Agent skill

Ds Review

by OpenLAIR in OpenLAIR/dr-claw

A skill your agent uses when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.

MITAuto-check passed

Install Ds Review

skills CLI
$ npx skills add OpenLAIR/dr-claw --skill ds-review -a claude-code

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

GitHub CLI
$ gh skill install OpenLAIR/dr-claw ds-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/OpenLAIR/dr-claw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ds-review .claude/skills/ds-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
ds-review
GitHub stars
1.2k
Token cost
~4k tokens
SKILL.md length
1,978 words
Files
4 (incl. references)
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.

  • Works in 8 steps: Plan the audit → Check novelty and positioning only when… → Write a reliable review report → …
  • Paper-like report is substantial enough for an independent skeptical audit before finalization
  • SKILL.md covers Interaction discipline, Purpose, Use when and Do not use when, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ds Review is an agent skill from OpenLAIR/dr-claw. Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/experiment-todo-template.md`, `references/review-report-template.md` and `references/revision-log-template.md`).

The repository describes itself as: A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power. The licence is MIT.

When your agent uses it

  • Paper-like report is substantial enough for an independent skeptical audit before finalization
  • Revision routing

Example prompts

  • “/ds-review”

Workflow steps

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

  1. Plan the audit
  2. Check novelty and positioning only when needed
  3. Write a reliable review report
  4. Produce the revision log
  5. Produce the follow-up experiment TODO list
  6. Route the next step
  7. Auto follow-up execution contract
  8. Manuscript revision delivery contract

What it can do on your machine

Read from SKILL.md and the folder at commit d51b64e. 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

Ds Review loads about 4k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 41 tokens; SKILL.md has 1,978 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.8k

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 OpenLAIR/dr-claw at commit d51b64e, republished under its MIT licence (© OpenLAIR). 1,978 words, ~4,026 tokens.

Download SKILL.mdSave it as .claude/skills/ds-review/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
ds-review
description
Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.
skill_role
companion
license
MIT
metadata.author
ResearAI/DeepScientist
metadata.version
1.0.0

Review

Use this skill when the quest already has a substantial draft, paper, or paper-like report and now needs an independent, skeptical, evidence-grounded audit.

This is not the same as ordinary write. It is also not the same as rebuttal.

  • write turns accepted evidence into a narrative.
  • review audits that narrative like a harsh but constructive expert reviewer.
  • rebuttal responds to concrete external reviewer pressure that already exists.

Interaction discipline

  • Follow the shared interaction contract injected by the system prompt.
  • For ordinary active work, prefer a concise progress update once work has crossed roughly 6 tool calls with a human-meaningful delta, and do not drift beyond roughly 12 tool calls or about 8 minutes without a user-visible update.
  • When the review report, revision plan, or follow-up experiment TODO list becomes durable, send a richer artifact.interact(kind='milestone', reply_mode='threaded', ...) update that says what the main risks are, what should be fixed next, and whether the next route is writing, experiment, or claim downgrade.
  • Hard execution rule: if this stage needs terminal work such as document builds, scripted checks, Git inspection, or file inspection, every such command must go through bash_exec.

Purpose

review is an auxiliary audit skill for paper-like deliverables.

It should convert “the draft feels almost done” into a durable, skeptical, technically grounded review workflow:

  1. identify the core claims and likely rejection reasons
  2. audit novelty, value, rigor, clarity, and evidence sufficiency
  3. write a reliable review note, not vague prose
  4. produce a concrete revision plan
  5. produce a follow-up experiment TODO list only when the paper truly needs more evidence
  6. route the next step cleanly to write, analysis-campaign, baseline, scout, or decision

Default review stance: independent audit before celebration. Do not treat “looks polished” as “is defensible”.

Use when

  • a substantial paper/draft.md, report draft, or paper-like manuscript already exists
  • the quest has enough evidence to support a real audit rather than just speculative comments
  • the user asks for:
    • a harsh review
    • a reliable paper audit
    • revision advice before submission
    • a decision about whether more experiments are still needed
  • the writing line feels close to done and you need a skeptical gate before stopping

Do not use when

  • the quest still lacks a meaningful draft or report
  • the task is ordinary drafting from evidence
  • concrete external reviewer comments already exist and the real task is response / revision
    • in that case use rebuttal

Non-negotiable rules

  • Review independently. Do not simply mirror previous self-review notes.
  • Do not fabricate praise, flaws, citations, novelty overlaps, or fatal defects.
  • Keep every serious criticism evidence-grounded.
  • Do not recommend more experiments when the real problem is wording, positioning, or claim scope.
  • Do not recommend rhetoric when the real problem is missing evidence.
  • If novelty or positioning is uncertain, treat that as a literature-audit question first, not an automatic experiment request.
  • If a claim is too broad for the evidence, prefer narrowing or downgrading the claim over defending it with style.
  • If startup_contract.review_followup_policy is present, honor it:
    • audit_only
      • stop after durable review artifacts and a clear route recommendation
    • auto_execute_followups
      • do not stop at the audit if the next route is already clear; continue into the required experiments and manuscript deltas
    • user_gated_followups
      • finish the audit first, then package the next expensive follow-up step into one structured decision
  • If startup_contract.manuscript_edit_mode = latex_required, treat the provided LaTeX tree or paper/latex/ as the writing surface when manuscript revision is needed.
  • If LaTeX source is unavailable while latex_required is requested, do not pretend the manuscript was edited; produce LaTeX-ready replacement text and an explicit blocker note instead.
  • Accept manuscript and review inputs from URLs, local file paths, local directories, or current-turn attachments; do not assume the draft is already perfectly normalized.

Primary inputs

Use, in roughly this order:

  • the current paper or report draft
  • the selected outline if one exists
  • the claim-evidence map if one exists
  • the six-field evaluation_summary blocks from recent main experiments and analysis slices
  • recent main and analysis experiment results
  • figures, tables, and captions
  • current-turn attachments and user-provided local paths / directories / URLs for the manuscript bundle or review packet
  • prior self-review or reviewer-first notes as low-trust auxiliary input
  • nearby papers when novelty or comparison is unclear

If the draft/result state is still unclear, open intake-audit first before continuing the review workflow. Before proposing extra experiments, read those structured evaluation_summary blocks first so you do not request work that the recorded evidence already resolved. If the user provided draft files or manuscript bundles directly, first normalize them into durable quest-visible paths before planning experiments or section-level revisions.

Core outputs

The review pass should usually leave behind:

  • paper/review/review.md
  • paper/review/revision_log.md
  • paper/review/experiment_todo.md
  • paper/paper_experiment_matrix.md when more evidence is still needed
  • paper/paper_experiment_matrix.json when more evidence is still needed

Use the templates in references/ when needed:

  • review-report-template.md
  • revision-log-template.md
  • experiment-todo-template.md

Review dimensions

Audit at least these dimensions:

  • research question and value
  • novelty and positioning
  • method-to-problem fit
  • evidence sufficiency
  • experimental validity and baseline comparability
  • claim scope and over-claiming risk
  • writing defensibility and logical flow
  • figure / table usefulness
  • submission readiness

Workflow

1. Plan the audit

Before writing the review itself, make the audit explicit.

Identify:

  • 1 to 3 core claims such as C1, C2, C3
  • the strongest current evidence
  • the weakest current evidence
  • the top 3 likely rejection reasons
  • whether the likely next route is:
    • text revision
    • literature / novelty audit
    • baseline recovery
    • supplementary experiment
    • claim downgrade
2. Check novelty and positioning only when needed

If novelty, related-work coverage, or field positioning is unclear:

  1. open scout
  2. run a focused literature / comparison audit
  3. record what is genuinely overlapping, what remains novel, and what is merely better positioned writing

Do not request new experiments just to answer a literature-positioning question.

3. Write a reliable review report

Write paper/review/review.md using references/review-report-template.md.

The review should be:

  • independent
  • skeptical but constructive
  • technically specific
  • reader-aware
  • evidence-grounded

At minimum, the review report should cover:

  • summary
  • strengths
  • weaknesses
  • key issues
  • actionable suggestions
  • storyline / outline advice
  • priority revision plan
  • experiment inventory and research experiment plan
  • novelty verification and related-work matrix
  • references

If helpful, include an internal conservative overall judgment or score, but do not pretend numerical precision when evidence is still unstable.

4. Produce the revision log

Write paper/review/revision_log.md using references/revision-log-template.md.

For each serious issue, record:

  • issue id
  • why it matters
  • what should change
  • whether the fix is writing-only, evidence-only, or experiment-dependent
  • whether the issue blocks finalize
  • one copy-ready replacement sentence / paragraph when feasible
  • one LaTeX-ready replacement block when startup_contract.manuscript_edit_mode = latex_required
Show full SKILL.md (924 more words)Show less
5. Produce the follow-up experiment TODO list

Only if more evidence is truly needed, write paper/review/experiment_todo.md using references/experiment-todo-template.md.

When the paper still lacks experimental support, also create or revise:

  • paper/paper_experiment_matrix.md
  • paper/paper_experiment_matrix.json

Treat the matrix as the paper-facing master plan and paper/review/experiment_todo.md as only the current execution frontier or review-facing subset.

Each TODO item should include:

  • the review issue it answers
  • the matrix exp id
  • the corresponding exp_id in the paper experiment matrix
  • why existing evidence is still insufficient
  • the minimum experiment or analysis needed
  • required metric(s)
  • minimal success criterion
  • whether this is:
    • analysis of existing results
    • new comparator baseline
    • supplementary experiment
    • figure / table regeneration only

Do not write a vague “run more ablations” list. Each TODO item should be concrete enough to turn into analysis-campaign slices or a baseline recovery task. The matrix should be broader than the TODO list and should classify the full paper-facing experiment space, not just analysis work. When building or revising that matrix, explicitly consider:

  • main comparison packaging or extension
  • component ablations
  • sensitivity / hyperparameter checks
  • robustness checks
  • efficiency / cost / latency / token-overhead checks when relevant
  • highlight-validation experiments that test the likely strengths of the method
  • limitation-boundary analyses
  • case study rows as optional rather than mandatory evidence

Do not assume the paper only needs “analysis experiments”. Do not assume case studies belong in the required set. If efficiency or cost could become a reviewer-facing strength or concern, put that into the matrix explicitly.

For the matrix, each row should usually record:

  • exp_id
  • tier
  • experiment_type
  • status
  • feasibility_now
  • claim_ids
  • highlight_ids
  • research_question
  • hypothesis
  • comparators
  • metrics
  • minimal_success_criterion
  • paper_placement
  • promotion_rule
  • next_action

The matrix should also keep a short highlight hypotheses block. Do not rely on prose intuition for the method's best selling point; if a likely highlight matters, it should have a corresponding validation row in the matrix.

Before treating the experiments section as stable, require that every currently feasible matrix row that is not merely optional or dropped is either:

  • completed
  • analyzed
  • excluded with a real reason
  • or blocked with a real reason

When extra evidence is truly needed, use the shared supplementary-experiment protocol:

  • recover ids / refs first if needed
  • create one artifact.create_analysis_campaign(...)
  • represent even one extra run as a one-slice campaign
  • record each completed slice with artifact.record_analysis_slice(...)

Do not invent a separate review-only experiment workflow.

6. Route the next step

After the review artifacts are durable:

  • if the issues are mostly narrative or claim-scope fixes, route to write
  • if novelty / positioning is still unclear, route to scout
  • if a requested comparator baseline is missing, route to baseline
  • if new evidence is truly required, route to analysis-campaign
  • if the route is costly or non-obvious, record a decision

Do not stop immediately after writing the review if the next route is already clear.

7. Auto follow-up execution contract

When startup_contract.review_followup_policy = auto_execute_followups:

  • treat the review as a gate, not as the endpoint
  • immediately turn the accepted follow-up route into action:
    • analysis-campaign
      • when new evidence is truly required
    • baseline
      • when a missing comparator baseline blocks fair review
    • write
      • when the issues are mostly text, outline, claim-scope, figure, or framing revisions
  • after each completed follow-up step, update:
    • paper/review/revision_log.md
    • paper/review/experiment_todo.md
    • the draft or manuscript-facing revision package
  • only treat the review line as truly closed after the follow-up route has either completed or been downgraded / blocked explicitly

When startup_contract.review_followup_policy = user_gated_followups:

  • stop after the durable audit artifacts
  • turn the next expensive follow-up package into one structured decision instead of continuing silently

When startup_contract.review_followup_policy = audit_only:

  • stop after the durable audit artifacts and route recommendation
8. Manuscript revision delivery contract

If manuscript revision is required, make the delta explicit:

  • section
  • old claim / weakness
  • new wording
  • evidence basis
  • remaining limitation

If startup_contract.manuscript_edit_mode = copy_ready_text:

  • provide copy-ready replacement wording in paper/review/revision_log.md or a nearby revision note
  • keep the wording directly usable by the user or downstream write

If startup_contract.manuscript_edit_mode = latex_required:

  • prefer editing the actual LaTeX sources when they are available
  • otherwise provide LaTeX-ready replacement text blocks with explicit insertion targets
  • preserve labels, citations, figure/table refs, and section structure in the suggested replacements

Companion skill routing

Open additional skills only when the review workflow requires them:

  • intake-audit
    • when the current draft/result/bundle state is still unclear
  • scout
    • when novelty, positioning, or related-work coverage is genuinely uncertain
  • baseline
    • when a missing comparator baseline blocks fair review
  • analysis-campaign
    • when the review identifies concrete evidence gaps that need supplementary runs
  • write
    • when the review identifies text, outline, claim-scope, or figure revisions
  • figure-polish
    • when the review identifies figure/table quality as a real weakness
  • decision
    • when route choice, cost, or claim downgrade is non-trivial

Artifact routing guidance

Use these tools deliberately:

  • artifact.record(payload={'kind': 'decision', ...})
    • review conclusion, claim downgrade recommendation, route choice, stop/go recommendation
  • artifact.create_analysis_campaign(...)
    • when the experiment TODO list should become concrete follow-up slices
  • artifact.record_analysis_slice(...)
    • one completed review-driven slice
  • artifact.submit_paper_outline(mode='revise', ...)
    • when the review materially changes the narrative blueprint
  • artifact.submit_paper_bundle(...)
    • only when the revised manuscript package is genuinely ready
  • artifact.interact(...)
    • user-visible progress and review milestones

Memory discipline

Stage-start requirement:

  • run memory.list_recent(scope='quest', limit=5)
  • run at least one memory.search(...) for:
    • paper title
    • main method name
    • review or self-review
    • key claim or strongest figure

Stage-end requirement:

  • if the review produced a durable lesson, claim downgrade, revision rule, or experiment-gap judgment, write at least one memory.write(...)

Useful tags include:

  • stage:review
  • type:paper-review
  • type:revision-plan
  • type:experiment-gap
  • type:claim-downgrade

Success condition

review is successful when:

  • a reliable skeptical review note exists
  • the highest-risk issues are explicit
  • the next revision route is unambiguous
  • any needed experiments are captured as a concrete TODO list
  • the quest can continue into write, analysis-campaign, baseline, scout, or finalize without ambiguity

The goal is not to sound severe. The goal is to make the next revision step technically clear and evidence-bound.

© OpenLAIR, 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 3 other files (references) in skills/ds-review of OpenLAIR/dr-claw.

  • SKILL.md
  • references/experiment-todo-template.md
  • references/review-report-template.md
  • references/revision-log-template.md

Open the folder on GitHubat commit d51b64e

Compare with similar skills

Ds 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.

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Rust Validationmacro-inc/macro4.6k—~396Automated safety check: NotesAGPL-3.0

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Questions about Ds Review

What does Ds Review do?

A skill your agent uses when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing. Ds Review is an agent skill from OpenLAIR/dr-claw. Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.

When should I use Ds Review?

Ds Review fits situations like: paper-like report is substantial enough for an independent skeptical audit before finalization; revision routing.

How do I install Ds Review in Claude Code?

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

How do I install Ds Review in Codex?

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

Can I use Ds 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 OpenLAIR/dr-claw --skill ds-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/ds-review, .gemini/skills/ds-review, .github/skills/ds-review and .opencode/skills/ds-review in your project.

What does Ds Review need to run?

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

Does Ds 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 Ds 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 Ds Review use?

Ds Review is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ds Review use?

About 4k tokens (SKILL.md is roughly 16k 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 792 tokens, read only when the agent opens those files.

What are the alternatives to Ds Review?

Skills that share tags, products or a category with Ds Review: Orchestrate (sickn33/agentic-awesome-skills, 47k stars), Marketing Claims Review (anthropics/claude-for-legal, 9.6k stars), Deepchat Sdd (ThinkInAIXYZ/deepchat, 6.4k stars) and Doc Coauthoring (Galaxy-Dawn/claude-scholar, 5.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ds Review?

OpenLAIR (a GitHub organization) maintains it in OpenLAIR/dr-claw, which has 1,155 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on September 17, 2026.

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