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/clarifynpx skills add qwerfunch/cladding --skill clarifygit 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 | $0.00057 | $0.00347 |
| Opus 5 | $0.00028 | $0.00173 |
| Sonnet 5 | $0.00011 | $0.00069 |
| Haiku 4.5 | $0.00006 | $0.00035 |
Grade A, and why
clarify 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 clarify
Use this workflow only for an answer contained in a new user message after Cladding displayed the pending question. Never infer, synthesize, or reuse an answer from an initialization turn. If no new user answer exists, stop without calling either clarify tool.
Use this workflow only after Cladding initialization returned a pending question and the user has answered it.
- Call
clad_prepare_clarifywith the user's answer verbatim. - Read the returned prompt, current state, and artifacts. Draft the structured refinement required by
clad_clarifyusing the current host model. - Call
clad_clarifywith the same answer, the one-time token, and the draft. - Ask
nextQuestionverbatim when one remains. - If the result is
needs_review, show the proposal diffs for everypendingReviewtarget and wait for explicit user approval. Only then callclad_resolve_onboarding_reviewwith the approved targets. Never describe onboarding as complete while review remains. - Report completion only when the returned status is
done.
Do not run clad clarify in a shell from an AI host. Do not use MCP sampling. Never answer a product question on the user's behalf. A stale, malformed, replayed, or answer-mismatched apply request is a no-op and must be prepared again.
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 · 19 lines · 57 tokens per session scan A 5d08bbb82125
clarify is a skill published in the GitHub repository qwerfunch/cladding (14 stars, last pushed 3d ago), licensed MIT. It adds 57 tokens to every session and 347 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.