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-feedbackgit 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-feedback)<a href="https://agentmods.dev/skills/cajasmota/grafel/grafel-feedback"><img src="https://agentmods.dev/badge/skills/cajasmota/grafel/grafel-feedback/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-feedback"><img src="https://agentmods.dev/badge/skills/cajasmota/grafel/grafel-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 51 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00125 | $0.03335 |
| Opus 5 | $0.00063 | $0.01667 |
| Sonnet 5 | $0.00025 | $0.00667 |
| Haiku 4.5 | $0.00013 | $0.00333 |
Grade A, and why
grafel-feedback 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 3d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
grafel-feedback
Generate an anonymized quality report that you can share with grafel maintainers to help improve extractor coverage, resolver accuracy, and framework support — without revealing any source code, file paths, or identifier names.
Privacy promise
The report contains:
- Entity name hashes: per-report ephemeral salt (from
crypto/rand), 4-hex output (e.g.ent_a3f7,op_92c1). Salt is never persisted and never logged. - Path templates:
<go>/<seg-1>/<seg-2>.go— depth preserved, all segments replaced. - Count ranges: exact entity counts are bucketed (1-5, 6-20, 21-100, 100+).
- Structural labels only: kind names (
function,class), language names, and framework annotation names (@GetMapping,@Inject) are not hashed — they are public framework vocabulary and essential for maintainers to diagnose issues.
The report does not contain:
- Source code (zero lines of code).
- Real file paths (depth + extension only).
- Real identifier names (hashed to 4 hex).
- Any network requests (fully offline).
- Any automatic issue creation (you decide whether to share).
How to run
grafel feedback [--group <name>] [--out <path>] [--yes]
Flags:
--group <name>— which group to analyse (default: inferred from your current directory).--out <path>— where to write the report (default:~/.grafel/feedback/<group>-<timestamp>.md).--yes— skip the confirmation prompt (useful for CI or scripting).
Example:
grafel feedback --group my-service
The CLI will show you what will (and will not) be collected, then ask for confirmation before generating the report.
Phase: synthesize the interpretation (do this after grafel feedback completes)
The deterministic collector emits statistics but no interpretation — historically every
genuinely useful finding in a feedback report was prose a human wrote by staring at the tables.
This phase makes you, the agent, write that prose automatically. After the .md report is
generated, open it, read every section, and append a new ## Findings & Interpretation
section to the end of the same file.
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.
- 3d ago Changed 21a6fbb5fe39
- 9d ago First seen · 251 lines · 125 tokens per session scan A acecd7db5f5b
grafel-feedback is a skill published in the GitHub repository cajasmota/grafel (14 stars, last pushed today), licensed MIT. It adds 125 tokens to every session and 3,335 once invoked, about $0.0006 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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