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 skills add qwerfunch/cladding --skill observabilitygit clone --depth 1 https://github.com/qwerfunch/claddingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/qwerfunch/cladding/observability)<a href="https://agentmods.dev/skills/qwerfunch/cladding/observability"><img src="https://agentmods.dev/badge/skills/qwerfunch/cladding/observability/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/qwerfunch/cladding/observability"><img src="https://agentmods.dev/badge/skills/qwerfunch/cladding/observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00061 | $0.00827 |
| Opus 5.5 | $0.00024 | $0.00331 |
| Sonnet 5.5 | $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 1mo ago.
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.
- 1mo ago Changed 90e24d5a2f7b
- 1mo ago First seen · 51 lines · 61 tokens per session scan A 637fde18c012
observability is a skill published in the GitHub repository qwerfunch/cladding (16 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 827 once invoked, about $0.0002 per session on Opus 5.5. 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
bug-fix-protocol
8-step disciplined bug-fix protocol that treats every production bug as two failures — the code defect itself and the testing system that allowed it through. Use when fixing a production bug, investigating a regression, writing a post-mortem, or auditing a missed defect. Triggers on "fix this bug", "production bug"…
vibedrift-drift-check
Use when writing or changing code in an existing repository to keep new code consistent with the repo's own conventions and avoid duplicating code that already exists. Checks a proposed function against the repo's dominant patterns and existing functions BEFORE it lands, turning drift detection into drift prevention.…
debugger
Systematic debugging method: 5-step root-cause analysis (capture, isolate, hypothesize, investigate, fix & verify) plus common bug-pattern reference. Use when errors, exceptions, test failures, or unexpected behavior appear. Loaded automatically by the debugger agent.
mcp-check
Validate MCP configuration and suggest improvements. Use when MCP servers fail to load, after editing .mcp.json, or when the user runs /mcp-check.
project-health-check
Audit project health across 7 dimensions (documentation, test coverage, security, code quality, dependencies, database, build/deploy) producing a scored report with prioritized fixes. Use for periodic checkups, before releases, or when the user runs /project-health-check.
debug
Systematic bug investigation with a five-phase framework: reproduce, narrow, diagnose, fix, verify. Supports automated regression bisect via --regression flag. Produces a structured debug report with root cause analysis, regression test, and CQ/Q self-evaluations.