Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/qwerfunch/cladding/observabilitynpx skills add qwerfunch/cladding --skill observabilitygit clone --depth 1 https://github.com/qwerfunch/claddingWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00061 | $0.00827 |
| Opus 5 | $0.00030 | $0.00413 |
| Sonnet 5 | $0.00012 | $0.00165 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 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 doctorconsumesevents.log.jsonland groupssentinel_missevents 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 --jsonemits the stableDoctorReportshape 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 frompreferred_personaforbidden_patterns—AI_HINTS_FORBIDDEN_PATTERN(#27) shows up in the detector heatmap; track its rate as a leading indicator of AI hygienepreferred_patterns— purely informational here (no detector); use it for narrative context when the user asks why the heatmap shifts
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 51 lines · 61 tokens per session scan A 637fde18c012
observability is a skill published in the GitHub repository qwerfunch/cladding (14 stars, last pushed 3d ago), licensed MIT. It adds 61 tokens to every session and 827 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
map-plan
ARCHITECT phase - decompose complex tasks into atomic subtasks with research, spec, and branch-scoped plan artifacts under .map.
map-review
Interactive 4-section code review using monitor, predictor, and evaluator agents plus the user and maintainer role reviewers on current changes. Use when reviewing a diff, PR, or staged work before merge. Do NOT use to plan or implement; use map-plan or map-efficient.
map-debug
Structured MAP debugging via task-decomposer, actor, and monitor agents. Use when reproducing a bug, isolating a regression, or diagnosing an error with specialized agents — including failing or flaky tests (pytest AssertionError), crashes and segmentation faults, memory-corruption or memory errors in native/C…
map-learn
Capture reusable lessons after a completed MAP workflow. Use when a MAP run has finished and you want rules written to .claude/rules/learned/ from a workflow summary or handoff. Do NOT use during active implementation.
map-efficient
State-machine MAP execution workflow for Codex. Use when implementing an approved MAP plan end to end, resuming from branch MAP taskplan or stepstate.json artifacts, or running non-trivial multi-subtask work. Use map-fast for tiny one-shot edits.
map-task
Execute a single subtask from an existing MAP plan via Actor and Monitor. Use when map-plan has decomposed work and you want fine-grained control over one subtask. Do NOT use without an existing plan; run map-plan first.