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概要と使いどころ
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.
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Compile your local stellar-build usage traces into sharper skills. Use when the user says "optimize my skills", "optimize-skills", "improve my skills from usage", "learn from my traces", "stellar-loop optimize", or wants the learning loop to refine installed skills based on how they've actually been used. DSPy-style prompt optimization over the local trace store at ~/.stellar-build/traces — fully local, reversible.
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Build AI tutoring systems with adaptive learning. TRIGGERS - Use when user needs help with ai-education-tutor related tasks.
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Design accommodation plans for learning differences. TRIGGERS - Use when user needs help with accommodation-plan related tasks.
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Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.
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Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.
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Evaluate learning materials and technology for WCAG, Section 508, and accessibility compliance with remediation recommendations
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Help job seekers use Otameshi Tenshoku to prepare profiles and applications, clarify trial conditions, plan learning visits or paid trial work, and reflect on the experience. Use when a candidate wants to apply for, prepare for, negotiate the scope of, or review an otameshi work experience; do not use to predict whether they will be hired or to judge their aptitude, to state fees, contract terms, or service conditions from memory, or to submit an application, update a profile, or send a message on their behalf. Employer-side requests belong to the employer skills (trial-work-design, trial-listing-draft, applicant-reply and others).
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Design what an employer will have a candidate do during an Otameshi Tenshoku trial: turn a business problem into a bounded paid assignment or an unpaid learning visit, with scope, completion criteria, candidate hours, company staff hours, duration, compensation and change conditions, and check the planned cost against the budget. Use when a hiring manager, recruiter or small-business owner wants to decide what work to offer, how much time it takes, what it costs, or how to adjust a plan when materials are late or the scope grows; do not use to write the public listing (trial-listing-draft), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post, contact or contract on the user's behalf.
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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g
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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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Synchronize recent research learnings across all affected theses, sector notes, and macro documents. Use after a research session or when user says "sync", "propagate", or "update everything".
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Apply continuous improvement principles to iteratively enhance processes, code quality, team practices, and system performance through small, incremental changes. Use when improving development processes, optimizing workflows, reducing waste, enhancing code quality over time, implementing retrospective learnings, or fostering a culture of continuous improvement.
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YC Office Hours — two modes. Startup mode: six forcing questions that expose demand reality, status quo, desperate specificity, narrowest wedge, observation, and future-fit. Builder mode: design thinking brainstorming for side projects, hackathons, learning, and open source. Saves a design doc. Use when asked to "brainstorm this", "I have an idea", "help me think through this", "office hours", or "is this worth building". Proactively invoke this skill (do NOT answer directly) when the user describes a new product idea, asks whether something is worth building, wants to think through design decisions for something that doesn't exist yet, or is exploring a concept before any code is written. Use before /plan-ceo-review or /plan-eng-review. (gstack)
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Route communication modes, learning workflows, retrospectives, output discipline, and perspective shifts to focused productivity skills.
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Write or revise AI-learning-notes article pages in "寫文章模式" when the user asks for articles, technical articles, blog posts, bilingual versions, article formatting consistency, shared article-base alignment, responsive layout, summary/TOC decisions, contrast, or publishing-flow compatibility.
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Promote project-specific memories into strict, small global memory only when they are cross-project, stable, useful, safe, and worth the global context cost. Load when the user says make this global, remember across projects, save this globally, or promote this learning.
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Capture durable project memory from work, debates, debugging discoveries, learned conventions, deferred options, and session outcomes. Load when the user says remember this, save this learning, record what happened, update project memory, or preserve context for future agents.
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Capture actionable learnings that emerge during conversation — when the agent or user discovers that a skill, a set of skills, or a process needs to be updated based on what's happening in the current chat. Sub-skill of the learn-from orchestrator. Load when the user says "we should update the skill for this", "this should be a skill rule", "add this as a gotcha", "the skill should know about this", "update the process for this", "remember this for next time", "this is important for the skill". Also triggers when the agent notices a skill's guidance was wrong or incomplete, a process step failed or was unnecessary, a new pattern emerged, a guardrail was missing, a workaround became a pattern, or a debugging session reveals a gap.
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Analyse experiment results, run validity checks (SRM, exposure parity, data integrity, novelty/primacy), interpret causally, make a ship/iterate/kill decision against the pre-declared rule, and append to cumulative learnings. Forces honest readouts — strips significance claims from underpowered or peek-violating tests; never lets directional results masquerade as causal wins. Load when results exist, or when the user says "read out this experiment", "analyse the test", "did the test win", "interpret the results", "what did we learn", "ship or kill", or when the experimentation orchestrator routes here.
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Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.
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Analyzes your recent Codex chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
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