scientific-agent-skills AGENTS.md

A set of repository instructions for K-Dense-AI's scientific-agent-skills project. It explains what belongs in the repository, how skills are organised, and how a new skill should be created.

In plain words
What is it for?
Checking whether a proposed skill fits the project, following its directory and title conventions, and creating or reviewing scientific-agent skills.
Why use it?
It gives contributors the project's scope and structure before they add or change a skill. This helps avoid broad or duplicate additions that do not fit the repository.

Instructions file for CodexOpenCode

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 instructions/k-dense-ai/scientific-agent-skills/agents-md
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills

Made for: Codex, OpenCode.

Per session 4,326 This file is loaded in full into every session.
When invoked 4,326 The same file — it is already loaded in full.
Security scan A 1 finding. Scan, not verified.
Origin original 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 $0.04326 $0.04326
Opus 5 $0.02163 $0.02163
Sonnet 5 $0.00865 $0.00865
Haiku 4.5 $0.00433 $0.00433

Measured yesterday against content hash f3a422683149, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

scientific-agent-skills AGENTS.md scanned grade A 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 yesterday.

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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

local links resolve, scripts parse, no `eval`/`exec`/`os.system`, no standard-library shadowing,
AGENTS.md · 373 lines

How it starts

The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Repository Guidance

This repository is a collection of Agent Skills for science and research. Every skill lives in its own directory under skills/ and must conform to the open Agent Skills specification.

Read this file before creating or changing a skill. CONTRIBUTING.md covers the same ground at more length, plus the pull-request process.

What belongs here

In scope: a narrow skill for one scientific package, database, platform, or research workflow — scanpy, depmap, benchling-integration, experimental-design.

Out of scope, and routinely declined:

  • General software-engineering or coding-judgment skills — they compete for selection on every task.
  • General infrastructure with a scientific example bolted on (a vector database, a cloud SDK) — accepting one implies carrying every competitor.
  • Broad "orchestrator" skills that route to other skills — they overlap every specialist by design.
  • A second provider for a service an existing skill already reaches.

The general-purpose skills that do exist are narrow output-format helpers (docx, pdf, pptx, generate-image, markdown-mermaid-writing). They are not precedent for broadening scope.

Layout

The repository root is an Agent Plugins 1.0.0 package: plugin.json plus the portable skills/ tree. Keep plugin.json valid against the Agent Plugins manifest schema, and keep its version identical to pyproject.toml [project].version. Do not add non-portable top-level fields to plugin.json (no inline MCP, hooks, or client-only keys — use mcp.json or a reverse-domain extensions namespace if those are ever needed).

plugin.json                 # Agent Plugins manifest (repo root)
skills/<skill-name>/
├── SKILL.md        # required
├── references/     # optional: long documentation, loaded only when needed
├── scripts/        # optional: executable helpers
└── assets/         # optional: templates and static resources

Read the full file on GitHub · 373 lines

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 · 373 lines · 4,326 tokens per session scan A f3a422683149

Subscribe to this mod's changes

scientific-agent-skills AGENTS.md is an instructions file published in the GitHub repository K-Dense-AI/scientific-agent-skills (40,390 stars, last pushed yesterday), licensed MIT. It adds 4,326 tokens to every session, about $0.0216 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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