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Keep research progress, decisions, and pending checkpoints across sessions. Store project context locally, recover essential details when returning, and track why decisions changed so research work can resume with a clear next step.
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Examine a plan or design through focused questions, each with a recommended answer. Work through connected decisions to reach shared understanding, using codebase exploration to resolve questions that the code can answer.
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Turn a design problem into an RFC, a proposal for review and discussion. Define who benefits and what changes, compare real alternatives, document drawbacks and open questions, and save a linked, validated draft.
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Review whether a proposal, specification, or design decision addresses the right problem and follows from its goals and constraints. Receive prioritized findings with supporting evidence, confidence, and concrete alternatives or questions that would resolve each concern.
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Turn an accepted feature proposal into an implementation specification grounded in existing code. Define scope, explain design decisions, track unresolved questions, and document migration steps and tests, with implementation tasks added when the work is approved for building.
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Collect unresolved work that needs human judgment from configured pull requests, issues, feedback, errors, and delivery signals. Group related problems into a decision queue with source links, supporting evidence, and visible gaps in coverage.
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Examine an existing plan, decision, or design through one question at a time, each with a suggested answer. Clarify assumptions, contradictions, and dependencies, then summarize agreed decisions, needed changes, and unresolved blockers.
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Read every architecture decision record in a project's docs/adr/ folder from beginning to end, giving the agent context about earlier design choices before it starts further work.
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Save working solutions, design decisions, code patterns, and failed approaches for future recall. The skill classifies entries, extracts tags, stores them in PostgreSQL with numerical text representations, and returns a record ID.
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Restructure an existing SKILL.md into v5 Hybrid format. Four agents use indexed reference books to identify workflow states, actions, decision rules, constraints, and completion conditions, then combine their findings into an upgraded skill and validation report.
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Creates LaTeX/PDF reports for pharmaceutical and clinical research. Compares patient groups by biomarkers, measurable biological indicators, and prepares treatment recommendation documents with evidence ratings, statistical results, and decision flowcharts.
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[omh] Deep dive before a decision: deep research engine - grounding for specs and decisions: study open-source reference implementations with pinned refs, gather live web evidence with citation discipline, verify contested claims, and distill a decision-grounding dossier that planning consumes; for a decision brief use research-brief, for upstream guidance use web-research. Use when the user says: research plan, literature review, research literature, review recent papers, deep research, deep-research, exhaustive research, saturation research.
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[omh] Uncertain technical choice for a spike: bounded decision prototype workflow: resolve one uncertain interaction, API, performance, or integration choice with a disposable, isolated experiment whose observed result feeds planning. Use when the user says: decision-prototype, decision prototype, prototype this uncertain choice before planning, prototype before planning, prototype the uncertain choice, run a small spike, small spike, spike solution.
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dontbesilent 个人决策系统。把任何一个需要长期跟踪的领域（业务、关系、健康、职业、学习、投资……）做成一个本地知识工程：四层结构、来源标签、写完不改的快照、能炼出规律的概念库。 触发方式：/dbs-decision、/决策系统、/决策立案、/结果回填、/状态画像 Personal decision system. Turns any long-running domain into a local knowledge project with four layers, source tags, immutable snapshots, and a concept library that learns patterns over time. Trigger: /dbs-decision, /决策系统, /决策立案, /结果回填, /状态画像
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Run a family meeting to make a big care decision together. Use when asked help me run a family meeting about care, we need to decide together about mom's care, my siblings and I disagree about our parent, or facilitate a care decision. Produces an agenda and structure for the meeting, how to prepare (facts, options, everyone's input), ground rules to keep old family dynamics from derailing it, how to include the person being cared for, ways to work through disagreement toward a decision, and clear next steps with owners. Not legal, medical, or financial advice.
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Use when the user asks to create, write, update, amend, supersede, or evaluate an ADR, architecture decision record, durable architecture decision, decision log, or baseline sync after architecture-changing work.
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Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.
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Monthly, on the last weekday, file work only, no browser at all. Folds the decision ledger, scores this Employee's own judgement against the metric files that measured it rather than against its own predictions, appends one outcome line per closed decision, retires a move proposed three times and never accepted, and rewrites the priorities where the evidence disagrees with them. It refuses to publish a calibration figure on too few closed decisions, changes nothing outside its own folder, proposes nothing to anybody, sends only where you released the channel, spends only where you released it, and never touches a credential.
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Offer 对比决策器。用户同时拿到两份（或多份）offer 纠结怎么选时，扮演 Senior Career Decision Advisor：结构化对比 comp / 成长 / AI 敞口 / 公司强度 / 团队风险 / 晋升速度 / 生活方式，识别 front-loaded vs long-term upside、resume value、switch-out 难度、hidden risks，最后给一条**明确、有立场、不中性**的推荐（并附「你是 X 类人选 A，你是 Y 类人选 B」的分叉判定）。三步：贴两份 offer → 补 priorities & 现状 → 自动生成并打开一份 HTML Offer Decision Report。是 offer-toolkit 里 JD Skill / Resume Skill / BQ Skill 拿到结果之后的下一环。关键词：offer compare, 选 offer, 两个 offer, offer decision, TC compare, 该去哪家, which offer, 4-year TC, RSU, sign-on, front-loaded, long-term upside, career decision。
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Use when you need to generate Architecture Decision Records (ADRs) for a Java project through an interactive, conversational process that systematically gathers context, stakeholders, options, and outcomes to produce well-structured ADR documents. This should trigger for requests such as Generate ADR; Create Architecture Decision Record; Document architecture decision; Architecture Decision Record for Java. Part of Plinth Toolkit
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Addresses healthcare AI privacy at the intersection of HIPAA and the EU AI Act for clinical decision support systems. Covers training data PHI handling, model transparency and explainability, patient rights in algorithmic decisions, FDA/OCR regulatory coordination, and bias monitoring. Keywords: healthcare AI, HIPAA, AI Act, clinical decision support, PHI training data, model transparency.
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Conducts a Data Protection Impact Assessment for automated decision-making and profiling systems under GDPR Article 35(3)(a), covering algorithmic transparency, meaningful human oversight, contestation mechanisms, and Art. 22 safeguards. Activate for DPIA automated decision, profiling DPIA, algorithmic impact assessment, Art. 35(3)(a), ADM risk assessment queries.
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Add fast, local, typed decisions to any project with Laya, an open-source non-generative decision model (pip install laya). It classifies, routes, scores and answers yes/no questions about a piece of text, returning calibrated probabilities in roughly 20-35 ms on a laptop GPU, with no LLM call and no data leaving the machine. Use this skill whenever the user wants to classify or route text, triage tickets or emails, detect spam, phishing, toxicity or intent, put a guardrail in front of an agent or LLM, score something against a rubric, or replace an LLM-based classifier to cut latency or cost. Also use it when they mention Laya, Jev, a "System 1 model", "typed decisions" or a "decision model", even if they never name Laya.
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Determine technologies needed to build the product, making build/buy/integrate decisions during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on requests to select tech stack, evaluate technologies, make build vs. buy decisions, discover existing assets, or when user asks "what technologies?", "select tech stack", "build or buy?", "what do we reuse?", "existing stack", "technical decisions", "what tools do we need?", "evaluate solutions". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and trade-offs. Feeds v0.6 Architecture Design.
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