Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/hyh0620/mcp-knowledge-service/setup-environmentnpx skills add hyh0620/mcp-knowledge-service --skill setup-environmentgit clone --depth 1 https://github.com/hyh0620/mcp-knowledge-serviceWrote 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/hyh0620/mcp-knowledge-service/setup-environment)<a href="https://agentmods.dev/skills/hyh0620/mcp-knowledge-service/setup-environment"><img src="https://agentmods.dev/badge/skills/hyh0620/mcp-knowledge-service/setup-environment.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.00221 |
| Opus 5 | $0.00008 | $0.00111 |
| Sonnet 5 | $0.00003 | $0.00044 |
| Haiku 4.5 | $0.00002 | $0.00022 |
Grade A, and why
setup-environment 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 5d 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
Setup Environment
Pipeline
- Check Python:
python3.11 --version - Create or reuse
.venv:python3.11 -m venv .venv source .venv/bin/activate - Install:
pip install -e '.[dev]' - Create local config:
test -f .env || cp .env.example .env test -f config/settings.yaml || cp config/settings.example.yaml config/settings.yaml - Run:
.venv/bin/python -m pip check
Output
- Local
.venv. - Ignored local
.env. - Ignored local
config/settings.yaml.
Failure Handling
- Do not print API keys.
- If provider credentials are missing, stop before ingestion or real query verification.
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.
- 5d ago First seen · 43 lines · 16 tokens per session scan A 6a80a6049a37
setup-environment is a skill published in the GitHub repository hyh0620/mcp-knowledge-service (0 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 221 once invoked, about $0.0001 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 2026-09-01.
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Retrieve knowledge from a vector-store collection via the vector-mcp MCP server's vectorsearch tool — semantic (ANN) search, lexical BM25 search, or a hybrid of the two fused with Reciprocal Rank Fusion. Use when the agent must answer a question from an indexed corpus, pull top-k relevant chunks for RAG context, or…
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