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概要と使いどころ
4 business growth agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Customer success (health scoring, churn), sales engineer (RFP), revenue operations (pipeline, GTM), contract & proposal writer. Python tools (stdlib-only).
日本語の概要は準備中です。原文の説明を表示しています。
Run the full design-doc-writer and design-doc-reviewer loop until consensus. Produces a polished design document with a PR plan.
日本語の概要は準備中です。原文の説明を表示しています。
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.
日本語の概要は準備中です。原文の説明を表示しています。
Create, refresh, and rewrite PR titles and descriptions following Sentry conventions. Use when opening a PR, writing or updating a PR title/body/description, refreshing an existing PR after material changes, or preparing branch changes for review.
日本語の概要は準備中です。原文の説明を表示しています。
Keep iterating on code changes until the tests pass, the build succeeds, or linting is clean. Runs in a tight loop of fix → run → check → repeat. Use when you want the agent to autonomously grind through test failures or build errors.
日本語の概要は準備中です。原文の説明を表示しています。
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".
日本語の概要は準備中です。原文の説明を表示しています。
Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
日本語の概要は準備中です。原文の説明を表示しています。
Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for animation, with awareness of current Kaleido static-export model (post-orca-EOL), HTML file-size bloat, and the limits of interactive-only output for journal submission. Use when producing zoomable/hoverable plots for notebook EDA, supplementary HTML, dashboards, or animated time-course / iteration visualizations.
日本語の概要は準備中です。原文の説明を表示しています。
Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).
日本語の概要は準備中です。原文の説明を表示しています。
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).
日本語の概要は準備中です。原文の説明を表示しています。
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
日本語の概要は準備中です。原文の説明を表示しています。
Use to process automated PR review feedback (Copilot, Greptile, Codex) and iterate until all AI threads are addressed, checks are green, and the PR is merge-ready. Trigger when the user says "address review feedback", "recycle reviews", or references an automated reviewer.
日本語の概要は準備中です。原文の説明を表示しています。
Runs Charted Coding iterations in a loop — route via charted-continue, batch red or green work for an entire test file, commit, then repeat until the PR is complete. Infers design doc and PR number when omitted. Use when the user invokes charted loop, wants batch TDD for a PR test file, or asks to continue charted work with commits.
日本語の概要は準備中です。原文の説明を表示しています。
Review hot paths, allocations, iteration, caching, event frequency, and correctness-preserving optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Turn product requirements into scoped tasks with dependencies and acceptance criteria. Use for implementation planning and prioritization.
日本語の概要は準備中です。原文の説明を表示しています。
Plan AI pilot programs with success criteria. TRIGGERS - Use when user needs help with ai-pilot-plan related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Smart crop to face, remove the background, and convert to WebP for a clean user avatar.
日本語の概要は準備中です。原文の説明を表示しています。
Apply a text watermark to a photo using layer-based image composition for brand protection and copyright.
日本語の概要は準備中です。原文の説明を表示しています。
Smart crop a product photo to Etsy's recommended 2000×2000px square format and export as JPEG.
日本語の概要は準備中です。原文の説明を表示しています。
Use AI detection to smart-crop individual portraits from a group photo.
日本語の概要は準備中です。原文の説明を表示しています。
Resize an image, add a branded text watermark, and convert to WebP in a two-step pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Enhance and standardize a property listing photo with auto-contrast, sharpening, and consistent sizing.
日本語の概要は準備中です。原文の説明を表示しています。
Extract invoice data and generate a formatted PDF summary in a single pipeline.
日本語の概要は準備中です。原文の説明を表示しています。