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 skills add cajasmota/grafel --skill grafel-graph-qualitygit clone --depth 1 https://github.com/cajasmota/grafelWrote 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/cajasmota/grafel/grafel-graph-quality)<a href="https://agentmods.dev/skills/cajasmota/grafel/grafel-graph-quality"><img src="https://agentmods.dev/badge/skills/cajasmota/grafel/grafel-graph-quality/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/cajasmota/grafel/grafel-graph-quality"><img src="https://agentmods.dev/badge/skills/cajasmota/grafel/grafel-graph-quality.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 | $0.00069 | $0.04837 |
| Opus 5 | $0.00034 | $0.02419 |
| Sonnet 5 | $0.00014 | $0.00967 |
| Haiku 4.5 | $0.00007 | $0.00484 |
Grade B, and why
grafel-graph-quality scanned grade B with 1 finding 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 10d 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.
Reads MCP configurationmediumAgent snooping
mcp.json carries server URLs and auth tokens; reading it lets a mod discover and abuse other integrations.
- **Phase 3 subagent** — spawned after Phase 2 completes, also with only `questions.json` in scope. Must not receive Phase 2's transcript, tool-call log, or any side-channel that reveals which files or entities the grep How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
grafel-graph-quality
Pre-docgen MCP quality benchmark. The skill answers a single question with data: does grafel MCP deliver value over grep+read on this group? It produces a shareable markdown report the user passes back to the grafel coordinator for tuning.
When to use this skill
Invoke when the user asks for any of:
- "Benchmark grafel on this group."
- "Is the MCP actually saving tokens here?"
- "Quality-check before we run /generate-docs."
- "Run a regression against the last benchmark."
- "/grafel-quality-check" (slash command).
- "Run as a CI gate after indexing — /grafel-graph-quality --since -- benchmarks only entities changed since that commit."
Do not invoke for one-off lookups, ad-hoc grep substitution, or to "test the daemon" - the daemon health-check is a separate concern.
MCP + grep, paired
grafel MCP gives you a navigable, accurate map of the code; grep gives you raw pattern matches.
Use MCP for structural questions: who calls X? what is the flow? where does Y live in the graph?
Use grep for raw enumeration: every if err != nil, every import line, every TODO.
Pair them: MCP narrows the search space; grep verifies edge-property questions MCP can't answer yet.
This benchmark exists precisely to measure whether grafel MCP earns its place in that pairing: if it does not outperform grep+read on structural questions, the pair is unbalanced and docgen will burn tokens for no quality gain. Phases 2 and 3 operationalise the pairing by running each side independently then comparing outcomes.
Why this skill exists
A predecessor MCP tool ("Tool A") was empirically found to consume 3-6× more tokens than grep+read on representative questions, while not improving answer quality. This skill is the gate that confirms grafel does not have the same failure mode. It is the formal step between "we built grafel" and "we have evidence grafel helps."
It runs before /generate-docs because docgen amplifies whatever bias the MCP has - if the MCP is slower and less accurate than grep on this group, docgen will burn tokens producing worse documentation than a grep-only pipeline would.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- prompts/01-question-generation.md 12 KB
- prompts/02-without-mcp-run.md 4.1 KB
- prompts/03-with-mcp-run.md 6.0 KB
- prompts/03a-rpc-capture.md 3.3 KB
- prompts/04-quality-judgment.md 3.1 KB
- prompts/05-report.md 9.1 KB
- prompts/06-extraction-calibration.md 5.9 KB
- prompts/07-delta-mode.md 4.3 KB
- schema/with-mcp-artifact.schema.json 11 KB
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.
- 10d ago First seen · 251 lines · 69 tokens per session scan B f3a4bd261e49
grafel-graph-quality is a skill published in the GitHub repository cajasmota/grafel (15 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 4,837 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (reads mcp configuration). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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