minirag-pgvector-mcp: Skill for Claude Code

.claude/skills/eval/SKILL.md

eval is a skill for Claude Code from mck-s/minirag-pgvector-mcp. It costs 54 tokens per session (709 once invoked), scanned A, original, MIT.

An evaluation skill that measures how well a retrieval system finds expected documents or text sections.

In plain words
What is it for?
It is for running golden test sets and reporting hit@k, MRR, and recall@k before changes are merged.
Why use it?
It shows whether changes to search, document splitting, or ranking improve or harm results.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is mck-s/minirag-pgvector-mcp's own configuration. It tells Claude Code how to work on minirag-pgvector-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything minirag-pgvector-mcp configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/mck-s/minirag-pgvector-mcp/main/.claude/skills/eval/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/mck-s/minirag-pgvector-mcp

Made for: Claude Code.

Wrote 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.

agentmods badge for eval

README.md
[![agentmods](https://agentmods.dev/badge/skills/mck-s/minirag-pgvector-mcp/eval/github.svg)](https://agentmods.dev/skills/mck-s/minirag-pgvector-mcp/eval)
Your own site
<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.

agentmods 80×15 button for eval

Your own site · 80×15
<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>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 709 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2mo ago against content hash c95de67ca95c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.claude/skills/eval/SKILL.md · 61 lines

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 — a golden/*.yaml file 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

  1. Load the golden set.
  2. For each case, call the actual tool (the MCP tool definition, not a private copy) with its query/doctype/k.
  3. 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.
  4. Aggregate across cases per tool.
  5. Write packages/eval/results/<date>-<tool>.md: per-case table (query, expected, returned, pass/fail, rank) + aggregate metrics.
  6. Persist to eval_runs for 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.

Read the full file on GitHub · 61 lines

Changes

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.

  1. 2mo ago First seen · 61 lines · 54 tokens per session scan A c95de67ca95c

Subscribe to this mod's changes

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.

Related

eval compared with skills from other repositories

eval next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
evalmck-s/minirag-pgvector-mcpSkill03mo agoMITA54Claude Code
imagegenopenai/codexSkill133kyesterdayApache-2.0A113Codex
playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143kyesterdayMITA103Claude Code