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Agent skill

Runtime Log Review

by JetXu-LLM in JetXu-LLM/DocMason

Review DocMason runtime query-session and retrieval-trace logs through the summary surface rather than raw JSON browsing alone.

Apache-2.0Auto-check passedKnowledge Management

Install Runtime Log Review

skills CLI
$ npx skills add JetXu-LLM/DocMason --skill runtime-log-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JetXu-LLM/DocMason runtime-log-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/canonical/runtime-log-review .claude/skills/runtime-log-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
runtime-log-review
GitHub stars
147
Token cost
~1.2k tokens
SKILL.md length
599 words
Files
2
Skills in repo
15
Repo updated
First seen
Licence
Apache-2.0

At a glance

Review DocMason runtime query-session and retrieval-trace logs through the summary surface rather than raw JSON browsing alone.

  • Works in 9 steps: For an explicit operator refresh, prefer… → Start with… → Use the summary modes that best match… → …
  • Knowledge Management work in your project
  • SKILL.md covers Required Capabilities, Procedure, Escalation Rules and Completion Signal, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Runtime Log Review is an agent skill from JetXu-LLM/DocMason. Review DocMason runtime query-session and retrieval-trace logs through the summary surface rather than raw JSON browsing alone.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `workflow.json`).

It sits in Knowledge Management. The repository describes itself as: DocMason is a repo-native agent that turns your complex office files into a local LLM knowledge base and your second brain. The repo is the app. Codex is the runtime. The licence is Apache-2.0.

When your agent uses it

  • Knowledge Management work in your project

Example prompts

  • “/runtime-log-review”

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. For an explicit operator refresh, prefer docmason workflow runtime-log-review --json so the derived summary and the request-level audit…
  2. Start with runtime/logs/review/summary.json and runtime/logs/review/benchmark-candidates.json when they exist.
  3. Use the summary modes that best match the request
  4. When the summary shows a case worth deeper inspection, open the referenced query-session or retrieval-trace JSON directly.
  5. If the operator needs the underlying evidence, route to retrieval, provenance tracing, or grounded-answer rather than guessing from log…
  6. Keep the workflow descriptive and review-oriented. Do not mutate prompts, skills, overlays, or benchmarks from inside this workflow.
  7. If you need to export a scratch review summary and the user did not specify a destination, place it under runtime/agent-work/.
  8. Treat runtime/logs/review/requests/.json as the canonical audit surface for the explicit review request that refreshed or read the…
  9. Return the operator-facing review summary and recommended next steps to the main agent.

What it can do on your machine

Read from SKILL.md and the folder at commit 362417b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Runtime Log Review loads about 1.2k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 599 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from JetXu-LLM/DocMason at commit 362417b, republished under its Apache-2.0 licence (© JetXu-LLM). 599 words, ~1,175 tokens.

Download SKILL.mdSave it as .claude/skills/runtime-log-review/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
runtime-log-review
description
Review DocMason runtime query-session and retrieval-trace logs through the summary surface rather than raw JSON browsing alone.

Runtime Log Review

Use this skill when the task is to review recent runtime activity, identify failures, or extract candidate cases from DocMason logs.

This is an explicit operator-facing workflow. The user may ask for it directly, or ask may route here automatically when the real intent is runtime review rather than question answering.

Required Capabilities

  • local file access
  • shell or command execution
  • ability to inspect structured JSON output

If the agent cannot inspect local runtime logs, stop and explain that log review is not possible.

Procedure

  1. For an explicit operator refresh, prefer docmason workflow runtime-log-review --json so the derived summary and the request-level audit record are regenerated together.
  2. Start with runtime/logs/review/summary.json and runtime/logs/review/benchmark-candidates.json when they exist.
    • treat live conversation state under runtime/state/ as the owner and runtime/logs/conversations/ as projection-only
      • projection-only means a derived mirror, not the primary truth surface
    • require canonical ask ownership before classifying a case as interactive-ask; workflow names, conversation linkage, or reconciliation leftovers alone are not enough
      • here canonical ask ownership means the case is backed by a governed ask turn and linked runtime artifacts, not only host transcript residue
  3. Use the summary modes that best match the request:
    • recent activity
    • no-result retrieval sessions
    • artifact-rich queries that still degraded or returned the wrong source family
    • degraded answer-first traces
    • trace cases where artifact supports existed but the final answer still remained partially grounded or unresolved
    • repeated failure patterns
    • frequently consulted sources or units
    • candidate benchmark or operator-review cases
    • real interaction activity versus synthetic evaluation traffic
    • active waiting shared jobs
    • active confirmation-required shared jobs
    • orphaned query sessions or retrieval traces that are not backed by committed truth
  4. When the summary shows a case worth deeper inspection, open the referenced query-session or retrieval-trace JSON directly.
  5. If the operator needs the underlying evidence, route to retrieval, provenance tracing, or grounded-answer rather than guessing from log metadata alone.
  6. Keep the workflow descriptive and review-oriented. Do not mutate prompts, skills, overlays, or benchmarks from inside this workflow.
  7. If you need to export a scratch review summary and the user did not specify a destination, place it under runtime/agent-work/.
  8. Treat runtime/logs/review/requests/<request_id>.json as the canonical audit surface for the explicit review request that refreshed or read the review-side outputs.
  9. Return the operator-facing review summary and recommended next steps to the main agent.
Show full SKILL.md (212 more words)Show less

Escalation Rules

  • If runtime/logs/review/summary.json does not exist yet, refresh the workflow once. If the regenerated summary still shows no recent activity, explain that the workspace has no recent ask, retrieval, or trace evidence to review yet.
  • If the summary shows degraded answer traces or unresolved answer states, preserve that uncertainty instead of flattening it into a generic warning.
  • If the request requires evidence validation rather than log review, switch to retrieval or provenance tracing.

Completion Signal

  • The workflow is complete when the main agent has a concise review summary with the relevant case IDs, repeated patterns, and follow-up recommendations.

Notes

  • This is an explicit operator-facing workflow, not a public docmason review-logs command.
  • Each explicit review invocation should leave one replayable request artifact under runtime/logs/review/requests/.
  • The review summary is derived from runtime logs under runtime/logs/.
  • The summary should distinguish committed truth from orphaned leftovers instead of reconstructing legality from mixed artifacts.
  • orphaned leftovers means reviewable runtime artifacts that are not backed by the committed governing truth for a completed case.
  • runtime/logs/review/benchmark-candidates.json is a read-only derived artifact that suggests future benchmark cases from conversation turns, retrieval sessions, and trace outcomes.
  • Real operator and user interactions should stay at the top of the main recent-activity views; evaluation-suite traffic is intentionally demoted into separate synthetic buckets.

© JetXu-LLM, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/canonical/runtime-log-review of JetXu-LLM/DocMason.

  • SKILL.md
  • workflow.json

Open the folder on GitHubat commit 362417b

Compare with similar skills

Runtime Log Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Runtime Log Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Runtime Log Review this skillJetXu-LLM/DocMason147—~1.2kAutomated safety check: PassApache-2.0
Logseq Review Workflow Evallogseq/logseq45k—~1kAutomated safety check: PassAGPL-3.0
Baoyu URL To Markdownsdyckjq-lab/llm-wiki-skill2.5k2 repos~3.2kAutomated safety check: PassNone
Obsidian CLIAtmosphere/atmosphere3.8k13 repos~795Automated safety check: PassApache-2.0
Esm Cjs Risk Scanlogseq/logseq45k—~3.3kAutomated safety check: PassAGPL-3.0
Capture Conversationoutline/outline41k—~474Automated safety check: PassCustom licence

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  • Knowledge Base Sync

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Categories

Questions about Runtime Log Review

What does Runtime Log Review do?

Review DocMason runtime query-session and retrieval-trace logs through the summary surface rather than raw JSON browsing alone. Runtime Log Review is an agent skill from JetXu-LLM/DocMason. Review DocMason runtime query-session and retrieval-trace logs through the summary surface rather than raw JSON browsing alone.

When should I use Runtime Log Review?

Runtime Log Review fits situations like: knowledge Management work in your project.

How do I install Runtime Log Review in Claude Code?

Run `npx skills add JetXu-LLM/DocMason --skill runtime-log-review -a claude-code`. Or copy the skill folder (skills/canonical/runtime-log-review in JetXu-LLM/DocMason) into .claude/skills/runtime-log-review in your project. Claude Code loads it when a task matches its description.

How do I install Runtime Log Review in Codex?

Run `npx skills add JetXu-LLM/DocMason --skill runtime-log-review -a codex`. Or copy the skill folder (skills/canonical/runtime-log-review in JetXu-LLM/DocMason) into .agents/skills/runtime-log-review in your project. Codex loads it when a task matches its description.

Can I use Runtime Log Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JetXu-LLM/DocMason --skill runtime-log-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/runtime-log-review, .gemini/skills/runtime-log-review, .github/skills/runtime-log-review and .opencode/skills/runtime-log-review in your project.

What does Runtime Log Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Runtime Log Review is instructions for the agent only.

Does Runtime Log Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Runtime Log Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Runtime Log Review use?

Runtime Log Review is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Runtime Log Review use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Runtime Log Review?

Skills that share tags, products or a category with Runtime Log Review: Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars), Obsidian CLI (Atmosphere/atmosphere, 3.8k stars) and Esm Cjs Risk Scan (logseq/logseq, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Runtime Log Review?

JetXu-LLM (a GitHub user) maintains it in JetXu-LLM/DocMason, which has 147 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 30, 2026.

Source: JetXu-LLM/DocMason on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.