「decision」の検索結果

Organizes an author’s response to peer review. Extracts every referee and editor point, tags severity and type, orders the revision by dependency, flags defensible pushbacks as questions, and builds a numbered comment → response → location letter whose substantive answers the author writes. Use when the user has referee reports, a revise-and-resubmit decision, or an editor’s letter and wants to plan the revision, draft the response letter, or check that every point was addressed. The reviewer-side twin is journal-review.

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Runs a deliberative two-model committee of GPT-6 Astra and Claude Opus under a selectable chair, Fable by default, with `/model-committee-astra`, `/model-committee-opus`, and `/model-committee-sol` selecting the others. Use when a consequential decision has several defensible options and two model families should propose, critique, revise, and cross-rank them against a predeclared rubric before converging, as in research design, interpretation, architecture, manuscript strategy, or evaluation design. Not for factual lookups, independent-coder reliability, brainstorming, routine implementation, or final professional judgment.

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Drafts a referee report for a journal editor. Produces a recommendation, claim-and-design summary, three to six decision-driving concerns, additional concerns, and a revision plan through five parallel adversarial finders, Breaker, Butcher, Shredder, Void, Situator, a Blue Team error filter, Chief Reviewer synthesis, Tone Guard legal pass, and optional confidential editor note. Use when the user has been asked to referee a manuscript. Self-audits go to paper-review-lite or presubmit, and author responses go to referee-response.

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Writes or reviews a pre-analysis plan before data collection: registry choice (OSF, AEA, AsPredicted, registered reports), locked models and decision rules, code run on simulated data, contingencies, and deviation records. Use when drafting or auditing a pre-registration or PAP, choosing a registry, or handling a deviation.

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Turns dictated captures in a deliverable's inbox/ into a proposed planning-wiki diff, classifying and tracing each statement; decisions need the author's assent. Use to intake notes or process the inbox.

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Consults Fable as an independent, read-only reviewer at an effort the caller matches to the question. The calling session (usually Opus, or Haiku) holds the task and escalates one decision point. Use before committing to an interpretation or a substantial piece of writing or analysis, when stuck or not converging, when changing approach, or as a final check on a finished task. Fallback for when the native advisor tool is unavailable. Not for a session already running Fable.

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 one consequential, contestable decision through a deliberative committee of GPT-6 Astra and Claude Opus, chaired by fable (default), astra, opus, or sol. Not for factual lookups, brainstorming, routine implementation, independent-coder reliability, or final professional judgment. Use when several options are defensible and the two model families should propose, critique, revise, and cross-rank under a predeclared rubric.

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An AI analyst for buying B2B software. Investigates what vendors claim, checks the evidence, surfaces contradictions and unanswered questions, and produces a shareable Decision Brief with the questions to ask before you buy. Quick Eval by default; Deep Eval on request. Use when asked to evaluate, compare, vet, or research B2B software vendors.

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Update an OpenSpec change by revising its existing planning artifacts and keeping them coherent with one another. Use when the user wants to revise a change's plan, fold new decisions into it, or reconcile its artifacts after an edit. Never edits code.

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Grill the user about a requirement, decision, or idea before implementation, then produce an actionable planning report without writing code. Use when the user wants to clarify requirements, stress-test an idea, compare approaches, or plan before coding.

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When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.

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When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.

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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.

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Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

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在Claude Code会话期间，将做出的架构决策捕获为结构化的架构决策记录（ADR）。自动检测决策时刻，记录上下文、考虑的替代方案和理由。维护一个ADR日志，以便未来的开发人员理解代码库为何以当前方式构建。

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Cross-verify Claude-generated plans and code using OpenAI Codex and Google Gemini CLI. Provides code review, plan validation, and comparative analysis. Use when needing second opinions on Claude's code or plans, validating technical decisions, or seeking consensus from multiple AI models.

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Write clear, plain-spoken code comments and documentation that lives alongside the code. Use when writing or reviewing code that needs inline documentation like file headers, function docs, architectural decisions, or explanatory comments. Works well for both human readers and AI coding assistants who see one file at a time.

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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

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This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.

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North star guidance for the agent-resources project. Use this skill to understand project context, get inspiration for development decisions, and resolve uncertainty about feature priorities or design choices. Reference when asking 'why does this project exist?' or 'what should we optimize for?'

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Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.

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Expert research analyst specializing in comprehensive information gathering, synthesis, and insight generation. Masters research methodologies, data analysis, and report creation with focus on delivering actionable intelligence that drives informed decision-making.

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Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).

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。

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, 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。