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 Goodeye-Labs/truesight-mcp-skills --skill eval-auditgit clone --depth 1 https://github.com/Goodeye-Labs/truesight-mcp-skillsWrote 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/goodeye-labs/truesight-mcp-skills/eval-audit)<a href="https://agentmods.dev/skills/goodeye-labs/truesight-mcp-skills/eval-audit"><img src="https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/eval-audit/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/goodeye-labs/truesight-mcp-skills/eval-audit"><img src="https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/eval-audit.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 | $0.00040 | $0.00585 |
| Opus 5 | $0.00020 | $0.00293 |
| Sonnet 5 | $0.00008 | $0.00117 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
eval-audit 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 11d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Audit
Audit LLM evaluation practice and route gaps to the right skills.
Interactive Q&A protocol (mandatory)
Ask one question at a time using the structured question tool (loaded per the HARD-GATE above).
Example question structure:
What should this audit prioritize first?
A) Live evaluation quality and coverage
B) Error analysis maturity
C) Review and promotion loop health
D) End-to-end process health
Rules:
- One question per message.
- Use the structured question tool for every question. Structure each with a short header, 2-4 options with labels and descriptions, and place the recommended option first. Do not add "(Recommended)" or similar annotations to option labels.
- Ask one follow-up only if ambiguity remains.
Inputs and evidence
Collect available evidence from Truesight first:
- datasets and dataset rows
- live evaluations
- evaluation runs/results
- review queue items
- existing evaluation criteria and deployment patterns
If evidence is missing, record that as a finding.
Diagnostic areas
- Evaluation coverage and quality dimensions
- Error analysis practice and category quality
- Review and promotion workflow discipline
- Template usage versus custom needs
- Operational hygiene (verification, reruns, iteration cadence)
Report format (mandatory)
For each finding, include:
### <Finding title>
Status: Problem exists | OK | Cannot determine
Evidence: <specific evidence from Truesight context>
Severity: critical | high | medium | low
Recommended skill: <one of current skill set>
Next command: <concrete instruction to run next>
Order findings by severity and impact.
Severity rubric
- critical: likely causes incorrect go/no-go decisions or severe user harm
- high: frequent quality failures or missing control loops
- medium: meaningful process weakness with moderate impact
- low: optimization opportunity, documentation, or ergonomics issue
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.
- 11d ago First seen · 84 lines · 40 tokens per session scan A 763b5c2ad177
eval-audit is a skill published in the GitHub repository Goodeye-Labs/truesight-mcp-skills (7 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 585 once invoked, about $0.0002 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 2026-08-31.
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