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/keyword-clustering/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/keyword-clustering)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/keyword-clustering"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/keyword-clustering/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/keyword-clustering"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/keyword-clustering.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 | $0.00053 | $0.00391 |
| Opus 5 | $0.00026 | $0.00196 |
| Sonnet 5 | $0.00011 | $0.00078 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
keyword-clustering 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 11d 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
Keyword Clustering
Group supplied or grounded search queries by intent and topic, and map each reproducible cluster to a page.
Required evidence table
Produce one row per normalized query with these fields: source, collection date, locale, device, raw query, normalized query, intent label, intent confidence, similarity/split-merge rule applied, cluster ID, target page, and exception rationale. Pin the similarity method and split/merge threshold before clustering; if judgment overrides that rule, the row's exception rationale makes the override reviewable.
Steps
- Record the query source, collection date, locale, and device; never invent query or volume evidence.
- Normalize each raw query with a stated rule while preserving the raw value in the table.
- Tag intent as informational / navigational / transactional / commercial, with a confidence value and an explicit multi-intent exception where one label would misrepresent the query.
- Apply the pinned similarity and split/merge rule so one cluster expresses one reviewable user need.
- Map each cluster to one target page/content piece and record why any mapping exception is necessary.
- Flag cannibalization wherever existing or proposed pages compete for the same cluster.
Acceptance
- Every query has complete source/date/locale/device provenance and an intent confidence.
- The normalization, similarity, and split/merge rules are fixed and replayable; exceptions carry reasons.
- Each cluster maps to exactly one page (no two pages target the same cluster).
- Queries/volumes are grounded, not fabricated, and the required evidence table accompanies the map.
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.
- 11d ago First seen · 32 lines · 53 tokens per session scan A 7631f8eae0f0
keyword-clustering is a skill published in the GitHub repository mtarcure/claude-vibe-squad (148 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 391 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
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
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-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.