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/checknpx skills add qwerfunch/cladding --skill checkgit 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 | $0.00073 | $0.00529 |
| Opus 5 | $0.00036 | $0.00264 |
| Sonnet 5 | $0.00015 | $0.00106 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
check 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.
What it actually says
Cladding check
Run clad check from the project root. Runs the 15 Iron Law stages — Type / Lint / Drift / Commit / Arch / Secret / Unit / Coverage / Spec-conformance / Deliverable-smoke / Smoke / Performance / Visual / Audit / UAT — and aggregates the worst exit code.
0— every stage cleared or skipped clean.1— at least one stage actually failed (fix-required).2— every result is skip (no fail-required input on the project yet).
--strict promotes warn-severity drift findings to error, matching the CI / pre-publish gate. The Drift stage runs every active detector under src/stages/detectors/ — npm run build:plugin Phase D recounts them and writes the integer into each plugin manifest (e.g. plugins/claude-code/.claude-plugin/plugin.json), so the number is never hand-maintained.
--internal shows stage codes (stage_1.1) instead of business names (Type). Default is the business-name surface; the audit log keeps internal ids regardless.
clad check
clad check --strict
clad check --internal
Gate economy (tiers)
Pick the cheapest tier that answers your question — the full pre-push suite is expensive and grows with the project:
clad check --tier=pre-commit— drift / arch / secret only (spec-vs-code, no full unit suite). Use for fast inner-loop feedback while implementing.clad check --tier=pre-push --strict— the full gate (type / lint / unit / cov + drift). This is whatclad done <featureId>already runs, so do NOT run it separately right beforeclad done— one authoritative full gate per feature, not two. A GREEN run refreshesspec/attestation.yamlwith the running Cladding version, strict blocking mode, detector-catalog SHA-256, module hashes, and feature markers. Seedocs/feature-cycle.md§ Gate economy.
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 · 35 lines · 73 tokens per session scan A 8e9cf445c426
check is a skill published in the GitHub repository qwerfunch/cladding (14 stars, last pushed 3d ago), licensed MIT. It adds 73 tokens to every session and 529 once invoked, about $0.0004 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.