching rule:

StatusTrigger
CRITICALconsecutive_failures >= 3 OR (status==failed AND days_since_last_success >= 3)
DEGRADEDsuccess_rate < 0.6 OR (latest skill-health/*.json avg_score < 2.5 over ≥3 runs)
FLAPPING3+ status transitions (success↔failed) in last 7 days per cron-state history or skill-runs output
WARNINGsuccess_rate < 0.8 OR consecutive_failures >= 1
HEALTHYsuccess_rate >= 0.8 AND consecutive_failures == 0 AND (no skill-health data OR avg_score >= 3)
NO DATAno 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)
3. Detect systemic patterns

Group non-HEALTHY skills by shared api_host OR shared last_error signature. If ≥2 skills share one:

  • Emit a single SYSTEMIC: callout (e.g. SYSTEMIC: 3 skills failing on api.coingecko.com (rate_limit)).
  • Do not duplicate the same error across per-skill rows — reference the systemic line.
4. Reconcile with memory/issues/

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:

  • Matching issue exists, same root cause → do nothing (no new file, no notification for this skill).
  • Matching issue exists, different root cause → append a note to the existing ISS file's body: Update YYYY-MM-DD: new signature: <error>. Do not file a new issue.
  • No matching issue → file a new one (see below).

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:

  • Merged (mergedAt non-null) → the fix shipped. Set status: resolved, resolved_at: <mergedAt>, move the row from Open to Resolved in INDEX.md.
  • Closed without merging (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.
  • Still open → leave untouched. The repair is in flight.

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:

  • Skip status: fix-pending issues. Those wait for the reconcile step above; a lucky HEALTHY classification must not close a repair that has not merged.
  • Remove the skill from that issue's 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:

  1. Find next ID: scan memory/issues/ISS-*.md, take max NNN, add 1. Format as zero-padded 3 digits (ISS-042).
  2. Write memory/issues/ISS-NNN.md with YAML frontmatter:
    yaml
    ---
    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">
  3. Append a row to 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.

5. Decide whether to notify

Build a stable signature from the current classification: sorted list of CRITICAL+FLAPPING+DEGRADED skill names + SYSTEMIC callouts. SHA-256 it → current_hash.

  • If current_hash == prev_report.hash AND now - prev_report.last_notified_at < 24h → do not notify. State unchanged.
  • Otherwise → notify (there's new signal or the daily reminder cadence elapsed).

Always write memory/skill-health/last-report.json:

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": [...] }
}
6. Format the report

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:

  • Cap per-section rows at 5; collapse the rest as +N more — see memory/issues/INDEX.md.
  • Omit HEALTHY list (count only). Omit any empty section.
  • Always end with Open issues: X · Resolved this run: Y.
  • If NO CRITICAL/DEGRADED/FLAPPING and no new/resolved issues → body is just HEALTH: OK — N skills healthy.
7. Notify and log
  • If the gate in step 5 said notify → ./notify "<report body>". Update last_notified_at in last-report.json to now.
  • If gate said skip → do not call ./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.

Health-view constraints

  • Never file two open issues for the same (skill, root_cause) pair — always check INDEX.md first.
  • Never edit a Resolved issue. If a previously-resolved issue re-fires, file a new ISS with a pointer (related: ISS-NNN) in the body.
  • Do not notify on pure HEALTHY runs more than once per 24h.
  • If in single-skill mode (selector was a skill slug), skip INDEX.md updates only if the single skill is HEALTHY — otherwise file/resolve as normal.
  • Never touch memory/issues/INDEX.md Resolved section except to move rows into it; never delete rows.

Analytics view

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.

Show full SKILL.md (1,170 more words)Show less

Why this exists

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.

Steps

1. Determine the window
  • 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).
  • Compute WINDOW_LABEL (e.g. "last 7d" or "last 72h").
2. Pull the run snapshot
bash
./scripts/skill-runs --json --hours $WINDOW_HOURS > output/.chains/skill-analytics-runs.json 2>/dev/null

If the script fails (auth, rate limit, network error) or the JSON is empty:

  • Log 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):

json
{
  "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": [...]}
}
3. Cross-reference with cron schedule

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).

4. Cross-reference with cron-state.json

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.

5. Mine exit taxonomy from logs

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-success
  • SKIP_UNCHANGED → skip-unchanged (autoresearch-evolution exit)
  • NEW_INFO → new-info (autoresearch-evolution exit)
  • _SKIP* (other) → skip-other
  • _ERROR / _FAILED → error
  • _PARTIAL → partial
  • (no match) → uncategorized

Build 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.

6. Anomaly classification

For each skill in the snapshot OR silent_scheduled, assign at most one anomaly flag, first match wins:

FlagTrigger
🔴 SILENTsilent_scheduled: true (enabled cron skill, zero runs in window)
🔴 ALL_FAILtotal >= 2 AND failure == total
🟠 CONSECUTIVE_FAILURESconsecutive_failures >= 3 (from cron-state)
🟠 LOW_SUCCESStotal >= 3 AND success / total < 0.80
🟡 ALL_SKIPtotal >= 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_RUNStotal > 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.

7. Compute summary
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)"
8. Build the verdict line

Pick the strongest single claim, in priority:

  1. Any 🔴 SILENT exists → "${N} scheduled skill(s) didn't run this window — ${first_skill}"
  2. Any 🔴 ALL_FAIL exists → "${first_skill} failed every run (${N}/${N}) — investigate"
  3. Any 🟠 CONSECUTIVE_FAILURES exists → "${first_skill} on ${N}-run failure streak"
  4. Any 🟠 LOW_SUCCESS exists → "${first_skill} ${pct}% success over ${total} runs — degraded"
  5. Any 🟡 ALL_SKIP exists → "${N} skill(s) only emitting skip-class exits this window — verify intent"
  6. Otherwise → "All ${distinct_skills} active skills healthy — ${overall_success_pct}% success across ${total_runs} runs"
9. Significance gate

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.

10. Write the article

Path: output/articles/skill-analytics-${today}.md. Overwrite if it exists (idempotent same-day reruns).

markdown
# 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.*
11. Write the dashboard JSON spec

Path: apps/dashboard/outputs/skill-analytics.json. Use the catalog components (Card / Stack / Heading / Text / Badge / Table).

json
{
  "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:

json
{"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.

12. Send notification (only if gate from step 9 passed)

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}.md

Keep 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.)

13. Log to memory/logs/${today}.md

Log 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

Analytics-view exit taxonomy

StatusMeaningNotify?
SKILL_ANALYTICS_OKsnapshot fetched, ≥1 anomaly flaggedYes
SKILL_ANALYTICS_QUIETsnapshot fetched, zero anomaliesNo (article + JSON written, log only)
SKILL_ANALYTICS_NO_DATAskill-runs returned empty / fetch failedNo (log only, no article overwrite)

Analytics-view constraints

  • Significance-gated. A clean fleet must produce zero notifications. Article and JSON spec still write so the dashboard reflects the latest state, but ./notify is silent.
  • Never invent runs. If 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).
  • Best-effort exit-taxonomy parsing. Log markers are human-written; expect a 10–20% miss rate. Do not block the article on parse failures — drop the affected skill into uncategorized and continue.
  • Idempotent. Same-day reruns overwrite the article and JSON spec. The log entry is appended (one block per run, lets the operator see analytic drift across reruns).
  • No issue filing in this view. The analytics view does not write to memory/issues/ — that contract belongs to the health view. Anomalies surface here as flags; persistence and resolution live in the health view's domain.
  • Respect workflow_dispatch / reactive. Skills with non-cron schedules cannot be SILENT — they fire only on demand. Excluding them from the silent-scheduled check prevents permanent false positives.

Network note (both views)

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.

  • Health view: if ./scripts/skill-runs fails, log SKILL_HEALTH_PARTIAL — skill-runs unavailable and continue with cron-state only.
  • Analytics view: if 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

Files

Just SKILL.md in skills/skill-health of aeonfun/aeon.

Open the folder on GitHubat commit df013db

Compare with similar skills

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 Health compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Health this skillaeonfun/aeon770—~7.8kAutomated safety check: PassMIT
Vercel Optimize Auditvercel-labs/agent-skills32k8 repos~4.3kAutomated safety check: PassNone
Kubeshark Installerkubeshark/kubeshark12k—~3.6kAutomated safety check: NotesApache-2.0
Kubeshark KFL2 Filter Referencekubeshark/kubeshark12k—~3.6kAutomated safety check: PassApache-2.0
KubeSphere ServiceMesh Managerkubesphere/kubesphere17k—~2.4kAutomated safety check: PassCustom licence
Kubernetes Network Root Cause Analysiskubeshark/kubeshark12k—~5.3kAutomated safety check: PassApache-2.0

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Categories

Questions about Skill Health

What does Skill Health do?

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.

When should I use Skill Health?

Skill Health fits situations like: tasks that involve Observability.

How do I install Skill Health in Claude Code?

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.

How do I install Skill Health in Codex?

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.

Can I use Skill Health in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Skill Health need to run?

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.

Does Skill Health access the network?

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.

Is Skill Health safe to install?

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.

What licence does Skill Health use?

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.

How many tokens does Skill Health use?

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.

What are the alternatives to Skill Health?

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.

Who maintains Skill Health?

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.