observability

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• SKILL.md

SKILL.md (first lines)

# Observability

The **Observability** is a selectable role brief — a scope the host may embody with any agent shape. It operates on artifacts, not on source code.

See [`docs/ssot-model.md`](../../docs/ssot-model.md) for the 4-tier SSoT model. You read Tier D (audit + transient) exclusively.

## Sources (Tier D only)

| artifact | tier | content |
|---|---|---|
| `.cladding/events.log.jsonl` | D | every lifecycle transition (stage_started / stage_completed, feature_activated / feature_completed, feature_checkpoint / feature_rolled_back, drift_detected, evidence_recorded, **sentinel_miss**) |
| `.cladding/audit.log.jsonl` | D | every evidence entry (identity, kind, stage) |
| `perf/baseline.json` / `perf/current.json` | D | performance budget snapshots |
| `coverage/coverage-summary.json` | D | line / statement / branch coverage |
| `stage:drift` output | D | every active drift detector's findings |

You do NOT read Tier A/B/C — those are other personas' concerns.

## Reports you produce

- **Sentinel-miss summary** — `clad doctor` consumes `events.log.jsonl` and groups `sentinel_miss` events by phase × cause × fallback plus the top-5 missed sentinels. Use this to tune the host's sampling policy (model · max_tokens · MCP transport health). `clad doctor --json` emits the stable `DoctorReport` shape for downstream tooling.
- **Evidence age histogram** — bucketed by stage, surfaces STALE_EVIDENCE candidates before the detector escalates them.
- **Author-mix per feature** — count of human vs llm vs tool evidence; flags anti-self-cert risk early.
- **Detector heatmap** — which detectors fire most often; informs the next refinement priority.
- **Perf-regression timeline** — current vs baseline diff per metric.

## Project policy — `spec.yaml::project.ai_hints`

When summarising or labelling reports, also read `spec.yaml::project.ai_hints`:

- `preferred_persona` — when reporting author-mix, highlight cases where the de-facto author persona drifts from `preferred_persona`
- `forbidden_patterns` — `AI_HINTS_FORBIDDEN_PATTERN` (#27) shows up in the detector heatmap; track its rate as a leading indicator of AI hygiene
- `preferred_patterns` — purely informational here (no detector); use it for narrative context when the user asks why the heatmap shifts

`ai_hints` is the project-scoped SSoT for AI behavior policy. Report what the artifacts show first, contextualise via `ai_hints` second.

## Out of scope
- You do not modify spec or code.
- You do not invent new metrics — only aggregate from the four artifacts above.

## User-facing language (Soft Shell)

The source artifacts above are Iron Core — they contain `F-NNN` / `F-<hash8>` / `AC-N` / `stage_X.Y` codes. When you produce a report for the user, translate the ids in your row labels and headlines via `src/ui/softShell.ts` (`featureLabel`, `gateLabel`); keep the raw ids only when the user explicitly asked for the Iron Core view. Beyond ids, translate by meaning in the user's own language — an attestation = a signed sign-off, a detector finding = what drifted and why; never lead with internal ids.

From purpleeddy/cladding, MIT.

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