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 review-and-promote-tracesgit 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/review-and-promote-traces)<a href="https://agentmods.dev/skills/goodeye-labs/truesight-mcp-skills/review-and-promote-traces"><img src="https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/review-and-promote-traces/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/review-and-promote-traces"><img src="https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/review-and-promote-traces.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 | $0.00042 | $0.00769 |
| Opus 5 | $0.00021 | $0.00385 |
| Sonnet 5 | $0.00008 | $0.00154 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
review-and-promote-traces 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 12d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review and Promote Traces
Use this skill for the judgment and promotion loop after trace evaluation.
Interactive Q&A protocol (mandatory)
If context is unclear, ask one question at a time using the structured question tool (loaded per the HARD-GATE above).
Example question structure:
What do you want to review first?
A) One specific run
B) Pending queue triage across runs
Rules:
- Ask 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 when needed, then continue.
Workflow
- If needed, flag evaluated run:
- Use
flag_review_itemwithrun_id.
- Use
- Retrieve review items:
- Use
list_review_itemswith pagination.
- Use
- Collect human judgments:
- Present review items to the user and ask for judgment one item at a time when needed.
- Capture
judgment_valueand optionalnotesfor each item. - Use the structured question tool (loaded per the HARD-GATE above) to present options that match the live evaluation setup:
- Binary example:
- A) Pass
- B) Fail
- Categorical example:
- A) <option 1>
- B) <option 2>
- C) <option 3>
- Continuous example:
- A) Enter numeric value (within configured range)
- B) Skip this item for now
- Binary example:
- If valid options are unclear, look them up before asking:
- Inspect
list_review_itemspayload for item result details and expected value hints. - Use
get_result(run_id)for run-level context and chain outputs. - If evaluation id is available in run metadata, call
get_evaluation(evaluation_id)and read config/judgment criteria to derive valid judgment options.
- Inspect
- When options remain ambiguous after lookup, ask one clarification question using the structured question tool before proceeding.
- Submit judgments:
- For each target item, call
judge_review_item. - Pass the user-provided
judgment_valueand optionalnotesintojudge_review_item. - Include notes when judgment context matters.
- For each target item, call
- Promote judged outputs:
- Use
add_reviewed_items_to_datasetwithrun_id.
- Use
- Report result:
- number of items judged
- promotion status and row counts
- any skipped or blocked items
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.
- 12d ago First seen · 79 lines · 42 tokens per session scan A 9ac22fd1d70f
review-and-promote-traces is a skill published in the GitHub repository Goodeye-Labs/truesight-mcp-skills (7 stars, last pushed 5mo ago), licensed MIT. It adds 42 tokens to every session and 769 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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