review-and-promote-traces

review-and-promote-traces is a skill for Claude Code from Goodeye-Labs/truesight-mcp-skills. It costs 42 tokens per session (769 once invoked), scanned A, original, MIT.

A workflow for reviewing flagged outputs from an evaluation run and deciding whether they should be added back to a dataset. A trace is a record of an AI system interaction or run.

In plain words
What is it for?
It is for reviewing individual runs or review queues, recording judgments and notes, and promoting approved items into an evaluation dataset.
Why use it?
It provides a defined human-review step for outputs that need judgment. This helps turn reviewed examples into labelled data for later evaluation.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the truesight plugin — 9 skills, 1 MCP server shipped together

Good fit It is for reviewing individual runs or review queues, recording judgments and notes, and promoting approved items into an evaluation dataset.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/goodeye-labs/truesight-mcp-skills/review-and-promote-traces
Install

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.

Any agent
npx skills add Goodeye-Labs/truesight-mcp-skills --skill review-and-promote-traces
Clone the repo
git clone --depth 1 https://github.com/Goodeye-Labs/truesight-mcp-skills

Made for: Claude Code.

Or install truesight, the plugin that ships this one along with the rest of its 9 skills, 1 MCP server.

Wrote 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.

agentmods badge for review-and-promote-traces

README.md
[![agentmods](https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/review-and-promote-traces/github.svg)](https://agentmods.dev/skills/goodeye-labs/truesight-mcp-skills/review-and-promote-traces)
Your own site
<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.

agentmods 80×15 button for review-and-promote-traces

Your own site · 80×15
<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>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 769 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash 9ac22fd1d70f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/review-and-promote-traces/SKILL.md · 79 lines

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

  1. If needed, flag evaluated run:
    • Use flag_review_item with run_id.
  2. Retrieve review items:
    • Use list_review_items with pagination.
  3. Collect human judgments:
    • Present review items to the user and ask for judgment one item at a time when needed.
    • Capture judgment_value and optional notes for 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
    • If valid options are unclear, look them up before asking:
      1. Inspect list_review_items payload for item result details and expected value hints.
      2. Use get_result(run_id) for run-level context and chain outputs.
      3. If evaluation id is available in run metadata, call get_evaluation(evaluation_id) and read config/judgment criteria to derive valid judgment options.
    • When options remain ambiguous after lookup, ask one clarification question using the structured question tool before proceeding.
  4. Submit judgments:
    • For each target item, call judge_review_item.
    • Pass the user-provided judgment_value and optional notes into judge_review_item.
    • Include notes when judgment context matters.
  5. Promote judged outputs:
    • Use add_reviewed_items_to_dataset with run_id.
  6. Report result:
    • number of items judged
    • promotion status and row counts
    • any skipped or blocked items

Read the full file on GitHub · 79 lines

Changes

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.

  1. 12d ago First seen · 79 lines · 42 tokens per session scan A 9ac22fd1d70f

Subscribe to this mod's changes

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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