Borrowing it
Nothing to install: this file belongs to mck-s/minirag-pgvector-mcp. 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/mck-s/minirag-pgvector-mcp/main/.claude/skills/eval/SKILL.mdgit clone --depth 1 https://github.com/mck-s/minirag-pgvector-mcpWrote 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/mck-s/minirag-pgvector-mcp/eval)<a href="https://agentmods.dev/skills/mck-s/minirag-pgvector-mcp/eval"><img src="https://agentmods.dev/badge/skills/mck-s/minirag-pgvector-mcp/eval/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/mck-s/minirag-pgvector-mcp/eval"><img src="https://agentmods.dev/badge/skills/mck-s/minirag-pgvector-mcp/eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00054 | $0.00709 |
| Opus 5.5 | $0.00022 | $0.00284 |
| Sonnet 5.5 | $0.00011 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
eval 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measure retrieval quality against a golden set, and produce a markdown accuracy report per tool. This is the skill that keeps you from over-trusting the system.
When to invoke
- After any change to chunking, embedder,
k, reranking, or tool logic. - Before merging anything that touches ingest/retrieve/mcp.
- On a schedule / in CI against a fixture corpus.
- Whenever you make a new real query worth capturing as ground truth → add it to the golden set.
Inputs
golden_set— agolden/*.yamlfile of cases:- query: "what did we decide about 振り替え scope?" doctype: meeting-notes # optional filter expected_sources: # ground truth - 2026-07-12-team-sync.md # or expected_chunk_ids: [...]tools— which tool(s) to score (search_context, …). Defaults to all.
Procedure
- Load the golden set.
- For each case, call the actual tool (the MCP tool definition, not a private copy) with its query/doctype/k.
- Score:
- hit@k — did an expected source/chunk appear in top-k?
- MRR — reciprocal rank of the first correct hit.
- recall@k — fraction of expected items found.
- Aggregate across cases per tool.
- Write
packages/eval/results/<date>-<tool>.md: per-case table (query, expected, returned, pass/fail, rank) + aggregate metrics. - Persist to
eval_runsfor trend history.
Two layers — keep them separate
- Retrieval eval (deterministic): the above. Fast, reproducible, no LLM. This is the primary gate.
- Answer eval (LLM-as-judge): given retrieved context, is the generated answer correct vs. a reference? Non-deterministic. Run separately, label clearly, never let it contaminate retrieval numbers.
Guardrails
- Test the real tool clients call, not a reimplementation — otherwise you're testing a fiction.
- Golden set is version-controlled data. Grow it; never delete a case to make numbers look better.
- A passing run is evidence, not proof — inspect a few per-case rows each time.
- When comparing changes (e.g. rerank on/off), change one variable and re-run; record the delta in the report.
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.
- 2mo ago First seen · 61 lines · 54 tokens per session scan A c95de67ca95c
eval is a skill published in the GitHub repository mck-s/minirag-pgvector-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 709 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
eval compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| evalmck-s/minirag-pgvector-mcp | Skill | 0 | 3mo ago | MIT | A | 54 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |