开发技能

Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.

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Parse all `# implements:` annotations in source code and emit `library/_code-index.md` as a shelf-index. Also extracts cross-module import edges and writes `library/_dependency-edges.md`. Idempotent — re-running produces byte-identical output if no annotations or imports changed.

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Generate boilerplate `# implements:` annotation for a function based on the artefact ID, inserting it as the first line of the function body.

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AI-First SDLC compliance rules and standards. Use when checking code quality, validating architecture, enforcing zero technical debt, or making any compliance decision.

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Set up Android app signing safely — generate an upload keystore, enroll in Play App Signing, and wire a Gradle release signingConfig that reads secrets from a git-ignored keystore.properties / CI (never committed). Use when configuring signing for a Play release.

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Configure SDLC team formation and SDLC method for this project. Asks the four-option SDLC method question (solo / single-team / programme / assured), then recommends and installs team plugins based on project type, language, tech stack, and support needs.

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Run the SDLC validation pipeline using direct tool invocations. No wrapper scripts required. Use when checking code quality before commits or PRs.

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Run all 5 decomposition validators (req-has-module, code-annotation-maps-to-module, visibility-rule-enforcement, anaemic-context-detection, granularity-match) on the project, reporting violations.

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Validated Google Play release — runs the pre-flight checks and clears the Play policy gates, builds a signed app bundle with a git-stamped versionCode, uploads to a track, and walks staged rollout (with halt/roll-forward). Use when releasing an Android app to Play.

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Scaffold a new Android app with SDLC and Play-safe defaults — Kotlin/Compose Gradle project with version catalog, convention plugins, module layout, a targetSdk that meets the Play mandate, R8 on release, and git-ignored signing so it passes the pre-flight checker from day one. Use when starting a new Android project.

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Commission a project to the Programme SDLC bundle. Tailored entry point with Programme-specific audience guidance; equivalent to /sdlc-core:commission --option programme.

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Commission a project to an SDLC option bundle (solo / single-team / programme / assured). Asks structured questions, recommends an option × level, installs the bundle, and records the decision in .sdlc/team-config.json.

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Knowledge base from NASA-STD-8719.14C (Process for Limiting Orbital Debris). Use for orbital-debris assessment planning, mission-related debris limits, explosion/passivation and collision risk, postmission disposal options, reentry casualty risk, and ODAR/EOMP governance. Covers STD-8719.14C (Approved 2021-11-05) synthesized notes only; does not replace NPR 8715.6 delivery timing, NASA-HDBK-8719.14 environment/modeling depth, or program-specific OSMA waivers.

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Knowledge base from NIST AI Risk Management Framework 1.0 (NIST AI 100-1). Use for AI risk management, trustworthiness characteristics, GOVERN/MAP/MEASURE/MANAGE functions, bias management, AI actor roles, TEVV, AI RMF profiles, and socio-technical risk framing. Does not cover sector-specific regulations, EU AI Act, ISO 42001 in depth, or the AI RMF Playbook tactical detail.

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Knowledge base from NASA Risk Management Handbook (NASA/SP-2011-3422). Use for risk-informed decision making, continuous risk management, performance commitments, risk scenario diagrams, risk disposition types, risk burn-down schedules, objectives hierarchies, deliberation and alternative selection, risk driver identification, and RM planning for space and safety-critical programmes. Does not cover domain-specific PRA methods (fault trees, event trees) in procedural depth — see NASA PRA Procedures Guide for those.

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Knowledge base from NASA-STD-7009B (Standard for Models and Simulations) with implementation depth from NASA-HDBK-7009B. Use for M&S criticality and programmatics, development evidence and capability assessment, verification/validation domains, uncertainty and sensitivity, use-phase results assessment, risk-informed decision reporting, and handbook life-cycle products. Covers STD-7009B (2024-03-05) + HDBK-7009B (2026-02-03) synthesized notes only; does not replace program-specific TA direction, NPR 7150.2 software requirements detail, or full PRA methodology.

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Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).

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This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.

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Structured code review through the shared OpenClaw agent-skills installation.

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Initialize an Obsidian vault with the agent-ready Zettelkasten system — creates directory structure, templates, CLAUDE.md, MCP config, and hooks

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Query OpenRouter API costs, credits, and usage breakdown by model, provider, and date for the last 30 days. Triggers on API spending, check costs, openrouter usage, how much have I spent.

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Apply React and Next.js performance optimization guidelines from Vercel Engineering — 40+ rules covering components, data fetching, bundle optimization, and rendering. Triggers on React components, Next.js pages, bundle optimization, performance improvements, refactoring React code.

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Build AI agents using the Claude Agent SDK. Covers query functions, ClaudeSDKClient, custom tools, MCP servers, hooks, permissions, subagents, and browser control (Chrome extension, dev-browser skill). Use when creating agents, adding tools, configuring agent behavior, or automating browser tasks.

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Build comprehensive ARCHITECTURE.md files following matklad's canonical guidelines — bird's-eye views, ASCII/Mermaid diagrams, codemaps, invariants, and layer boundaries. Triggers on document the architecture, create ARCHITECTURE.md, map this codebase, architectural overview.

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