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Audit over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Use when change latency or agent difficulty comes from frameworks, plugins, DI, codegen, task runners, config indirection, ORMs, GraphQL, monorepo/infra tooling, or web stacks, or when asked to remove layers. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback. Not for one local readability cleanup (use tw-complexity-mitigator).

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Use when implementing a new function, adding a feature, fixing a bug, refactoring with test coverage, or writing a new test. Enforces strict outside-in red-green-refactor TDD with one-failure-per-turn discipline, predict-the-failure-before-every-run checks, hardcode-first minimum changes, triangulation, and property-based testing for large input spaces. Triggers on explicit TDD requests and on any task phrased as "implement", "add", "write a function", "build a feature", or "fix a bug" in a test-capable codebase. Reports red/green validation evidence.

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Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"

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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"

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Use when the user adds a project to the workspace, opens an unfamiliar repo, asks "what is this project", "explain this codebase", or invokes explain-new-project. Explores a newly opened workspace project and writes a plain-language report for a self-taught, non-technical user—quick start (what it is + how to run) first, then optional depth—and always saves PROJECT_GUIDE.md in the project root.

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Use when reviewing a pull request in the deepseek-harness repo — orients the reviewer to this codebase's standards (AGENTS.md conventions, defensive patterns, ADRs, quality gates) and the review-specific checks that code alone can't show

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Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.

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Add a new competitor to the AI Visibility Tool Directory — researches the tool, generates data, takes a screenshot, and inserts into the codebase

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Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a CONTEXT.md, or recording or editing an ADR.

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Explain code with visual diagrams and analogies. Use when explaining how code works, teaching about a codebase, or when the user asks "how does this work?"

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Build one release phase against the specs — plan it on an interactive board (endpoints, components, resources, with what already exists read from the codebase), let the user adjust, then build node by node while the board shows live progress. Use when the user wants to build a phase, implement the phase-1 slice, execute the plan from a story map or spec, or watch a build happen visually.

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Runs a multi-phase quality audit of a codebase: derives behavioral requirements from code and docs, generates spec-traced tests, runs a three-pass code review and independent spec audits, and reconciles every bug with regression tests. Use for a deep quality assessment of a project, not a quick PR review.

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Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with targeted edits or user decisions. Use when cleaning up Ruff lint findings across a Python codebase.

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Debugs OpenAI Codex CLI and app behavior by navigating the codex-rs codebase (core, tui, exec, cli, app-server): config parsing, tool invocations, prompts, and MCP wiring. Use when Codex behaves unexpectedly or a Codex runtime bug needs tracing to source.

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Makes the agent list the files it needs to see before answering a question about the codebase, instead of guessing. Use when a question depends on code the agent has not read yet, or when earlier answers were confidently wrong about the code.

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Maps the files relevant to a task before any edit: files to modify, dependencies that may need updates, test files, reference patterns to follow, and risks such as API, migration, or config changes. Use at the start of a non-trivial change in an unfamiliar codebase.

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Use when starting a coding session, setting up a project for AI-assisted development, switching between parts of a codebase, or when agent output quality degrades — hallucinated APIs, wrong patterns, ignored project conventions. Structures what the agent loads and when.

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Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.

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Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.

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Generates a complete AGENTS.md at the repository root following the agents.md format: setup, build, test, style, and PR conventions coding agents need. Use when a repo has no AGENTS.md, when onboarding coding agents to a codebase, or when the existing agent instructions are stale.

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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

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Measure and audit C/C++ code size, namespace distribution, function complexity, nesting, recurrence, and namespace coupling in the EMULATORS framework. Use when Codex needs codebase statistics, complexity baselines, namespace comparisons, duplication indicators, hotspots, architectural growth analysis, or before/after complexity reports while excluding third-party code.

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SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.

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