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Agent skill

Logseq Review Workflow Eval

by logseq in logseq/logseq

Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…

AGPL-3.0Auto-check passedKnowledge Management

Install Logseq Review Workflow Eval

skills CLI
$ npx skills add logseq/logseq --skill logseq-review-workflow-eval -a claude-code

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

GitHub CLI
$ gh skill install logseq/logseq logseq-review-workflow-eval --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/logseq/logseq.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/logseq-review-workflow-eval .claude/skills/logseq-review-workflow-eval && 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
logseq-review-workflow-eval
GitHub stars
45k
Token cost
~1k tokens
SKILL.md length
442 words
Files
5 (incl. scripts, references)
Skills in repo
12
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…

  • Works in 6 steps: Read the root AGENTS.md. → Prepare isolated snapshots → Run the generated run-before.md prompt… → …
  • Evaluating whether changes to .agents/skills/logseq-review-workflow improved review quality
  • SKILL.md covers Overview, Inputs, Workflow and Evaluation Rules, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Logseq Review Workflow Eval is an agent skill from logseq/logseq. Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a structured delta. Use when evaluating whether changes to .agents/skills/logseq-review-workflow improved review quality, coverage, validation rigor, subagent orchestration, or false-positive rate.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/evaluation-rubric.md` and `scripts/compare_outputs.py`).

It sits in Knowledge Management, covering Subagents. It works with Git. The repository describes itself as: A privacy-first, open-source platform for knowledge management and collaboration. Download link: http://github.com/logseq/logseq/releases. roadmap: https://logseq.io/p/NX4mcggEV. The licence is AGPL-3.0.

When your agent uses it

  • Evaluating whether changes to .agents/skills/logseq-review-workflow improved review quality
  • Validation rigor
  • Subagent orchestration
  • False-positive rate

Example prompts

  • “/logseq-review-workflow-eval”

Requirements

  • Python 3

Workflow steps

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

  1. Read the root AGENTS.md.
  2. Prepare isolated snapshots
  3. Run the generated run-before.md prompt in a fresh agent or fresh thread. Save the full response as outputs/before.md.
  4. Run the generated run-after.md prompt in another fresh agent or fresh thread with the same model and tool availability. Save the full…
  5. Compare outputs
  6. Add qualitative judgment using references/evaluation-rubric.md when the deterministic comparison is not enough.

What it can do on your machine

Read from SKILL.md and the folder at commit 22a29b3. 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

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    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

Logseq Review Workflow Eval loads about 1k tokens when it runs, and up to ~1.5k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 442 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~104
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.5k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from logseq/logseq at commit 22a29b3, republished under its AGPL-3.0 licence (© logseq). 442 words, ~1,023 tokens.

Download SKILL.mdSave it as .claude/skills/logseq-review-workflow-eval/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
logseq-review-workflow-eval
description
Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a structured delta. Use when evaluating whether changes to .agents/skills/logseq-review-workflow improved review quality, coverage, validation rigor, subagent orchestration, or false-positive rate.

Logseq Review Workflow Eval

Overview

Use this skill to evaluate behavior changes in .agents/skills/logseq-review-workflow without leaking the intended outcome into the review runs. Keep the review target and prompt identical, isolate each skill revision into its own snapshot, run fresh agents with the same settings, then compare the returned findings and verification discipline.

Inputs

Collect these before running the evaluation:

  • Before revision: a git ref, commit, tag, or branch that contains the old logseq-review-workflow skill.
  • After revision: usually the current working tree; use a git ref only when comparing two committed revisions.
  • Review prompt: the exact user prompt to run against both skill revisions. Include the same patch, commit range, PR description, or changed-file scope for both runs.
  • Run settings: model, reasoning effort, available tools, repository state, and whether subagents are available.

Use realistic review prompts. Prefer prompts that exercise the specific area changed in logseq-review-workflow, such as routing rules, validation requirements, pass aggregation, or no-findings handling.

Workflow

  1. Read the root AGENTS.md.

  2. Prepare isolated snapshots:

    bash
    python .agents/skills/logseq-review-workflow-eval/scripts/setup_eval.py \
  --before-ref <old-ref> \
  --prompt-file <review-prompt.md> \
  --case-name <short-case-name>

    Add --after-ref <new-ref> only when the after revision should come from git instead of the current working tree.

  3. Run the generated run-before.md prompt in a fresh agent or fresh thread. Save the full response as outputs/before.md.

  4. Run the generated run-after.md prompt in another fresh agent or fresh thread with the same model and tool availability. Save the full response as outputs/after.md.

  5. Compare outputs:

    bash
    python .agents/skills/logseq-review-workflow-eval/scripts/compare_outputs.py \
  --before <eval-dir>/outputs/before.md \
  --after <eval-dir>/outputs/after.md \
  --out <eval-dir>/comparison.md

  6. Add qualitative judgment using references/evaluation-rubric.md when the deterministic comparison is not enough.

Show full SKILL.md (196 more words)Show less

Evaluation Rules

  • Do not tell either run what changed in the skill or what result is expected.
  • Do not let the before run read the after snapshot, after output, or comparison notes.
  • Do not let the after run read the before output before it completes.
  • Use the same review target and prompt text for both runs, except for the explicit skill snapshot path.
  • Preserve raw outputs. Do not edit them before comparison.
  • Treat more findings as better only when the added findings are concrete, correctly scoped, and validated.
  • Treat stricter verification as better only when it is feasible and does not fabricate unrun checks.
  • Flag regressions where the after output loses a real finding, adds speculative noise, skips required rule routing, or claims unperformed runtime validation.

Output

Return:

  • Snapshot paths and git refs used.
  • Commands or agent prompts used to run both sides.
  • comparison.md location.
  • A concise conclusion: improved, regressed, mixed, or inconclusive.
  • The specific evidence behind that conclusion, including changed findings, validation quality, and any run limitations.

Resources

  • scripts/setup_eval.py: create isolated before/after snapshots and prompt files for both runs.
  • scripts/compare_outputs.py: summarize structural differences between two raw review outputs.
  • references/evaluation-rubric.md: qualitative scoring criteria for review-output quality.

© logseq, AGPL-3.0. 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 4 other files (scripts, references) in .agents/skills/logseq-review-workflow-eval of logseq/logseq.

  • SKILL.md
  • agents/openai.yaml
  • references/evaluation-rubric.md
  • scripts/compare_outputs.py
  • scripts/setup_eval.py

Open the folder on GitHubat commit 22a29b3

Compare with similar skills

Logseq Review Workflow Eval 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.

Logseq Review Workflow Eval compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Logseq Review Workflow Eval this skilllogseq/logseq45k—~1kAutomated safety check: PassAGPL-3.0
LLM WikiCharlesHoskinson/sevenlayer116—~1.5kAutomated safety check: PassCustom licence
Open Pagepricklywiggles/niamos192—~939Automated safety check: PassNone
Memex Synciamtouchskyer/memex142—~533Automated safety check: PassMIT
Turingdb GraphClawBio/ClawBio1.2k1 repos~4.2kAutomated safety check: PassMIT
TalkAbilityai/cornelius109—~834Automated safety check: PassMIT

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Works with

Categories

Questions about Logseq Review Workflow Eval

What does Logseq Review Workflow Eval do?

Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…. Logseq Review Workflow Eval is an agent skill from logseq/logseq. Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a structured delta.

When should I use Logseq Review Workflow Eval?

Logseq Review Workflow Eval fits situations like: evaluating whether changes to .agents/skills/logseq-review-workflow improved review quality; validation rigor; subagent orchestration; false-positive rate.

How do I install Logseq Review Workflow Eval in Claude Code?

Run `npx skills add logseq/logseq --skill logseq-review-workflow-eval -a claude-code`. Or copy the skill folder (.agents/skills/logseq-review-workflow-eval in logseq/logseq) into .claude/skills/logseq-review-workflow-eval in your project. Claude Code loads it when a task matches its description.

How do I install Logseq Review Workflow Eval in Codex?

Run `npx skills add logseq/logseq --skill logseq-review-workflow-eval -a codex`. Or copy the skill folder (.agents/skills/logseq-review-workflow-eval in logseq/logseq) into .agents/skills/logseq-review-workflow-eval in your project. Codex loads it when a task matches its description.

Can I use Logseq Review Workflow Eval 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 logseq/logseq --skill logseq-review-workflow-eval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/logseq-review-workflow-eval, .gemini/skills/logseq-review-workflow-eval, .github/skills/logseq-review-workflow-eval and .opencode/skills/logseq-review-workflow-eval in your project.

What does Logseq Review Workflow Eval need to run?

Going by SKILL.md and its folder, Logseq Review Workflow Eval needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Logseq Review Workflow Eval 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 Logseq Review Workflow Eval 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Logseq Review Workflow Eval use?

Logseq Review Workflow Eval is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Logseq Review Workflow Eval use?

About 1k tokens (SKILL.md is roughly 4.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 470 tokens, read only when the agent opens those files.

What are the alternatives to Logseq Review Workflow Eval?

Skills that share tags, products or a category with Logseq Review Workflow Eval: LLM Wiki (CharlesHoskinson/sevenlayer, 116 stars), Open Page (pricklywiggles/niamos, 192 stars), Memex Sync (iamtouchskyer/memex, 142 stars) and Turingdb Graph (ClawBio/ClawBio, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Logseq Review Workflow Eval?

logseq (a GitHub organization) maintains it in logseq/logseq, which has 45,158 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.

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