效率提升技能

Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.

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Standard Operating Procedures and router for MCP task triage, subagent selection, and orchestration.

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Review-fix pipeline for Codex. Equivalent to the repository's Claude skill: collect review feedback, apply fixes through the existing review workflow, and prepare replies. Usage: /review [<issue#>|<PR#>]

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Phase exécution. Détecte le mode (epic/task/bug) selon le type d'issue et lance le bon mécanisme : loop driver background pour epic, code-dev synchrone pour task/bug. Usage: /implement <issue#>

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Conception pipeline for Codex. Equivalent to the repository's Claude skill: detect epic/task/bug mode, then delegate to the existing repo agents and workflows. Usage: /analyse [<issue#>] [<description>]

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Execution pipeline for Codex. Detect epic/task/bug mode and implement with Codex models only. Usage: /implement <issue#>

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Shared in-workflow quality gates for code/PR skills — the gate model, trust tiers, and the three primitives (evidence-before-done, independent judge, anti-gaming). DO NOT TRIGGER directly — referenced by consumer skills via relative path.

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Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.

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Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

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Run the full speckit workflow end-to-end — specify, plan, critique, tasks, implement, review — making all decisions autonomously. Stops before extract; run that manually.

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Run the speckit implementation phase chunk-by-chunk in clean-context subagents, then review, then produce a final report. Picks up from an existing tasks.md.

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Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.

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Execute the implementation plan by processing and executing all tasks defined in tasks.md

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Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

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Run the speckit preparation phases end-to-end — specify, plan, critique, tasks — making all decisions autonomously. Stops before implementation.

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Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"

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Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.

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Interact with Paddock — AI agent orchestration dashboard. Use when registering agents, managing tasks, syncing skills, or querying agent/task status via Paddock APIs.

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Verify tasks marked [X] in tasks.md are implemented, not phantom completions (marked done but backed by missing or dead code).

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Verify tasks marked [X] in tasks.md are implemented, not phantom completions (marked done but backed by missing or dead code).

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Post-implementation quality gate that reviews changes, fixes small issues (scout rule), creates tasks for medium issues, and generates analysis for large issues.

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Execute the implementation plan by processing and executing all tasks defined in tasks.md

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Perform a non-destructive post-implementation verification gate validating the implementation against spec.md, plan.md, tasks.md, and constitution.md.

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Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

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