epistemic-health-check — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited epistemic-health-check (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Register: DISCIPLINE Goal: Detect when previously valid knowledge is stale, underspecified, or unsafe to rely on. Core principle: evidence has a lifecycle: capture → verify → use → age → revalidate → retire.
Announce: "Using epistemic-health-check to detect stale knowledge before relying on old evidence."
python3 tools/epistemic_health_check.py .Cadence automation:
# At the end of each ADR/knowledge-compound cycle
python3 tools/epistemic_health_check.py . --record-cycle
# When performing the periodic health check or before C3/C4 release
python3 tools/epistemic_health_check.py . --record-run --strictThe script stores cadence state in docs/superskills/.epistemic-health-state.json by default.
Pass a project root containing docs/superskills/. If the KB does not exist, the script exits cleanly and reports that there is nothing to check.
Run this skill when any of these are true:
--record-cycle / --record-run;VOLATILITY: WATCHFUL or VOLATILITY: VOLATILE;last_seen or a retire_if condition;ASSUMED, T4, or README/install/spec claims;framework-doctor reports drift;[ ] ADR LAST CONFIRMED freshness vs VOLATILITY
[ ] unresolved ASSUMED/T4 claims
[ ] Gotchas with status/last_seen/retire_if fields
[ ] stale or missing QBR/M.AT calibration updates
[ ] stale docs/install/version references in framework mode
[ ] cadence due: cycles since last health check or days since last recorded runDefault freshness windows:
VOLATILE: 30 days
WATCHFUL: 90 days
STABLE: 180 days
UNKNOWN/no volatility: 120 daysEPISTEMIC HEALTH
Status: PASS | WARN | FAIL
Stale ADRs:
Stale Gotchas:
Unresolved assumptions:
Calibration issues:
Recommended action:A WARN does not always block work, but it must be represented in claim grammar as residual risk.
| Finding | Action |
|---|---|
| Stale C3/C4 ADR | Revalidate or downgrade confidence before release |
| Stale Gotcha | Mark WATCH/INACTIVE or refresh last_seen with evidence |
| Unresolved T4 assumption | Verify, explicitly carry as residual risk, or block if critical |
| Missing QBR calibration after 5+ cycles | Create/update qbr-calibration entry |
| Drift in framework docs | Run framework-doctor and fix before release |
Use claim grammar:
CLAIM: Historical knowledge is safe to rely on for this task.
EVIDENCE: T1 — ran `python3 tools/epistemic_health_check.py .` and reviewed findings.
SOURCE: command output.
SCOPE: Verifies staleness signals in docs/superskills and structured Gotchas; does not prove external reality unless sources were revalidated.
RESIDUAL RISK: List any WARN findings or assumptions carried forward.<!-- Populated by knowledge-compound after cycles where this skill underperformed --> <!-- New entries should follow shared/gotcha-schema.md -->
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.