Agent skill

Ds Intake Audit

by OpenLAIR in OpenLAIR/dr-claw

A skill your agent uses when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before…

MITAuto-check passedDevelopment

Install Ds Intake Audit

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

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

GitHub CLI
$ gh skill install OpenLAIR/dr-claw ds-intake-audit --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-intake-audit .claude/skills/ds-intake-audit && 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-intake-audit
GitHub stars
1.2k
Token cost
~2.6k tokens
SKILL.md length
1,299 words
Files
2 (incl. references)
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before…

  • Works in 6 steps: Read startup intent first → Retrieve memory before filesystem triage → Inventory the quest state → …
  • A quest does not start from a blank state and the agent must first audit
  • SKILL.md covers Interaction discipline, Tool discipline, Purpose and Use when, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ds Intake Audit is an agent skill from OpenLAIR/dr-claw. Use when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before choosing the next anchor.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/state-audit-template.md`).

It sits in Development. It works with Git. 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

  • A quest does not start from a blank state and the agent must first audit
  • Reconcile existing baselines
  • Review materials before choosing the next anchor

Example prompts

  • “/ds-intake-audit”

Workflow steps

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

  1. Read startup intent first
  2. Retrieve memory before filesystem triage
  3. Inventory the quest state
  4. Trust-rank and reconcile
  5. Choose the next anchor
  6. Report and hand off

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 Intake Audit loads about 2.6k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 1,299 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

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). 1,299 words, ~2,578 tokens.

Download SKILL.mdSave it as .claude/skills/ds-intake-audit/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ds-intake-audit
description
Use when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before choosing the next anchor.
skill_role
companion
license
MIT
metadata.author
ResearAI/DeepScientist
metadata.version
1.0.0

Intake Audit

Use this skill when the quest already has meaningful state and the first job is to normalize that state instead of restarting the canonical research loop from zero.

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.
  • Message templates are references only. Adapt to the actual context and vary wording so updates feel natural and non-robotic.
  • If a threaded user reply arrives, interpret it relative to the latest intake-audit progress update before assuming the task changed completely.
  • When the audit reaches a durable route recommendation, send one richer artifact.interact(kind='milestone', reply_mode='threaded', ...) update that says what state is trusted, what still needs work, and which anchor should run next.

Tool discipline

  • Do not use native shell_command / command_execution in this skill.
  • Any shell, CLI, Python, bash, node, git, npm, uv, or repo-audit execution must go through bash_exec(...).
  • For git inspection or maintenance inside the current quest repository or worktree, prefer artifact.git(...) before raw shell git commands.
  • Use shell execution only when durable quest files, artifacts, and memory are insufficient; do not bypass durable state just because shell feels faster.

Purpose

intake-audit is an auxiliary entry skill, not a normal long-running anchor.

Its purpose is to answer four questions before deeper work begins:

  1. what already exists?
  2. what is trustworthy?
  3. what can be reused directly?
  4. which skill should take over next?

This skill exists because many quests do not start from a clean slate. Common non-blank starts include:

  • a baseline already exists and may already be confirmed
  • a main experiment has already finished and only needs durable recording or interpretation
  • analysis results already exist across child branches or worktrees
  • a draft or paper bundle already exists
  • reviewer comments already exist and the quest is really a revision/rebuttal task
  • the user explicitly says not to rerun from scratch

Do not treat these as edge cases. They are common research entry states.

Use when

  • startup_contract.launch_mode = custom and the profile implies existing work
  • the quest root already contains meaningful baseline, experiment, analysis, or paper assets
  • the user says:
    • “baseline 已经有了”
    • “不要重新复现”
    • “先整理现有结果”
    • “已有论文/草稿,先基于现有状态继续”
  • review materials exist but the current paper/result state is still unclear

Do not use when

  • the quest is genuinely blank and should start with ordinary scout or baseline
  • the active state is already well-normalized and the next anchor is obvious
  • the task is a pure non-research request

Non-negotiable rules

  • Do not rerun expensive work just because files exist. First decide whether a trust gap actually requires rerunning.
  • Do not fabricate missing durable records in order to make the quest look cleaner.
  • Do not mark an existing baseline as trusted unless the metric contract, source, and comparability are clear enough.
  • Do not mark an existing experiment as a durable main result unless it is genuinely the main run for an accepted idea line.
  • Do not silently import old drafts, plots, or notes as the active contract if they belong to a different idea line or branch line.
  • Do not lose provenance. If an artifact is reused, record where it came from and why it is trusted enough.
  • If the quest is really a review/revision task, route to rebuttal instead of pretending this is a normal fresh paper-writing pass.

Typical intake states

Classify the current quest into one or more of these buckets:

  • baseline_ready
  • baseline_partial
  • main_result_ready
  • analysis_ready
  • draft_ready
  • paper_bundle_ready
  • review_package_ready
  • unclear_state

Also classify every important asset by trust:

  • trusted
  • usable_with_verification
  • reference_only
  • stale_or_conflicting
  • missing_context

Primary truth sources

Use, in roughly this order:

  • startup_contract
    • especially launch_mode, custom_profile, entry_state_summary, review_summary, and custom_brief when present
  • quest continuity files:
    • brief.md
    • plan.md
    • status.md
    • SUMMARY.md
  • recent durable artifact state and quest snapshot
  • current workspace tree and visible quest files
  • prior memory cards and decisions
  • git history and current branch topology when needed
  • user messages

Do not trust chat recollection over durable state.

Workflow

1. Read startup intent first

Before touching the workspace, inspect:

  • startup_contract
  • the latest user message
  • recent quest status

Interpret these fields specially when present:

  • launch_mode = custom
    • do not force the standard full-research route
  • custom_profile = continue_existing_state
    • expect reusable assets and state normalization
  • custom_profile = revision_rebuttal
    • expect a paper/review package and likely handoff to rebuttal
  • custom_profile = freeform
    • prefer the custom brief over the default stage ordering
2. Retrieve memory before filesystem triage

Stage-start requirement:

  • run memory.list_recent(scope='quest', limit=5)
  • run at least one memory.search(...) using:
    • the quest title or central topic
    • any known baseline id or method name
    • any known paper title or venue short name
    • any known review keyword such as rebuttal, review, or revision

The point is to reuse prior route knowledge before re-auditing the same state from scratch.

Show full SKILL.md (507 more words)Show less
3. Inventory the quest state

Create or refresh a durable audit note using references/state-audit-template.md.

The inventory should cover:

  • baseline assets
  • main experiment assets
  • analysis assets
  • writing assets
  • review assets
  • git / branch / worktree state
  • missing or conflicting state

Useful places to inspect include:

  • artifacts/
  • baselines/
  • experiments/main/
  • experiments/analysis/
  • paper/
  • reviews/ or equivalent user-provided review folders

Do not over-read the entire tree. Read enough to classify the state and locate the likely trust anchors.

4. Trust-rank and reconcile

For each major asset, decide:

  • can it be trusted as-is?
  • does it need a light verification pass?
  • is it only reference material?
  • is it stale or conflicting?

Then reconcile it with the durable artifact layer:

  • existing reusable baseline:
    • artifact.attach_baseline(...)
    • then artifact.confirm_baseline(...) when trust is justified
  • existing main result:
    • artifact.record_main_experiment(...) only if the run is genuinely the accepted main run and the required fields can be filled honestly
  • existing analysis results:
    • if the campaign already exists, use artifact.record_analysis_slice(...) for each real finished slice that needs durable registration
  • existing outline:
    • artifact.submit_paper_outline(mode='select'|'revise', ...) when there is a real durable outline contract
  • existing paper bundle:
    • artifact.submit_paper_bundle(...) when the draft/package state is genuinely ready

If the evidence is insufficient for a durable backfill, record that insufficiency explicitly instead of inventing a cleaned-up history.

5. Choose the next anchor

After reconciliation, write one durable route decision with artifact.record(payload={'kind': 'decision', ...}).

Typical next anchors:

  • baseline exists but trust is incomplete -> baseline
  • baseline and route are ready, but no durable main result exists -> experiment
  • main result exists, but follow-up evidence is missing -> analysis-campaign
  • evidence is strong and writing should begin -> write
  • review package is active -> rebuttal
  • the quest is effectively complete or should pause -> finalize
6. Report and hand off

At the end of the intake pass, send one threaded artifact.interact(kind='milestone', ...) update that says:

  • what already exists and is trusted
  • what remains untrusted or incomplete
  • which next skill should take over
  • whether the user needs to provide anything else
  • artifacts/intake/state_audit.md
  • artifacts/intake/recommended_next_step.md
  • one decision artifact for the post-audit route
  • one or more repair/backfill artifact calls when justified

Companion skill routing

Open additional skills only when the audit indicates they are necessary:

  • baseline
    • when an existing baseline must be validated, repaired, confirmed, or waived
  • experiment
    • when the accepted route lacks a durably recorded main result
  • analysis-campaign
    • when the main result exists but the evidence boundary is still weak
  • write
    • when a trustworthy draft or outline should become the active writing line
  • rebuttal
    • when reviewer comments, revision requests, or meta-review materials define the real task
  • decision
    • when more than one next anchor remains plausible

Memory discipline

Stage-end requirement:

  • if the intake pass produced a durable route choice, trust judgment, or asset-reuse rule, write at least one memory.write(...)

Useful tags include:

  • stage:intake-audit
  • type:state-audit
  • type:route-handoff
  • type:reuse-rule
  • state:trusted
  • state:needs-verification

When the audit concerns a specific existing line, include identifiers when known:

  • baseline_id
  • idea_id
  • run_id
  • branch
  • paper_state

Success condition

intake-audit is successful when:

  • the quest's current state is understandable
  • the trustworthy reusable assets are explicit
  • the untrusted gaps are explicit
  • the next anchor is explicit
  • the system can continue without pretending the quest started from zero

© 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 1 other file (references) in skills/ds-intake-audit of OpenLAIR/dr-claw.

  • SKILL.md
  • references/state-audit-template.md

Open the folder on GitHubat commit d51b64e

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Works with

Categories

Questions about Ds Intake Audit

What does Ds Intake Audit do?

A skill your agent uses when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before…. Ds Intake Audit is an agent skill from OpenLAIR/dr-claw. Use when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before choosing the next anchor.

When should I use Ds Intake Audit?

Ds Intake Audit fits situations like: A quest does not start from a blank state and the agent must first audit; reconcile existing baselines; review materials before choosing the next anchor.

How do I install Ds Intake Audit in Claude Code?

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

How do I install Ds Intake Audit in Codex?

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

Can I use Ds Intake Audit 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-intake-audit -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-intake-audit, .gemini/skills/ds-intake-audit, .github/skills/ds-intake-audit and .opencode/skills/ds-intake-audit in your project.

What does Ds Intake Audit need to run?

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

Does Ds Intake Audit 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 Intake Audit 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 Intake Audit use?

Ds Intake Audit 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 Intake Audit use?

About 2.6k tokens (SKILL.md is roughly 10k 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 198 tokens, read only when the agent opens those files.

What are the alternatives to Ds Intake Audit?

Skills that share tags, products or a category with Ds Intake Audit: Finishing a Development Branch (obra/superpowers, 297k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Code Design Rationale Investigator (cursor/plugins, 11k stars) and Contributor-First PR Merge (HKUDS/OpenHarness, 16k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ds Intake Audit?

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.