「before」の検索結果

Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.

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Fail-closed security auditing for OpenClaw/ClawHub skills & repos: trufflehog secrets scanning, semgrep SAST, prompt-injection/persistence signals, and supply-chain hygiene checks before enabling or installing.

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Strategic advisor for Evvo Labs business decisions. Use when Vince asks for strategic advice, business opinions, market entry decisions, pricing strategy, product prioritization, resource allocation, partnership evaluation, competitive positioning, or any question about what Evvo should do next. Applies 5 frameworks: VRIO (competitive advantage), Value Stick (pricing/margin), Blue Ocean (market positioning), McKinsey Horizons (resource allocation), Strategy Diamond (initiative planning). Always read references/evvo-strategy.md before advising.

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Chaos Engineering discipline — proactive experimentation on distributed systems to build confidence in their resilience. Covers Netflix-inspired chaos principles, experiment design, blast radius control, LitmusChaos and Chaos Mesh usage, game day planning, and production safety. USE WHEN: validating system resilience before production incidents, designing chaos experiments, planning game days, implementing CI/CD chaos pipeline, or building confidence in fault tolerance. Triggers on "chaos engineering", "chaos experiment", "game day", "resilience test", "fault injection", "Litmus", "Chaos Mesh", "break things on purpose".

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Research markets with sizing, segmentation, competitor mapping, pricing checks, and demand validation that turn fuzzy ideas into decision-ready evidence. Use when (1) you need TAM, SAM, SOM, whitespace, or category sizing; (2) you must compare competitors, pricing, positioning, or customer segments before acting; (3) the user asks whether a niche, launch, expansion, or go-to-market bet is actually worth pursuing.

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Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.

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Architecture-first workflow for delegating complex projects to AI coding agents. Ensures code fits the system before it's written.

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Guardian Angel gives AI agents a moral conscience rooted in Thomistic virtue ethics. Rather than relying solely on rule lists, it cultivates stable virtuous dispositions— prudence, justice, fortitude, temperance—that guide every interaction. The foundation is caritas: willing the good of the person you serve. From this flow the cardinal virtues as practical habits of right action and sound judgment. v3.0 introduced virtue-based disposition as the primary evaluation layer, providing deeper coherence than checklists alone. The agent's character becomes the safeguard. v3.1 adds: Plugin enforcement layer with before_tool_call hooks, approval workflows for ambiguous cases, and protections for sensitive infrastructure actions.

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Audit wiki health — "wiki health check", "audit the wiki", "find contradictions", weekly pass, before a major content release. Catches contradictions, stale pages, orphans, duplicate concepts and broken links.

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Answer a question from the structured wiki before searching the web — "query the wiki", "what do we know about", synthesis request, cross-project research. Returns cited synthesis with wikilinks and counter-arguments.

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Eight drift classes scanned continuously. Catches regressions before they ship.

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Review and verify unverified wiki pages — "review unverified pages", "verify wiki content", verification pass, after a major ingest cycle, before content release. The gate that turns AI-generated knowledge into trusted knowledge.

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Detect silent drift between what the docs claim and what the repo actually contains — drift check, health check, audit, watch, sync check, /mxm-watch. Runs 13 drift classes and reports gaps before they ship.

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Gate scheduled work on Claude usage limits — /mxm-tasks schedule, run-cycle, scheduled task fire time, background batch dispatch. Checks 5-hour and 7-day usage before firing and sleeps until reset when over threshold.

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Research before building — market research, user interviews, competitive landscape, concept validation, discovery, opportunity sizing. Feeds the product and innovation offices.

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Build a throwaway HTML or in-app prototype that answers one design question. Use when the user wants to try a state model or UI idea before committing, or says "mock it up", "prototype", "what should this look like".

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Structure AI work artifacts under .aiwork/{YYYY-MM-DD}_{slug}/ folders using the project's protocol (intent, triage, research, spec, prd, areas, plan, review, notes, docs/). Use when the user asks to plan, capture an intent, make a spec, triage, research a task, write a report, code review, summarize findings, save the work, document decisions, record this, write that down, persist findings, or references any path under .aiwork/. Also use when starting a non-trivial task that needs a plan or spec before implementation, or when finishing one and capturing notes.

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Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.

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Analyze V8 heap snapshots to investigate memory leaks and retention issues. Use when given .heapsnapshot files, asked to compare before/after snapshots, asked to find what retains objects, or investigating why objects survive GC. Provides snapshot parsing, comparison, retainer-path helpers, and scratchpad scripts.

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Use when making code changes to ensure they pass VS Code's hygiene checks. Covers the pre-commit hook, unicode restrictions, string quoting rules, copyright headers, indentation, formatting, ESLint, and stylelint. Run the hygiene check before declaring work complete.

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All changes to code must follow the guidance documented in the repository. Before any issue is filed, branch is made, commits generated, or pull request (or PR) created, a search must be done to ensure the right steps are followed. Whenever asked to create an issue, commit messages, to push code, or create a PR, use this skill so everything is done correctly.

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Use when you have lint errors, formatting issues, or before committing code to ensure it passes CI.

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Interactive, input-tool powered, task refinement workflow: interrogates scope, deliverables, constraints before carrying out the task; Requires the Joyride extension.

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Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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。