drclaw-skill-library

A library of more than 170 skills that routes research, machine-learning, experiment, paper, data, model-training, evaluation, deployment, and scientific-communication tasks to matching helpers.

In plain words
What is it for?
Use it to match tasks with skills for academic research, machine-learning engineering, experiments, data work, model training, evaluation, deployment, and scientific communication.
Why use it?
It helps choose a relevant specialist workflow when a task spans several technical areas or no specific skill was selected. This reduces manual searching through the available skill library.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/openlair/dr-claw/drclaw-skill-library
Any agent
npx skills add OpenLAIR/dr-claw --skill drclaw-skill-library
Clone the repo
git clone --depth 1 https://github.com/OpenLAIR/dr-claw

Made for: Claude Code, Codex.

Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 499 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00100 $0.00499
Opus 5 $0.00050 $0.00249
Sonnet 5 $0.00020 $0.00100
Haiku 4.5 $0.00010 $0.00050

Measured yesterday against content hash ae8bc6d49c43, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

drclaw-skill-library scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/query_library.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

bootstrap/codex/skills/drclaw-skill-library/SKILL.md · 34 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 34 lines · 100 tokens per session scan A ae8bc6d49c43

Subscribe to this mod's changes

drclaw-skill-library is a skill published in the GitHub repository OpenLAIR/dr-claw (1,050 stars, last pushed 6d ago), with no licence file. It adds 100 tokens to every session and 499 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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memory-manager

Manage long-term AI R&D memory: retrieval, writeback, promotion, and shared export. TRIGGER when: run bootstrap, each new user turn, each execution batch, significant failure, replan, high-resource action, long-action resume, final report handoff, or compaction markers detected (Compact/压缩/Summary). DO NOT TRIGGER…

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experiment-execution

Execute AI/ML experiments locally or remotely with environment, runtime, and logging controls. Prefer invoking via research-workflow. TRIGGER when: user asks to run/launch/start/resume/monitor a training job, evaluation, or benchmark, or a plan is ready for execution, or experiment needs rerun/recovery. DO NOT TRIGGER…

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project-context

Initialize and maintain per-project runtime context (env, secrets, snapshots). Prefer invoking via research-workflow. TRIGGER when: new run needs env setup, preflight before experiment/eval, runtime fields missing (paths, API keys, GPU config, proxy), run snapshot needed, or shared-memory needs project config. DO NOT…

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deep-research

Deep evidence-first research with broad discovery, verification, and traceable citations. Prefer invoking via research-workflow. TRIGGER when (MANDATORY — you MUST invoke this skill, no exceptions): user message contains ANY of these keywords or synonyms — 调研/研究/对比/综述/文献/证据/机制/根因/为什么/可行性/路线图/分析/探索, or…

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run-governor

Govern run-level execution policy: mode selection, durable run tracking, long-action watch/resume policy, stage reporting, and safety allowances. TRIGGER when: starting a non-trivial research task (set mode + runid), switching local/remote target, creating a new run, or mode-aware policy decisions needed. DO NOT…

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