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Full reimplementation of DnaSP 6 for population genetics analysis of aligned DNA sequences. Covers nucleotide diversity, haplotype statistics, neutrality tests (Tajima's D, Fu & Li's D*/F*, R2), linkage disequilibrium (D, D', R², ZnS, Za, ZZ), minimum recombination (Rm), mismatch distribution, InDel polymorphism, between-population divergence (Dxy, Da, fixed/shared sites), outgroup-based Fu & Li D/F tests (fuliout), the HKA multi-locus neutrality test (hka), the McDonald-Kreitman test (mk), Ka/Ks (dN/dS) via the Nei-Gojobori (1986) method (kaks), Fu's Fs test (fufs), the site frequency spectrum (sfs, folded and outgroup-unfolded), transition/transversion ratio (tstv), and codon usage bias - RSCU (Sharp & Li 1987) and ENC (Wright 1990) (codon). Accepts FASTA or NEXUS input; outputs DnaSP-compatible TSV and a Markdown report.

Rich downstream visualisation and reporting for bulk RNA-seq differential expression and scRNA marker/contrast outputs.

Summarise pre-computed differential expression results with ranked gene lists, biological themes, and publication-ready interpretation.

Deterministic CRISPR screen hit ranking from local guide-level count tables

Query the ClinPGx API for pharmacogenomic gene-drug data, clinical annotations, CPIC guidelines, and FDA drug labels

Classify germline variants from VCF/BCF files according to the ACMG/AMP 2015 28-criteria evidence framework and generate clinical-grade interpretation reports with per-variant evidence audit trails and ACMG SF v3.2 secondary findings screening.

Find clinical trials for a gene, variant, or condition from ClinicalTrials.gov + EUCTR, with FHIR R4 output

Semantic Similarity Index for disease research literature using PubMedBERT embeddings

Shotgun metagenomics profiling — taxonomy, resistome, and functional pathways

Ancestry decomposition PCA against the Simons Genome Diversity Project

Cell segmentation in fluorescence microscopy images. Supports Cellpose/cpsam (Cellpose 4.0) with additional backends planned. Produces segmentation masks, per-cell morphology metrics (area, diameter, centroid, eccentricity), overlay figures, and a report.md.

Bioconductor package discovery, workflow recommendation, setup inspection, and starter code generation grounded in official Bioconductor containers and BiocManager.

Meta-agent that routes bioinformatics requests to specialised sub-skills. Handles file type detection, analysis planning, report generation, and reproducibility export.

Search scientific papers via the BGPT MCP server and retrieve structured experimental data — methods, results, conclusions, quality scores, and 25+ metadata fields per paper.

Detect Neanderthal and Denisovan introgression segments from modern human genomes

Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.

Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer, RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.

When writing code against an external library, framework, SDK, or API, look up its CURRENT documentation with the context7 MCP tools instead of relying on memory. Use whenever you import/call a third-party package or hit an unfamiliar or fast-moving API.

Answer questions about the owner's services and domain using the approved DuckBug knowledge base through configured MCP tools.

[DEPRECATED] Renamed to polymarket-paper-trader. Run: npx clawhub install polymarket-paper-trader

Everyone's trading Polymarket with AI agents. Practice first — $10k paper money, real order books, zero risk. No wallet, no API keys, no real money. Then compete on the leaderboard.

Turn any source that describes how a kind of task gets done (a SKILL.md, a chat log, a runbook, plain prose) into a runnable Morph trace. Lift it into a DAG, write a contract per step, place inputs, and drive the full run lifecycle to verify it. Use whenever someone wants to make a trace from a source.

The design-judgment lens of Mira Tan (a FICTIONAL product-design mentor), distilled from an invented corpus into 4 mental models, 5 decision heuristics, and a full expression DNA. Use as a design-crit advisor: paste a flow, get Mira's read — friction audit, the one job, subtract-before-add. Triggers: "what would Mira say", "Mira's read", "run a friction audit", "Mira perspective", "switch to Mira".

Read before a js eval cell that installs a package, spawns a server or PTY, or starts a long run, and before any bun -e, script, server, CLI, test, bundle, or package-management work: this session's eval js kernel runs Bun 1.4+ (this skill is present only when it does). Bun 1.4 ships builtins that replace 15+ npm deps — check here BEFORE installing sharp, puppeteer/playwright (scraping), marked, node-cron, node-pty, concurrently, serve-static, tar, json5, fast-xml-parser, string-width. Triggers: eval js, bun, Bun.serve, bun test, bun build, bun install, bun run, image resize, headless browser, markdown render, cron, PTY.

MUST read before generating images. Prompt-crafting guide for gpt-image-2.5 covering tool routing (native image_generation server tool vs the generate_image tool), model and quality selection, prompt structure, exact text rendering, reference-image editing, transparent assets, output formats, and multi-turn refinement.

Release, publish, CalVer tag push, npm publish, 배포, 릴리즈 for senpi. Use for the canonical release flow, GitHub tag/release publication, and npm publishing.

Sync a fork branch with an upstream remote using a history-preserving merge. Use this whenever the user says /merge-upstream, merge upstream, sync upstream, sync fork, or wants upstream changes integrated without rebasing or force-pushing.

Run a single manual tmux-based QA scenario for the todo continuation feature against the real CLI (./pi-test.sh) in an interactive TUI. Captures scrollback, asserts deterministic pass/fail count markers, and cleans up test fixtures. Use only for the manual-qa milestone features.

Implements a single feature in the pi-mono `todotools` builtin extension work. Use for refactoring, continuation runtime, config resolver, prompt builder, test authoring, golden snapshots, CHANGELOG entries, and harness helpers. Does NOT do manual tmux QA.

Full development pipeline: create GitHub issue, plan, implement with agent team, verify (build+test), commit, push, close issue. Use when you need to implement a feature or fix end-to-end.

Automatically generate production-ready Skills for any software, API, CLI tool, library, workflow, or service. Use this skill whenever the user wants to create a skill from scratch for a target application, convert an existing tool into an agent-native skill, generate skills for multiple platforms (Claude Code, OpenClaw, Codex), or automate the full skill creation pipeline including analysis, design, implementation, testing, optimization, and multi-platform packaging. Also use when the user mentions "skill-anything", "generate a skill for", "make a skill from", "skillify", or wants to turn any software into an agent-ready skill. Even if they just say "create a skill for X" where X is any tool or API, this skill should trigger.

Build evidence-backed day trips, multi-day itineraries, and efficient stop sequences with Google Maps data through the standalone CLI. Use when the desired output is a practical travel plan, not merely a route or list of places.

Analyze Google Business Profile visibility, geographic grid rankings, local search keywords, and nearby competitors through the standalone Google Maps CLI. Use for local SEO or location-strategy decisions, not ordinary place recommendations.

Search places, resolve addresses, compare routes, inspect neighborhoods, and retrieve geographic or environmental facts through the standalone @cablate/mcp-google-map CLI. Use for concrete location questions; use the travel-planning or local-seo Skill for those specialized outcomes.

Optimize title tags, meta descriptions, Open Graph, and Twitter cards for maximum click-through rate. Generates multiple A/B test variations with character counting and SERP preview. Use when asked to "optimize title tag", "write meta description", "improve CTR", "Open Graph tags", "fix my meta tags", "social media preview", "my click rate is low", "title optimization", or any meta tag task.

Discover, analyze, and prioritize keywords for SEO and GEO content strategies. Identifies high-value opportunities based on search volume, competition, intent, and business relevance. Generates topic clusters and content calendars. Use when asked to "find keywords", "keyword research", "what should I write about", "keyword analysis", "find me topics to write", "search volume", "keyword difficulty", "content ideas", or any keyword discovery task.

Entry point + orchestrator for the recomby-geo GEO (Generative Engine Optimization) workflow on OpenAI Codex CLI. Use when the user wants to run any stage of the GEO pipeline on a client folder — intake, visibility audit, content-gap analysis, content brief, draft production, distribution, or monthly re-audit — or asks to "run GEO", "audit AI search visibility", or "GEO this client". Codex has no bare slash commands, so this skill is how the 7 stages (that Claude Code runs as /01-intake … /07-reaudit) are driven on Codex. It routes to the per-stage specs in this plugin's commands/ and enforces the orchestration rules. Does not auto-fill expert content — the human-in-loop brief checkpoint is the moat.

Write SEO-optimized blog posts, landing pages, and long-form page copy following Google's E-E-A-T and Helpful Content guidelines. Handles new content creation from a keyword, topic, or brief, and full-page rewrites. Use when asked to "write a blog post", "create a landing page", "write content about X", "content for keyword X", "draft an article", "blog post about", "landing page for", "service page", "product page copy", "rewrite this page", "how-to guide", or "listicle". NOT for analyzing/auditing existing content (seo-geo-optimizer, content-quality-auditor), title tags or meta descriptions (meta-tags-optimizer), or keyword expansion (keyword-research) — those skills own their triggers.

Direct and implement programmatic videos from a natural-language sentence: brief, shots, style, timeline, assets, engine-specific coding prompts, draft, review and repair. Use when asked to make a video, product launch, explainer, Reel/Short, kinetic typography or docs-to-video with HyperFrames, Remotion or Motion Canvas, or to improve an existing code-video project. 一句话视频制作与导演工作流。

Enhancement-overlay decision protocol for STREAMING the output of long-running LLM / agent runs from the *backend*, not just wiring a typing animation in the UI. Activates when a coder agent must stream final tokens to a chat client, surface intermediate agent steps (which tool, which node, partial reasoning), emit custom tool-progress events, choose a transport (SSE vs WebSocket), or decide what to do when the client disconnects mid-stream. The langchain / langgraph skills mention stream modes but stop at "you can stream"; this skill encodes *what to stream, over what transport, and how to fail safely*.

Tool skill — the *first move* in any LM-debugging session: dump the actual rendered prompt the framework sent to the model, before changing anything else. Activate when an LM call produced an unexpected output (wrong answer, schema violation, refusal, truncation, cost spike, latency spike, infinite loop, "model got dumber after upgrade"). The skill enforces a 30-second inspect step BEFORE any prompt edit, model swap, retry, or temperature tweak. Cross-framework cheat sheet: DSPy `inspect_history`, LangGraph `get_state_history`, CrewAI `step_callback`, LangChain `set_debug`/`set_verbose`, Aider `/diff`+`--verbose`, raw OpenAI/Anthropic via `OPENAI_LOG=debug`/`ANTHROPIC_LOG=debug` or HTTPX event hooks. Do NOT activate for first-time prompt authoring, exploratory prompt design, or non-LM bugs.

Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).

Designs multiscale chunking for RAG by embedding small units for retrieval precision and returning larger context for synthesis. Use when fixed-size chunks either lose surrounding context or dilute relevance in long documents, manuals, filings, or codebases. Covers sentence-window and parent-child or auto-merging strategies, base chunk sizing, and evaluation. Do not use when a single chunk scale already meets retrieval and generation needs.

ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use CoT, reasoning type, ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.

Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

Decision protocol for building, debugging, and operating LangGraph-based agent systems. Activates when a coder agent is asked to design a stateful LLM workflow, add human-in-the-loop, choose a multi-agent pattern (supervisor / swarm / hierarchical), pick a checkpoint backend, or migrate a fragile chain into a durable graph. LangGraph is positioned by its maintainers as a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents" — this skill encodes the *when* and *why*, not the API.

Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.

Galaxy tool discovery, intelligent recommendation, and execution — 8,000+ bioinformatics tools from usegalaxy.org with multi-signal scoring and workflow suggestions

Operator toolkit for nf-core/pacsomatic matched tumor-normal workflows from BAM inputs. Use this skill when the user needs to validate run inputs, generate pacsomatic-compliant samplesheets, prepare reproducible Nextflow launch artifacts, run locally or submit to schedulers (LSF/Slurm/PBS/SGE), and triage execution failures. Triggers on requests to run pacsomatic, prepare launch commands/scripts, perform dry-run checks, or troubleshoot pipeline startup and scheduler submission errors.

Render an interactive, self-contained HTML companion for a GEO content brief (04-content-brief) or a publish-ready draft (05-production), so a NON-technical client reviewer (founder, organizer staff, the domain expert filling slots) can fill REQUIRED-FILL slots, leave section-level comments, and approve/return work in the browser instead of editing Markdown. Use when a brief or draft needs to go to a client/expert for review, or when building the briefs/index.html entry page for a client folder. The reviewer's input comes back as a JSON file that 04-content-brief Step 9 ingests. Visual quality is delegated to the frontend-design skill.

NCBI GEO access via GEOparse and E-utilities. Search by keyword/organism/platform, download GSE series matrices, parse GPL annotations, extract GSM metadata, load expression matrices into pandas. For single-cell use cellxgene-census; for multi-DB access use gget-genomic-databases.

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.

Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster format. For standard research posters, use latex-posters instead. This skill provides modern web-based poster design with responsive layouts and easy visual integration.

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.