415건 · 관련도순
Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.
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Multi-pass structural code review with adversarial analysis, scope-aware dispatch, adversarial persona passes, and forced-finding protocol. Supports input modes: branch diff (default), PR number (#123), commit hash, pending changes (--pending). Use when asked to "review this PR", "code review", "pre-landing review", "check my diff", or "review #123". Proactively suggest when the user is about to merge or land code changes. NOT for behavioral verification against a running build (see mk:evaluate); NOT for post-implementation simplification (see mk:simplify).
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Use when autonomously improving a measurable scalar metric through bounded, git-tracked iterations: modify one scoped change, verify, keep or revert. Triggers on /mk:loop, 'optimize coverage/bundle size/lint count', 'iterate until the metric improves'. NOT for subjective cleanup (see mk:cook), known-root-cause bugs (see mk:fix), behavioral grading (see mk:evaluate), or shipping (see mk:ship).
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Inspect a declared React Native or Expo feature slice and produce a read-only SwiftUI behavioral parity contract before any implementation.
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Verify one approved SwiftUI feature slice against its behavioral parity contract with deterministic and conditional Apple-platform checks.
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Record measured or directly seen security-research behavior as cited Observations, classify it against reusable security concepts, and decide whether it remains an observation, becomes a Lead, or supports a Finding. Use after passive analysis, a tool run, a probe, a crash, a timing anomaly, or a user-supplied behavioral report produces noteworthy evidence.
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Create a fresh NeatContext context from a user-defined behavioral profile and an existing local knowledge folder. Use only when the user explicitly invokes this skill or asks to create a new context rather than save the current conversation.
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Create a fresh NeatContext context from a user-defined behavioral profile and an existing local knowledge folder. Use only when the user explicitly invokes this skill or asks to create a new context rather than save the current conversation.
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Create a fresh NeatContext context from a user-defined behavioral profile and an existing local knowledge folder. Use only when the user explicitly asks to create a new context, or runs /neatcontext-create — not to save the current conversation.
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Mandatory skills, conventions, and behavioral rules for Avalonia UI development using the Zafiro toolkit.
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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
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> Game theory, Monte Carlo simulations, behavioral economics, and competitive war gaming.
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Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
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Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
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Enforce traceability from natural-language intent to machine predicates, final observables, evidence, and owner-readable acceptance. Use when conducting any audit, review, QA pass, parity or compliance check, security assessment, code or architecture review, visual or behavioral comparison, data validation, system verification, completion gate, or claim that an implementation satisfies a human request.
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Modos operacionais de IA (brainstorm, implement, debug, review, teach, ship, orchestrate). Use para adaptar o comportamento com base no tipo de tarefa.
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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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Build hyper-personalized, narrative-driven, single-file portfolio/pitch websites in the style of anass.pro. Use when the user asks for a "site like anass.pro", a one-recipient candidacy/pitch site, a behavioral-engineering landing page, a single-file storytelling portfolio with a pixel-art game hero, custom cursor, typewriter text, procedural Web Audio SFX, scroll-revealed scenes, or a dark purple narrative interface that addresses one named reader. Trigger on phrases like "personal pitch site", "one-page candidacy", "narrative portfolio", "scrollytelling pitch", "pixel-art hero", "interactive landing for X", or any request to recreate / clone / adapt anass.pro.
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Identifies Oracle-to-PostgreSQL migration risks by cross-referencing code against known behavioral differences (empty strings, refcursors, type coercion, sorting, timestamps, concurrent transactions, etc.). Use when planning migration of a procedure, trigger, query, or refcursor client, and again to validate the finished port.
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Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.
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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Refactor test files to improve behavioral coverage, clarity, structure, assertions, fixture setup, mocking strategy, and remove implementation-detail or low-value tests. Use when asked to clean up, declutter, consolidate, or improve tests.
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Design and implement unit, conformance, integration, timing, boot, trace, video, audio, and regression tests for EMULATORS processors, chips, peripherals, and complete machines. Use when creating a verification strategy for a new emulator, converting hardware documentation into tests, adding diagnostic programs or golden artifacts, measuring behavioral coverage, or proving that components interact correctly.
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