284 件 ・ 関連度順
概要と使いどころ
Turn reflection into decisions by scoring predictions against outcomes and tracking whether commitments held. Use when running a quarterly review, scoring forecast calibration, or reflection keeps producing notes instead of change.
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。
Map evidence-based connections between a wellbeing initiative and specific academic learning outcomes. Use when justifying wellbeing programmes, integrating SEL, or linking pastoral and academic work.
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。
Plan a unit using backwards design from desired outcomes through assessment evidence to learning activities. Use when starting a new unit or redesigning an existing one from standards.
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。
Annotate variants, compute amino acid coding changes, predict coding outcomes.
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。
Monthly, file work only, no browser at all. Re-reads a month of real change outcomes and rewrites the file that decides what the fix runner may change without asking first. A class the member merged unchanged repeatedly widens by exactly one step. A class they edited, closed unmerged, or that regressed narrows immediately. It never touches the outer boundary, which no volume of evidence can widen.
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。
Monthly on the last weekday, light browser lane. Re-tests every theme and every severity rule against a month of real ticket evidence and rewrites strategy/themes.md wherever the evidence disagrees with the assumption. It merges two themes the evidence shows are one, splits a theme whose tickets have two different answers, retires a theme with no volume, creates a theme for a cluster that kept landing unclassified, and rewrites a severity rule wherever the outcomes contradict it. Theme ids are never reused and never renumbered. It holds every outbound action unless you released the channel, and it never touches a credential.
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。
Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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 session ends and the user wants a summary of what was done and what it cost. Writes a shareable SESSION_REPORT.md with outcomes, work performed and estimated resource usage.
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。
Evaluates observed product outcomes against a prior hypothesis; does not run experiments or change user treatment.
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。
Design Python error paths with narrow exceptions, rollback, contextual logs, and preserved blocker truth. Use when handling retries, verifier outcomes, parser failures, or exception flow in Python services.
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。
Adjudicates authorized exercise outcomes against independently held ground truth, redacted evidence, and explicit status boundaries. Use when comparing observations, resolving discrepancies, or preparing evidence-led security-review handoffs.
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。
Standardize PRD discovery and drafting for product scope, user outcomes, requirement IDs, and acceptance criteria. Use when creating PRD, product requirements, feature specification, or acceptance criteria plan.
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。
Validates defensive controls with paired authorized action and observation evidence, distinguishing blocked, prevented, detected, responded, and telemetry-gap outcomes. Use for purple-team validation design or offline fixture review; not unsanctioned testing or production efficacy claims.
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。
Standardize BRD and BRD-lite discovery for business goals, stakeholder impact, current-to-future state, and measurable value outcomes. Use when creating BRD, business case, project justification, ROI narrative, or AS-IS to TO-BE scope.
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 skill effectiveness across sessions. Computes per-skill metrics (action rate, friction, outcomes), identifies degrading skills, and generates improvement recommendations. Requires session-scan data in metrics.jsonl.
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。
Analyzes recent CI failures on pull requests to identify flaky tests, using retry outcomes (failed attempt → green re-run) and cross-PR recurrence as evidence, and maintains a local longitudinal ledger so flakiness can be tracked over time. TRIGGER when: the user wants to find flaky tests, correlate recent CI failures, check which tests fail across PRs or recover on retry, or refresh the flakiness trend report. DO NOT TRIGGER when: babysitting a single PR's CI until green → monitoring-pull-requests; diagnosing or fixing one specific failing test → the bug-analysis skills.
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。
Produce a flow-level summary of the current Claude Code session — executive summary, chronological timeline, and outcomes — written into the active feature directory next to spec.md / plan.md.
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。
Search places, resolve addresses, compare routes, inspect neighborhoods, and retrieve geographic or environmental facts through the standalone @cablate/mcp-google-map CLI. Use for concrete location questions; use the travel-planning or local-seo Skill for those specialized outcomes.
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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
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。
Complete Product Owner / Product Manager capability — a wiki-style knowledge map of product practice in active software development: the role and its boundaries, strategy cascade (vision → OKRs → roadmap → backlog), continuous discovery, backlog craft (epics, INVEST stories, acceptance criteria), prioritization frameworks (RICE, MoSCoW, Kano, WSJF), sprint delivery, metrics (North Star, AARRR), stakeholder management, and anti-patterns. The PO/PM owns what gets built, in what order, and why — including the end-to-end user flows and user journeys. Use when scoping a product or feature, decomposing an idea into outcomes/epics/stories, prioritizing a backlog, defining an MVP, mapping user journeys, measuring success, or saying no to a stakeholder. Pairs with super-ui-ux-design, which owns how each screen looks and behaves.
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。
Prepare a proportionate work plan with outcomes, steps, dependencies and acceptance evidence. Use to plan substantial work or coordinate an approved team; a plan-only request does not authorize implementation.
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。
When the user needs to organize product initiatives into a prioritized, time-sequenced plan with outcomes and dependencies.
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 extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.
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。
Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.
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。