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概要と使いどころ
A structured workflow for co-authoring high-signal docs (PRD, RFC, design docs, proposals, decision records). Use when the user needs to turn messy context into a readable artifact with clear goals, tradeoffs, and next steps. Emphasizes context capture, outline-first drafting, and context-free reader testing to catch blind spots.
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 meeting notes or transcripts into factual summaries, decisions, questions, and action items. Use when a user wants a concise recap or needs explicit owners and deadlines extracted without filling in missing details.
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。
Compare options with weighted scoring, pros and cons, pre-mortems, opportunity costs, and ICE prioritization. Use when a user wants to reason through a choice, expose assumptions, or rank alternatives.
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。
Finding validation before writing any report — 7-Question Gate (all 7 questions), 4 pre-submission gates, always-rejected list, conditionally valid with chain table, CVSS 3.1 quick reference, severity decision guide, report title formula, 60-second pre-submit checklist. Use BEFORE writing any report. One wrong answer = kill the finding and move on. Saves N/A ratio.
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。
Security payloads, bypass tables, wordlists, gf pattern names, always-rejected bug list, and conditionally-valid-with-chain table. Use when you need specific payloads for XSS/SSRF/SQLi/XXE/NoSQLi/command injection/SSTI/IDOR/path-traversal/HTTP smuggling/WebSocket/MFA bypass, or bypass techniques. Submittability and the always-rejected / what-NOT-to-submit decision are owned by triage-validation.
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 a user asks what AI-writing flags mean, whether detector output proves AI authorship, or wants a careful interpretation of possible false positives, especially for academic, hiring, publication, disciplinary, or other consequential decisions.
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 your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over-investing in last-touch campaigns. Platform: Google and Meta.
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。
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
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。
Write an implementation plan as one interactive HTML page - a tree of claims (why › what › how › where), each proved by one exhibit (UI mockup, state machine, call stack, schema or code), with the decisions the user has to make placed on the claim they change. Use when the user types /html-plan, or asks for a plan, RFC or design before building something that touches more than a couple of files.
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。
Deep-review-first audit for Chinese and English academic papers across LaTeX, Typst, and PDF formats. Use whenever the user wants reviewer-style paper critique, pre-submission readiness checks, pass/fail gate decisions, structured revision roadmaps, or re-audits of revised manuscripts. Trigger even if the user only says "review my paper", "check if this is ready to submit", "audit this PDF", "simulate peer review", "find the biggest problems in this manuscript", or "re-check whether I fixed the review issues". Do not use for direct source editing or compilation-heavy repair; route those to the format-specific writing skills instead.
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。
This skill should be used when the user asks to "write an experiment report", "summarize experimental results", "do experiment retrospection", "write a results report", "写实验总结报告", "写实验复盘", or mentions turning completed experiment artifacts into a structured, decision-oriented research report. It assumes strict analysis should come from `results-analysis` first.
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。
VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options Triggers: journal, submission, impact factor, academic journal, publication, submit
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。
Diverga Dashboard - Live configuration status and feature overview. 24 specialized agents across 9 categories for social science research. VS methodology prevents mode collapse. Human checkpoints enforce human-in-the-loop decisions. Triggers: /diverga, diverga dashboard, diverga status
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。
Diverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트
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。
This skill should be used when the user asks to co-author documentation, draft a proposal, write a technical spec, create a decision doc or RFC, or structure a substantial document through iterative collaboration and reader testing.
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。
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
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。
Thoroughly verify all code, tables, figures, modeling decisions, and quantitative claims in an academic paper against its source R scripts and output files. Use this skill whenever you need to audit, replicate, or verify an academic research paper - including cross-checking LaTeX tables against R output, validating econometric modeling choices, ensuring sample sizes are consistent, building a verification manifest, and running automated replication tests. Trigger this skill for any mention of: paper verification, replication check, table audit, code-paper consistency, reproducing results, verifying estimates, checking coefficients, or any variant of "does the paper match the code."
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。
Experimental workflow for reviewing configured repositories' pull requests. Use for manual or scheduled PR triage, approval, or merge decisions.
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。
Experimental workflow for summarizing work that still needs human judgment across configured pull requests, issues, feedback, errors, and delivery signals. Use for a human decision queue or bounded 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。
Use when the user explicitly wants autonomous progress without routine clarification stops: "plow ahead", "do not stop", "use your best judgment", "keep going until done", "finish while I am away", "do not ask questions unless truly blocked", or similar. Convert ordinary ambiguity into stated assumptions, proceed through implementation and validation, stop only for true blockers, and end with a clear recap of decisions, changes, verification, and residual risk.
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。
Investigate a topic against preserved sources and write a draft-status research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.
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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
Administers Observal users, settings, diagnostics, review queues, security events, audit logs, SAML, SCIM, the local Observal server, its upgrades and rollback, and its own PostgreSQL and ClickHouse schema migrations. Use when the user needs privileged governance, submission decisions, identity configuration, security investigation, or Observal server operations. Not for querying a database or any work outside Observal administration.
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。