Borrowing it
Nothing to install: this file belongs to mtarcure/claude-vibe-squad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mtarcure/claude-vibe-squad/main/.agents/skills/layered-analysis-loop/SKILL.mdgit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote 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/mtarcure/claude-vibe-squad/layered-analysis-loop)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/layered-analysis-loop"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/layered-analysis-loop/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/mtarcure/claude-vibe-squad/layered-analysis-loop"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/layered-analysis-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.00414 |
| Opus 5 | $0.00030 | $0.00207 |
| Sonnet 5 | $0.00012 | $0.00083 |
| Haiku 4.5 | $0.00006 | $0.00041 |
Grade A, and why
layered-analysis-loop 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 9d 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.
What it actually says
Layered Analysis Loop
Analyze in deliberate passes that each answer one question, so depth accumulates on a stable base instead of one undirected read producing a shallow summary.
Steps
- Write the question each layer will answer before starting it. A layer without a question becomes a re-read.
- Run layer 1 for structure only: what exists, how it is organized, where the entry points are. Resist diagnosing anything yet.
- Run layer 2 for behavior: follow the primary paths end to end and record what actually happens, in order.
- Run layer 3 for edges: error paths, empty and boundary inputs, concurrency, and the states the primary path assumes but does not enforce.
- Run layer 4 for contradiction: actively try to falsify the model built by layers 1-3. Look for the case that breaks it rather than the case that confirms it.
- Close each layer with a written delta — what changed in the model — and carry forward only findings with evidence. An unrecorded layer did not happen.
- Stop when a full layer produces no delta, and say so. Continuing past convergence spends budget; stopping before it ships an untested model.
- Report the layers run, the delta from each, and the layer at which convergence occurred.
Acceptance
- Each layer has a written question fixed before the layer ran.
- Structure, behavior, edge, and contradiction layers are separately recorded.
- Every layer closes with an explicit delta, including "no change".
- The contradiction layer names at least one specific attempt to falsify the model.
- The stopping condition is stated as convergence or as an explicit budget cutoff.
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.
- 9d ago First seen · 27 lines · 60 tokens per session scan A 1e26369e0676
layered-analysis-loop is a skill published in the GitHub repository mtarcure/claude-vibe-squad (109 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 414 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
contributor-onboarding
Help a new contributor get productive on this checkout - inspect sync state against main, build, run the repository's exact verification gate, and produce a local what's-new digest. Never fetches, pulls, or modifies a dirty tree on its own. Explicit-only.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
skill-installer
Install, update, trust, or inspect Codewhale skills from GitHub or local skill folders. Use when the user asks for available skills or wants a community skill installed.