Research skills

Browse reusable Agent Skills, each with a clear purpose and practical guidance.

network-tox-docking-research-planner

Generates complete network toxicology + molecular docking research designs from a user-provided toxicant and disease/phenotype. Always use this skill when users want to investigate how an environmental toxicant, endocrine disruptor, heavy metal, food contaminant, pharmaceutical residue, or consumer product chemical may contribute to a disease through shared molecular targets, hub genes, pathways, and docking evidence. Trigger for:"network toxicology study", "toxicology mechanism paper", "target prediction + PPI + docking", "environmental pollutant and disease mechanism", "hub genes and docking for toxicant", "Lite/Standard/Advanced toxicology plan", "CTD + SwissTargetPrediction + GeneCards + STRING", "CB-Dock2 docking study", "triclosan/BPA/cadmium/PFAS + disease". Also triggers for Chinese phrasings:"网络毒理学研究设计"、"毒物机制论文"、"靶点预测+PPI+对接"、"环境污染物与疾病机制". Trigger even for casual phrasings like "I want to study how chemical X affects disease Y" or "help me design a toxicology paper". Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.

1.45k repo starsObserved in 1 repos
Research
GitHub

novelty-vs-feasibility-assessor

Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.

1.45k repo starsObserved in 1 repos
Research
GitHub

primary-plan-recommender

Compares multiple study-route options for the same biomedical research question and recommends one primary plan, while explicitly explaining why alternative routes are secondary, premature, weaker, or dependency-heavy. Always use this skill when the user already has a reasonably defined question but is unsure which main study route should anchor the project. Focus on plan comparison, route selection, dependency awareness, and primary-plan justification rather than full protocol drafting.

1.45k repo starsObserved in 1 repos
Research
GitHub

reference-retrieval-skill

Based on user input, directly find relevant literature or automatically construct PubMed Boolean search queries to retrieve and filter references suitable for citation. Applicable for quickly finding high-quality evidence on specific topics and completing reference lists.

1.45k repo starsObserved in 1 repos
Research
GitHub

knowledge_graph_skill

Skill for searching and evolving the SQLite-backed Knowledge Graph. Use this when you need structured fact/concept/link search across one or more teams, NPCs, or directory scopes. The Knowledge Graph (KG) is stored in the application's database (not YAML). It is scoped by (team_name, npc_name, directory_path). Facts and concepts carry generation numbers and origin tags. Search methods (choose the right one): 1. Keyword search — fast substring match over fact statements. `kg_search_facts(engine_or_kg, "keyword")` → List[str] 2. Embedding search — semantic cosine similarity via vector embeddings. `kg_embedding_search(engine_or_kg, query="...", embedding_model="nomic-embed-text", embedding_provider="ollama", similarity_threshold=0.6, max_results=20)` → List[dict] with 'content', 'type', 'score' 3. Link search — graph traversal (BFS/DFS) starting from keyword-matched seeds. `kg_link_search(engine_or_kg, query="...", max_depth=2, breadth_per_step=5, strategy="bfs", max_results=20)` → List[dict] with 'content', 'type', 'depth', 'path', 'score' 4. Hybrid search — combines keyword + embedding + link, boosting results found by multiple methods. `kg_hybrid_search(engine_or_kg, query="...", mode="all", max_depth=2, similarity_threshold=0.6, max_results=20)` → List[dict] with 'content', 'type', 'score', 'source' Graph evolution (use sparingly, usually in background): - `kg_initial(content, model, provider)` — build a new KG from text - `kg_evolve_incremental(existing_kg, new_content_text, ...)` — add content - `kg_sleep_process(existing_kg, model, provider)` — prune/deepen/consolidate - `kg_dream_process(existing_kg, model, provider, num_seeds)` — speculative synthesis When a user asks a question that spans facts, concepts, and their relationships, prefer hybrid search. For pure semantic similarity without graph structure, use embedding search. For exploring connected neighborhoods, use link search with BFS.

1.43k repo starsObserved in 1 repos
Research
GitHub

knowledge_sememolution_skill

Skill for population-based Knowledge Graph evolution via Sememolution. Use this when the user wants creative cross-domain synthesis, speculative reasoning, or when a single KG search might be too narrow. Sememolution maintains a population of KG "individuals". Each individual has its own graph (different facts, concepts, links) and its own genome controlling how it searches and evolves. Core genome parameters: - `lambda_depth` — Poisson rate for search traversal depth - `lambda_breadth` — Poisson rate for search breadth per step - `sleep_ops` — which refinement ops to apply during sleep - `dream_probability` — chance of speculative synthesis per cycle Workflow: 1. Create a population: `SememolutionPopulation(model, provider, population_size=100, sample_size=10)` 2. Initialize: `pop.initialize()` 3. Assimilate text: `pop.assimilate_text(chunk)` — each individual absorbs it differently 4. Sleep cycle: `pop.sleep_cycle()` — each individual prunes/deepens independently 5. Query and rank: `pop.query_and_rank(question)` — sample individuals, each searches its own graph with Poisson-sampled depth/breadth, generates a response, and responses are ranked. Winners get fitness bumps. 6. Evolve: `pop.evolve_generation()` — tournament selection, crossover, mutation. When to use this: - The user asks open-ended "what if" or "how might X relate to Y" questions - You need diverse perspectives on the same knowledge corpus - You want to discover non-obvious connections across domains - Standard KG search returns shallow or overly literal results Important: this is computationally expensive. Only invoke after checking whether standard keyword/embedding/hybrid search is sufficient.

1.43k repo starsObserved in 1 repos
Research
GitHub

mcp-docs

Fetch live MCP specification and docs from modelcontextprotocol.io. Training data may be outdated — default to using this skill when the query touches MCP protocol in any way. Trigger for: implementing or debugging MCP features (sampling, elicitation, completion, tools, resources, prompts, transports); SEPs or spec requirements; MCP transport behavior (SSE, Streamable HTTP, reconnection, retry); authorization/OAuth; conformance test failures; capability negotiation; message schemas. Skip only for pure Kotlin, build system, or refactoring tasks with zero MCP protocol dependency. When in doubt, trigger.

1.41k repo starsObserved in 3 repos
Research
GitHub

crypto

Get real-time cryptocurrency prices. Use when users ask about Bitcoin, Ethereum, or other crypto prices and market data.

1.41k repo starsObserved in 2 repos
Research
GitHub

spec-kitty-git-workflow

Understand how Spec Kitty manages git: what git operations Python handles automatically, what agents must do manually, worktree lifecycle, auto-commit behavior, merge execution, and the safe-commit pattern. Triggers: "how does spec-kitty use git", "worktree management", "auto-commit", "who commits what", "git workflow", "merge workflow", "rebase WPs", "worktree cleanup", "safe commit". Does NOT handle: runtime loop advancement (use runtime-next), setup or repair (use setup-doctor), mission selection (use mission-system).

1.41k repo starsObserved in 1 repos
Research
GitHub