Agent skill

Research Lint

by iusztinpaul in iusztinpaul/ai-research-os-workshop

Health-check a research directory produced by /research. An agent skill from iusztinpaul/ai-research-os-workshop.

MITAuto-check passedKnowledge Management

Install Research Lint

skills CLI
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-lint -a claude-code

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

GitHub CLI
$ gh skill install iusztinpaul/ai-research-os-workshop research-lint --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/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ai-research-os/skills/research-lint .claude/skills/research-lint && 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
research-lint
GitHub stars
179
Token cost
~2.3k tokens
SKILL.md length
777 words
Files
7 (incl. scripts)
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Health-check a research directory produced by /research. An agent skill from iusztinpaul/ai-research-os-workshop.

  • Works in 7 steps: Locate the research dir → Pick the checks → Run the cheap checks (in parallel via… → …
  • The user says things like lint my research
  • SKILL.md covers Step 1 — Locate the research dir, Step 2 — Pick the checks, Step 3 — Run the cheap checks… and Step 4 — Run the LLM checks…, plus 6 more sections
  • Runs Python scripts from its folder; calls uv

What it does

Research Lint is an agent skill from iusztinpaul/ai-research-os-workshop. Health-check a research directory produced by /research. Runs seven checks — orphan sources, missing entity/concept hubs, missing comparison candidates, broken wikilinks, stale claims, contradictions, and open-question synthesis. Outputs a report; edits where safe (broken-link flags, open-question append, contradiction surfacing); flags-only otherwise. Always user-triggered, never automated. Trigger when the user says things like "lint my research", "health check my research", "check the wiki", "audit my research…

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `agents/lint_judge.md`, `scripts/_lintlib.py` and `scripts/lint_broken_links.py`).

It sits in Knowledge Management, covering Linting and formatting. The repository describes itself as: How to turn your Second Brain into a living research memory that your agents maintain. Workshop with slides, video and code. The licence is MIT.

When your agent uses it

  • The user says things like lint my research
  • Health check my research
  • Audit my research dir
  • Whats wrong with my wiki

Example prompts

  • “lint my research”
  • “health check my research”
  • “check the wiki”
  • “/research-lint”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Locate the research dir
  2. Pick the checks
  3. Run the cheap checks (in parallel via bash)
  4. Run the LLM checks (parallel subagents)
  5. Apply the safe edits
  6. Append to log.md
  7. Present the report

What it can do on your machine

Read from SKILL.md and the folder at commit dc66605. 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 5 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

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

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    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

Research Lint loads about 2.3k tokens when it runs. Until then it costs about 154 tokens; SKILL.md has 777 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~154
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

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 iusztinpaul/ai-research-os-workshop at commit dc66605, republished under its MIT licence (© iusztinpaul). 777 words, ~2,287 tokens.

Download SKILL.mdSave it as .claude/skills/research-lint/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
research-lint
description
Health-check a research directory produced by /research. Runs seven checks — orphan sources, missing entity/concept hubs, missing comparison candidates, broken wikilinks, stale claims, contradictions, and open-question synthesis. Outputs a report; edits where safe (broken-link flags, open-question append, contradiction surfacing); flags-only otherwise. Always user-triggered, never automated. Trigger when the user says things like "lint my research", "health check my research", "check the wiki", "audit my research dir", "what's wrong with my wiki", "find orphans / contradictions / stale claims".
user_invocable
true

Research Lint

You audit a research directory and surface health issues. Lint is user-triggered, never automated. Each pass is read-mostly: you write only to wiki/open-questions.md, wiki/contradictions.md, index.yaml, index.md, and log.md — never to source pages, entity pages, concept pages, or raw files.

This skill answers: "what's broken or thin in this wiki, and what should I research next?"

Step 1 — Locate the research dir

Same logic as /research (query mode):

  1. If the user provides a path, use it.
  2. Otherwise scan working-dir for research-*/ and ask if multiple exist.
  3. If only one exists, use it.

Verify it's a v4 layout — <research_dir>/raw/ and <research_dir>/wiki/ must exist directly under the research dir (no memory/ wrapper). If it's older (v1 with raw at root, or v3 with memory/ wrapper), instruct the user to run migrate_layout.py first and stop.

Step 2 — Pick the checks

Default: run all seven checks. The user can scope down via natural language ("just check broken links", "skip the LLM stuff"). Map their phrasing to:

CheckCostEdits the wiki?
orphanscheap (script)flags only
missing-hubscheap (script)flags only
missing-comparisonscheap (script)flags only
broken-linkscheap (script)flags only
stale-claimsLLMflags only
contradictionsLLM (slow)appends to wiki/contradictions.md
open-questionsLLMappends to wiki/open-questions.md

The four cheap checks always run. The three LLM checks run by default but can be skipped per user request.

Step 3 — Run the cheap checks (in parallel via bash)

Run all four mechanical scripts in parallel and collect their JSON outputs. They all read-only; safe to run any time.

bash
SKILL_DIR="${CLAUDE_PLUGIN_ROOT:-.claude}/skills/research-lint"
RD="<research_dir>"

uv run --script "$SKILL_DIR/scripts/lint_orphans.py" --research-dir "$RD" > "$RD/lint-orphans.json" &
uv run --script "$SKILL_DIR/scripts/lint_broken_links.py" --research-dir "$RD" > "$RD/lint-broken-links.json" &
uv run --script "$SKILL_DIR/scripts/lint_missing_hubs.py" --research-dir "$RD" > "$RD/lint-missing-hubs.json" &
uv run --script "$SKILL_DIR/scripts/lint_missing_comparisons.py" --research-dir "$RD" > "$RD/lint-missing-comparisons.json" &
wait

Each script prints a single-line JSON {check: "...", findings: [...]} describing what it found. Aggregate counts only — do not load full findings into your context window unless the user explicitly asks for the full list.

Step 4 — Run the LLM checks (parallel subagents)

Spawn three lint_judge subagents in parallel using a single message with multiple Agent calls. Each gets a different check_type:

  1. contradictions — reads every wiki/sources/*.md "Tensions" section + entity/concept "Tensions" sections; writes new contradictions to wiki/contradictions.md.
  2. stale-claims — for each entity/concept page with source_count >= 3, checks whether the newest source contradicts older claims; flags candidates without writing.
  3. open-questions — synthesizes gaps from wiki state into wiki/open-questions.md.

Pass each subagent:

  • check_type
  • research_dir
  • research_topic, input_summary (read from index.yaml)
  • output_path (only for contradictions and open-questions; null for stale-claims since it's flag-only)

Each returns a JSON summary on stdout: {check_type, findings_count, written: true|false, flags: [...]}. The orchestrator aggregates flags into the final report.

Step 5 — Apply the safe edits

Some checks generate additive edits the lint pass should apply automatically. Others generate flags the user must act on.

CheckAuto-applyNotes
orphansNOflag only — user decides whether to delete the source or add a wiki citation
missing-hubsNOflag — user can /research it or accept the absence
missing-comparisonsNOflag — user can /research-render comparison <a> <b> if they want
broken-linksNOflag — wiki page may be missing because not yet promoted, or because the link is genuinely wrong
stale-claimsNOflag — needs human judgment
contradictionsYESappend to wiki/contradictions.md (the page is meant to grow)
open-questionsYESappend new questions to wiki/open-questions.md

After any auto-apply edits land, regenerate the index:

bash
# If contradictions.md or open-questions.md changed, count_wiki_pages may have moved.
PRIOR_CREATED=$(grep '^created:' "<research_dir>/index.yaml" | awk -F"'" '{print $2}')
# Re-run build_index_yaml.py is unnecessary because no source data changed,
# but build_index_md.py must regenerate to reflect new pages in the index.
uv run --script ${CLAUDE_PLUGIN_ROOT:-.claude}/skills/research/scripts/build_index_md.py --research-dir "<research_dir>"
Show full SKILL.md (256 more words)Show less

Step 6 — Append to log.md

bash
DATE=$(date -u +%Y-%m-%d)
cat >> "<research_dir>/log.md" <<EOF

## [$DATE] lint | <topic>

- orphans: <count>
- missing-hubs: <count>
- missing-comparisons: <count>
- broken-links: <count>
- stale-claims: <count flagged>
- contradictions: <count appended to contradictions.md>
- open-questions: <count appended to open-questions.md>
EOF

Step 7 — Present the report

Structure the report so the user can scan it and act:

## Lint report — <topic>
Research dir: <path>
Run at: <ISO-8601>

### Summary
- Total sources: <N>, total wiki pages: <M>
- Issues found: <orphans> orphans · <missing_hubs> missing hubs · <broken> broken links · <stale> stale claims · <contradictions> contradictions · <open_questions> open questions

### Action items (need your decision)
1. Orphans (<N>) — sources never cited by the wiki:
   - <title> (origin: <origin>, score: 0.XX)
   - ... (capped at 5 shown; full list in <research_dir>/lint-orphans.json)
2. Missing hubs (<N>) — concepts/entities mentioned ≥3 times with no page:
   - "<concept>" appears in <X> source pages
   - ...
3. Broken links (<N>):
   - [[wiki/concepts/foo]] referenced from [[wiki/sources/abc]] but the file doesn't exist
   - ...
4. Stale claims (<N>): see <research_dir>/lint-stale-claims.json

### Auto-applied edits
- <K> contradictions appended to [[wiki/contradictions.md]]
- <L> open questions appended to [[wiki/open-questions.md]]

### Next steps
- For each orphan: decide delete vs. add wiki citation
- For missing hubs: run /research with the concept name, OR accept the absence
- For missing comparisons: run /research-render comparison <a> <b>
- For broken links: edit the source file, OR remove the reference

Cap the bulleted lists at 5 items per category. Point users at the JSON files in <research_dir>/ for the full lists. After they review, the JSONs can be deleted (rm <research_dir>/lint-*.json) — they are scratch.

Important notes

  • You are read-mostly. The only files you write to are: wiki/contradictions.md, wiki/open-questions.md, index.md (regenerated), and log.md (append). Never touch source pages, entity pages, concept pages, or raw files.
  • Subagents do the LLM work. Do not load source pages or wiki pages into your own context — spawn subagents (or use scripts) for everything content-heavy.
  • Idempotent. Re-running lint should produce no new edits if the wiki hasn't changed (modulo last_updated and the log entry). Contradictions and open-questions append only when genuinely new.
  • Cheap before expensive. Always run the 4 mechanical scripts first; their findings can sharpen the LLM checks (e.g., the contradictions subagent only needs to read pages with source_count >= 2).

Agent reference

  • agents/lint_judge.md — Lint Judge Subagent. Generic agent dispatched for contradictions, stale-claims, and open-questions checks via a check_type input.

Script reference

  • scripts/lint_orphans.py — Finds sources in index.yaml whose original_path and uri_full are never wikilinked from any wiki page.
  • scripts/lint_broken_links.py — Scans every wiki page for [[wikilinks]] and flags any that don't resolve to an existing file.
  • scripts/lint_missing_hubs.py — Counts entity/concept name mentions across source pages; flags those with ≥3 mentions and no wiki/entities/<slug>.md or wiki/concepts/<slug>.md.
  • scripts/lint_missing_comparisons.py — Pairs entity/concept pages by mutual [[wikilink]] references; flags pairs with ≥2 mutual citations and no comparison page.

© iusztinpaul, MIT. 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 6 other files (scripts) in plugins/ai-research-os/skills/research-lint of iusztinpaul/ai-research-os-workshop.

  • SKILL.md
  • agents/lint_judge.md
  • scripts/_lintlib.py
  • scripts/lint_broken_links.py
  • scripts/lint_missing_comparisons.py
  • scripts/lint_missing_hubs.py
  • scripts/lint_orphans.py

Open the folder on GitHubat commit dc66605

Compare with similar skills

Research Lint 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.

Research Lint compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Research Lint this skilliusztinpaul/ai-research-os-workshop179—~2.3kAutomated safety check: PassMIT
Maintain Codex Wikiaiskillstore/marketplace4331 repos~2kAutomated safety check: NotesNone
Mc Wiki Deep Lintreceptron/mulmoclaude371—~1.4kAutomated safety check: WarnMIT
Speckit Wiki Statusharu/redmine_ai_helper106—~647Automated safety check: PassMIT
LLM Wikilewislulu/llm-wiki-skill655—~3.7kAutomated safety check: PassNone
Second Brain LintNicholasSpisak/second-brain737—~1kAutomated safety check: NotesNone

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Questions about Research Lint

What does Research Lint do?

Health-check a research directory produced by /research. An agent skill from iusztinpaul/ai-research-os-workshop. Research Lint is an agent skill from iusztinpaul/ai-research-os-workshop. Health-check a research directory produced by /research.

When should I use Research Lint?

Research Lint fits situations like: the user says things like lint my research; health check my research; audit my research dir; whats wrong with my wiki.

How do I install Research Lint in Claude Code?

Run `npx skills add iusztinpaul/ai-research-os-workshop --skill research-lint -a claude-code`. Or copy the skill folder (plugins/ai-research-os/skills/research-lint in iusztinpaul/ai-research-os-workshop) into .claude/skills/research-lint in your project. Claude Code loads it when a task matches its description.

How do I install Research Lint in Codex?

Run `npx skills add iusztinpaul/ai-research-os-workshop --skill research-lint -a codex`. Or copy the skill folder (plugins/ai-research-os/skills/research-lint in iusztinpaul/ai-research-os-workshop) into .agents/skills/research-lint in your project. Codex loads it when a task matches its description.

Can I use Research Lint 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 iusztinpaul/ai-research-os-workshop --skill research-lint -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research-lint, .gemini/skills/research-lint, .github/skills/research-lint and .opencode/skills/research-lint in your project.

What does Research Lint need to run?

Going by SKILL.md and its folder, Research Lint needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Research Lint access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Research Lint 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 Research Lint use?

Research Lint is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Research Lint use?

About 2.3k tokens (SKILL.md is roughly 9.1k 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 Research Lint?

Skills that share tags, products or a category with Research Lint: Maintain Codex Wiki (aiskillstore/marketplace, 433 stars), Mc Wiki Deep Lint (receptron/mulmoclaude, 371 stars), Speckit Wiki Status (haru/redmine_ai_helper, 106 stars) and LLM Wiki (lewislulu/llm-wiki-skill, 655 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Research Lint?

iusztinpaul (a GitHub user) maintains it in iusztinpaul/ai-research-os-workshop, which has 179 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on June 27, 2026.

Source: iusztinpaul/ai-research-os-workshop on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.