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/dual-level-retrieval/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/dual-level-retrieval)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/dual-level-retrieval"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/dual-level-retrieval/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/dual-level-retrieval"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/dual-level-retrieval.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 | $0.00044 | $0.00385 |
| Opus 5 | $0.00022 | $0.00192 |
| Sonnet 5 | $0.00009 | $0.00077 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
dual-level-retrieval 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
Dual-Level Retrieval
Retrieve twice — once coarsely over the whole corpus to locate candidates, once precisely inside them to read evidence — so breadth never comes at the cost of quoting accurately.
Steps
- Run the coarse pass over the entire corpus using cheap, recall-oriented queries: names, symbols, and identifiers rather than concepts. Optimize for missing nothing, and accept false positives.
- Record the coarse hit set as a list of locations before reading any of them. This list is the denominator for the fine pass.
- Rank the hit set by expected decisiveness, not by match count — the file that defines a behavior outranks the ten that mention it.
- Run the fine pass by reading the ranked candidates in full context, wide enough to capture the enclosing definition and its callers.
- Quote from the fine pass only. Any claim traced to a coarse-pass snippet is unverified, because a grep line is not a reading of the code.
- Feed fine-pass discoveries back into a new coarse query when they reveal vocabulary you did not know to search for, and iterate until a coarse pass adds no new decisive locations.
- Report both levels: how many locations the coarse pass found, how many the fine pass read, and what was deliberately left unread.
Acceptance
- Coarse queries and their exact hit counts are recorded.
- Every quoted line comes from a file read in the fine pass.
- Candidate ranking is justified by decisiveness rather than frequency.
- At least one feedback iteration is attempted, or its absence is justified.
- The gap between locations found and locations read is stated explicitly.
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 · 26 lines · 44 tokens per session scan A 2f6190dda3fe
dual-level-retrieval is a skill published in the GitHub repository mtarcure/claude-vibe-squad (148 stars, last pushed 2d ago), licensed MIT. It adds 44 tokens to every session and 385 once invoked, about $0.0002 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.
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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.