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概要と使いどころ
Run a read-only adversarial Kimi challenge review that questions assumptions, design choices, and tradeoffs. Use when the user wants pushback on whether the approach is right, not a defect-only review.
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Research a topic and create a new note in the vault. Use this skill whenever the user wants to learn about a topic they don't know yet, investigate a question, explore an idea, or asks "what is X", "research X", "tell me about X", "I want to understand X", "look into X". Also triggers on /research. This is for BROAD research (web + vault) — for academic papers specifically, use /paper-discover instead.
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Conversational profile builder. Produces validated JSON config files for any rootnode Skill that consumes profile-shaped configuration (handoff trigger check, mode router, critic gate, future Skills). Reads a target JSON Schema (draft-07), conducts a progressive-depth interview using plain-language questions, validates answers, and writes the resulting JSON to a destination path. Schema-agnostic — works with any profile schema conforming to draft-07. Use when a user needs to create, clone, or revise a profile and you do not want them hand-editing JSON. Trigger on: "build me a profile," "create a profile," "I need a profile for X," "set up my profile," "edit my profile," "clone the X profile," "walk me through making a profile." Do NOT use when the user already has a working profile and only wants to invoke the consuming Skill, when the schema cannot be located, or for non-profile JSON config work. Always validate before writing.
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Evaluates and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).
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Audit recent high-relevance papers from a target venue to identify claim-relevant experimental evidence gaps after core P0 results exist. Use for venue completeness and reviewer-risk analysis; not for redefining the research question or running experiments.
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Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then analyze trajectories for RMSD, RMSF, radius of gyration, hydrogen bonds, native contacts, PCA and free energy surfaces. Use this skill for protein stability under mutation, ligand binding-mode and residence-time questions, conformational sampling, membrane proteins, and disordered ensembles. Also trigger on OpenMM, MDAnalysis, mdtraj, Simulation.step, LangevinMiddleIntegrator, PDBFixer, DCD or XTC trajectory, RMSD analysis, or production MD.
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Find out whether a chemical series is already claimed, using SureChEMBL's patent-extracted compound corpus and, where a key is available, PatentsView for legal status and assignee history. Use this skill to trace a structure to the patent documents that disclose it, survey an assignee's filings around a target, and understand what the freedom-to-operate question requires that a structure search cannot answer. Also trigger on SureChEMBL, patent chemistry, Markush structure, freedom to operate, composition of matter, assignee, priority date, patent family, or PatentsView.
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Use when a SaaS founder needs to set, audit, or redesign pricing — choosing a value metric, structuring tiers and packaging, setting price points, designing discounts, planning a price increase, or optimizing a pricing page. Also for monetization questions like free-to-paid conversion, expansion revenue, willingness-to-pay research, or per-seat vs usage-based. Not for which acquisition channels to use (use channel-expert) or the wording of the pricing page (use copywriter-skill).
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Use when the user needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.
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Qualify a vendor or supplier of GxP-relevant software or services. Covers vendor risk classification, assessment questionnaire design, desk and on-site audit approaches, quality agreement evaluation, SLA review, and ongoing monitoring cadence definition. Use when selecting a new vendor for a GxP-critical system, onboarding a cloud provider for regulated data, performing a periodic re-qualification, when a vendor audit finding requires re-assessment, or when EU Annex 11 or ICH Q10 requires supplier qualification.
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Plan a comprehensive spectroscopic analysis campaign by defining the analytical question, assessing sample characteristics, selecting appropriate techniques using a decision matrix, planning sample preparation for each technique, sequencing analyses from non-destructive to destructive, and defining success criteria with a cross-validation strategy.
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Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.
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Guide a person in developing active listening skills. AI coaches receptive mindset preparation, full-presence attending, reflective paraphrasing, clarifying questioning, synthesis of understanding, and integration of insights into communication practice. Use when a person wants to improve communication, when someone keeps misunderstanding others or feels misunderstood, when preparing for a difficult conversation such as a feedback session or conflict resolution, or when a person notices they talk more than they listen.
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Universal SDLC pipeline orchestrator with stack provider auto-discovery. Reads stack.md profiles from installed plugins, picks the highest-priority match, executes a 5-phase pipeline (BA → Dev → QA → Sec → Docs) plus stack-defined extra phases. Use when: - User invokes /sdlc:start "<feature>" - User asks to "run the SDLC pipeline" or "go through the full pipeline" - You need to coordinate specialist agents to deliver a complete feature Do NOT use for: - Trivial single-file edits (just edit directly) - Read-only questions about the codebase - Casual conversation
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。
Open GitHub Issues for human input and respond to the researcher team. Use this skill whenever you need to handle a human_issue event, respond to human issues, ask humans a question, or check team communications. Also triggers for: "any human messages?", "check issues", "respond to humans".
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Bring how named experts would think about the question. Use when the user wants expert thinking on a tradeoff, choice, or stuck problem — not a verdict.
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Use when a human-curated dbt model index must guide BigQuery SQL for a warehouse question. Not for discovering undocumented models or executing warehouse changes.
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 when asked to prototype one design question through a cheap logic or UI experiment, including button-driven state-model checks. Not for polished artifacts: use polished-web-prototype.
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Use when an exact symbol, path, entrypoint, or line range can bound a focused code question or patch proposal under a fixed source budget. Not for source changes or broad repository exploration.
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Assess and route one bounded Proto UI dependency drift, advisory, or update question. Use to trace manifest and lockfile identity, consumer impact, compatibility evidence, provenance, update risk, and the exact next update or no-action transition. This leaf remains read-only.
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Assess one bounded Proto UI Adapter question without changing repository or external state. Use for new-Adapter research, target compatibility, catalog readiness, reviewed Module support or omission evidence, Host Capability fidelity, lifecycle translation, dependency risk, or implementation readiness. Do not catalog, implement, repair, or approve the Adapter slice.
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Create interview preparation from a job analysis, tailored resume, and master resume. Use when the user wants likely questions, truthful STAR answers, and ways to discuss gaps.
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Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.
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。
Analyze codebases using a graph database instead of reading source files. Use when understanding code architecture, finding functions or call patterns, tracing data flow, checking dependencies, or answering "where is X used?" questions. Grafema builds a queryable code graph from static analysis — prefer querying the graph over reading files manually.
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。