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discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or compounds, investigating an anomaly, or deciding what experiment or analysis to run next. Triggers on "make discoveries", "explore this data", "find something in", "what's in this dataset", "research this question", "generate hypotheses", "why is this result happening".
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
// README
Biodiscovery Skills
Inspired by Intuition Pumps and Other Tools for Thinking by Daniel Dennett, this repo provides almost every thinking tool humanity has, adjusted for AI agents to make scientific breakthroughs.
Tools like Claude Science and others focus on exact methods and tool usage. No one addresses the problem of scientific thinking — which is the most important thing in doing science and making scientific discoveries.
Focused on the life sciences — the operators are domain-general, but the vocabulary, the worked failure modes and the literature tooling are aimed at biological discovery.
Contributions are welcome.
Just give this repo to your AI agent, and will set it up for you. Don't forget about paperclips registration.
Requirements
Paperclip — required. The discovery-director skill delegates
literature search to it, and returns insights rather than reading lists. Install with:
curl -fsSL https://paperclip.gxl.ai/install.sh | bash
That installs to ~/.paperclip/, signs you in, and registers it with your agent. Alternatives:
npx gxl-paperclip, or point your client at the hosted MCP server https://paperclip.gxl.ai/mcp.
Non-interactive auth uses an API key from the keys dashboard:
export PAPERCLIP_API_KEY='gxl_...'
Everything else in both skills runs with no external dependencies.
Install
Claude Code — plugin (recommended)
/plugin marketplace add dekan-aleksandr/biodiscovery-skills
/plugin install biodiscovery-skills@biodiscovery-skills
Any agent — one command
curl -fsSL https://raw.githubusercontent.com/dekan-aleksandr/biodiscovery-skills/main/install.sh | bash
Copies both skills into ~/.claude/skills/. Pass a path to install elsewhere:
... | bash -s -- .claude/skills for project-level.
Manual
git clone https://github.com/dekan-aleksandr/biodiscovery-skills.git
cp -r biodiscovery-skills/skills/* ~/.claude/skills/
Or just point your agent here
Read https://raw.githubusercontent.com/dekan-aleksandr/biodiscovery-skills/main/skills/discovery-director/SKILL.md
and follow it. Load skills/discovery-toolbox/SKILL.md on demand when you hit its triggers.
Skills
discovery-director
The operating stance for an agent doing open-ended research: explore broadly → discriminate → go deep on exceptional leads → confirm independently.
Covers how to hold the research map while delegating work, how to treat literature (what humans tested, not what nature is doing), what counts as staying on the question versus drifting, how to read a detection floor as a property of the instrument rather than of reality, and seven discovery modes — orient, question, explore, discriminate, depth, confirm, accumulate.
Fires on: open-ended research, exploring a dataset for findings, hypothesis generation, screening a large candidate space, investigating an anomaly, deciding what to run next.
discovery-toolbox
~90 operators in nine routed sections, loaded on demand — never wholesale. Route to the smallest relevant section, activate 1–3 operators, return to the research loop.
| A Visual reasoning | shape, heterogeneity, ranking, individual observations |
| B Instrument / detectability / information | new screen, weak or null result, lossy pipeline |
| C Search / reframing | ideas repeat, experiment space frozen, yield falling |
| D Causal identification | causal claims, natural experiments, covariate adjustment |
| E Competing explanations | a lead may deserve real resources |
| F Observation / selection process | filtering, missingness, curation, provenance |
| G Pipeline / artifact diagnosis | spectacular result, unexplained change, replication failure |
| H Allocation / depth | breadth vs depth, where to spend |
| I Confirmation / hygiene | a lead may become a claim |
Fires on: a null or weak screen, a result that looks too good, stalled search, a causal claim, an unexplained anomaly, a suspected pipeline bug, a lead about to be escalated.
docs/TOOL_INVENTORY.md — the long-form list of tools behind the toolbox,
with sources.
License
MIT
// HOW IT'S BUILT
KEY FILES