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Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.

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Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.

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Review correctness, access, repository ownership, authority, localization, compatibility, resources, scope, and verification evidence.

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Review hot paths, allocations, iteration, caching, event frequency, and correctness-preserving optimization.

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Use when reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.

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Test API endpoints for correctness, edge cases, auth handling, and OpenAPI contract compliance.

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Grounds every implementation decision in official documentation. Use when you want to verify an approach against the official docs before implementing it, or when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.

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Implements, adopts, and reviews iOS 26+ Liquid Glass in SwiftUI: glassEffect, GlassEffectContainer, correctness, performance, and design fit. Use when adding Liquid Glass to a SwiftUI app or refactoring existing UI to the new design language.

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Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production auth, security-sensitive logic, a high-stakes migration, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.

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Conducts a multi-axis code review (correctness, readability, architecture, security, performance) before merging. Use before merging any change, after finishing a feature or bug fix, when reviewing code another agent or person wrote, or when asked to review a diff or pull request.

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Use this skill for critical code review that focuses on whether the code actually works correctly and achieves the expected results - not just whether it matches a design document. This skill assumes designs can be wrong, finds bugs the design didn't anticipate, and verifies end-to-end correctness. Triggers include "critical code review", "deep code review", "verify this implementation works", "find bugs in this code", or reviewing implementation against requirements. NOTE - Security review is OUT OF SCOPE; use a dedicated security review skill for that purpose.

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Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

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Use when evaluating a completed or in-progress project across scope, correctness, quality, testing, and learning outcomes using structured Praise-Critique-Grow feedback.

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Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.

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Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.

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Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions "qedgen", "lean proof", "formal proof", "verify my code", "prove correctness", "formal verification", or wants mathematical guarantees about their implementation.

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Load when asked to review a local diff/change set for correctness, regressions, blast radius, affected flows, and missing tests using the knowledge graph; use `review-pr` for intent/checklist/merge-gate evidence.

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Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.

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Use when reviewing, debugging, or improving TBQ3/TB language code for trading correctness, Bar strategy behavior, order direction, position state, sizing, costs, backtest realism, event timing, multi-symbol logic, or function documentation consistency.

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Audits WordPress plugin or theme PHP code for internationalization (i18n) correctness — text-domain consistency, use of escaped translation helpers (esc_html__, esc_attr__, esc_html_e), correct placeholder helpers (sprintf with translator comments, _n for plurals, _x for context), no variable text-domains, no concatenation inside __() calls, presence of load_plugin_textdomain or load_theme_textdomain, and matching declared Text Domain in the plugin/theme header. Use before plugin/theme release, when reviewing contributor PRs, when adding new strings, when migrating to a new text domain, or when a translator reports issues with the .pot file.

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Reviews code for objective correctness, security, and reliability.

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