Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsnpx agentmods add skills/k-dense-ai/scientific-agent-skills/autoskillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/autoskill)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/autoskill"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/autoskill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/autoskill"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/autoskill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 4 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Privilege Escalation · line 68 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00088 | $0.03909 |
| Opus 5.5 | $0.00035 | $0.01564 |
| Sonnet 5.5 | $0.00018 | $0.00782 |
| Haiku 4.5 | $0.00009 | $0.00391 |
Grade B, and why
autoskill scanned grade B with 1 finding 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 14d ago.
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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
# # if xcodebuild plug-ins error: sudo xcodebuild -runFirstLaunch Copies of this mod
8 near-identical copies found in the catalogue:
- autoskill — 97% identical, 127 lines differ
- autoskill — 97% identical, 127 lines differ
- autoskill — 97% identical, 127 lines differ
- autoskill — 95% identical, 125 lines differ
- autoskill — 89% identical, 142 lines differ
- autoskill — 89% identical, 101 lines differ
- autoskill — 89% identical, 142 lines differ
- autoskill — 88% identical, 141 lines differ
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autoskill
Requires a running screenpipe daemon. This skill has no alternate data source — it reads exclusively from the local screenpipe HTTP API (default
http://localhost:3030). If the daemon isn't running,run()raisesScreenpipeUnreachablewith install instructions.
Network access & environment variables. This skill makes authenticated HTTP requests to (a) the user's local screenpipe daemon on loopback, and (b) the user-configured LLM backend — one of
http://localhost:1234/v1(LM Studio, default),https://api.anthropic.com(opt-in Claude), or a user-supplied BYOK Foundry gateway. The adapters readSCREENPIPE_TOKEN,LM_API_TOKEN,ANTHROPIC_API_KEY, andFOUNDRY_API_KEYfor the corresponding configured service. HTTPX also honors its standard proxy and CA environment settings; sentence-transformers uses Hugging Face cache/download settings. Opt-in cloud backends receive redacted cluster summaries and matched skill descriptions. Embedding-model installation may also download public model files; local inference does not imply zero network access.
Overview
Turn the user's own workflow history — captured passively by the local screenpipe daemon — into new skills. This skill is on-demand: the user invokes it with a time window, it queries screenpipe's local HTTP API, clusters repeated workflow patterns, compares each pattern against the existing skills in this repo, and produces a staged folder of proposals the user can review, edit, and promote.
When to Use This Skill
Invoke this skill when the user asks to:
- "Analyze my last 4 hours / day / week and propose new skills."
- "Look at what I've been doing and tell me what's not covered yet."
- "Draft a skill from my recent workflow."
- "Find composition recipes for workflows I repeat."
Do not invoke it for one-off questions about screenpipe itself, for real-time screen queries, or without an explicit user request — the skill analyzes sensitive local content and must stay explicitly user-triggered.
What ships with it
15 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.
- .gitignore 34 B
- config.yaml 2.1 KB
- references/api-contracts.md 7.4 KB
- references/https-proxy.md 2.2 KB
- references/screenpipe-config.yaml 1.4 KB
- scripts/autoskill.py 1.1 KB runs code
- scripts/backends.py 5.1 KB runs code
- scripts/cluster.py 1.7 KB runs code
- scripts/doctor.py 4.5 KB runs code
- scripts/fetch_window.py 2.8 KB runs code
- scripts/match_skills.py 1.2 KB runs code
- scripts/promote.py 1.5 KB runs code
- scripts/redact.py 1.6 KB runs code
- scripts/run.py 7.4 KB runs code
- scripts/synthesize.py 2.7 KB runs code
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.
- 14d ago Changed · +14 lines · -35 tokens per session d7fc7db09464
- 15d ago Changed · +3 lines 951d70907655
- 1mo ago Changed · +17 lines 6c342b90d218
- 1mo ago First seen · 234 lines · 123 tokens per session scan B 50daf88d5a18
autoskill is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 3,909 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Builds personalized demo assets for top prospects using the founder's product API/MCP/SDK. Researches prospect, proposes demo concepts, builds working prototype, tests it, and generates comparison report with live demo link.
meta-ad-manager
Front door and loop owner of a Meta (Facebook/Instagram) ad harness. Reads the brand's ads/ docs, works out which campaign the user means and which stage it is at, hands off to the stage skill that runs it (intake, strategy, creative, launch, checks, fixes, answers), and writes every stage change back to the docs. Use…
inbound-lead-enrichment
Fills in missing data for inbound leads — researches the company, identifies the person's role and seniority, finds other stakeholders at the company, checks for existing CRM relationships, and updates the lead record. Produces enriched lead data ready for qualification or outreach. Tool-agnostic.