Skill Health

${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).

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:

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.

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:

   ---
   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 timestamp>
   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:

{
  "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.

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

./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):

{
  "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:

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).

# 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).

{
  "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.

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

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.