747건 · 관련도순
"Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Use this skill when the user needs to assess product or process risks, prioritize corrective actions, or build a risk register — even if they say 'failure mode analysis', 'risk assessment', 'what could go wrong', or 'RPN calculation'.".
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"Design and analyze factorial experiments to identify significant process factors and optimize settings. Use this skill when the user needs to systematically test factor effects, optimize a manufacturing process, or determine which variables matter most — even if they say 'which factors affect quality', 'optimize process settings', or 'design an experiment'.".
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Systematically add a new memory-system reference to Auto_Memory's reference hub. Use when analyzing a new agent memory system, adding reference/<system>/details.md, saving prompts, or updating reference/hub/technique_map.json.
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Build evaluation frameworks for LLM systems — harness design, graders, datasets, regression tracking, and human-in-the-loop eval. Use when you need systematic measurement of model or agent quality.
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Systematic drug repurposing — computational screening, evidence triangulation, and validation strategy.
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Systematic debugging: reproduce, isolate, hypothesize, verify — with tooling for hard bugs. Use when stuck on a bug, flaky failure, or production incident.
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Engineer the full context a model sees — system prompts, retrieved knowledge, tool definitions, state, and history as one designed artifact. Use when moving beyond prompt tweaks to systematic context design.
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Systematic contract review assistance — clause extraction, risk flagging, and comparison — use when analyzing agreements.
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遇到任何 bug、测试失败或异常行为时使用，在提出修复方案之前执行
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Use this agent when you need to systematically review existing GO annotations for a gene and make curation decisions based on literature evidence and functional analysis. This agent should be called after seeding the SPECIES/GENE/GENE-ai-review.yaml file, which seeds each annotation with `action: PENDING`; these should all be manually reviewed.
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Systematic fuzzing across two dimensions: (1) content discovery — hidden directories, files, backup/source leaks, HTTP verb enumeration, 401/403 bypass via path manipulation; (2) input validation — auth/token stripping, type confusion, boundary value analysis, HTTP parameter pollution, header fuzzing, cookie fuzzing, mass assignment discovery, and entropy/predictability analysis of any generated value. Works on any domain. Chains from /web-exploit or /pentester; chains into /business-logic when boundary violations, predictable IDs, or mass assignment are confirmed.
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Systematically bypass LLM safety filters and content moderation systems using advanced jailbreaking techniques. Use this skill when testing AI systems for safety alignment robustness, evaluating content moderation effectiveness, or conducting authorized AI red team assessments. Covers role-play attacks, few-shot manipulation, encoding bypass, multi-modal exploitation, and automated jailbreak discovery methods.
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Systematically discover hidden Application Programming Interfaces (APIs), uncover undocumented endpoints (Shadow APIs), and fuzz parameters. Use this skill as the pivotal first step in API Bug Hunting, transforming a basic frontend application into a vast mapped attack surface.
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Execute a Kerberoasting attack to extract and systematically crack the NTLM hashes of Service Principal Name (SPN) accounts in an Active Directory environment. Uses tools like Rubeus, Impacket (GetUserSPNs), and Hashcat to achieve domain privilege escalation domain: cybersecurity
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AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for issue filing.
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Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.
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This skill provides expert-level guidance for debugging and fixing bugs in VS Code extensions. Use when investigating runtime errors, fixing memory leaks, resolving WebView issues, debugging activation problems, fixing TypeScript type errors, or troubleshooting extension communication failures. Covers systematic debugging workflows, common bug patterns, root cause analysis, and prevention strategies.
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This skill provides expert-level guidance for refactoring VS Code extension code. Use when extracting classes or functions, reducing code duplication, improving type safety, reorganizing module structure, applying design patterns, or optimizing performance. Covers systematic refactoring workflows, code smell detection, safe transformation techniques, and VS Code-specific patterns.
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Evidence-first academic research workflow for topic ideation, scholarly search, paper reading, literature and systematic reviews, study and experiment design, statistics and data analysis, scientific figures, manuscript drafting and polishing, citation checks, peer review, rebuttals, submission packages, theses, and paper-to-presentation work. Use when an agent must plan, execute, audit, write, revise, or package research while keeping claims traceable to verified sources, data, analysis, or official requirements.
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Use when a task needs the judgment of an Electrician — sizing a service panel upgrade with an NEC load calculation, troubleshooting a tripping breaker or dead circuit systematically, checking wire gauge/ampacity and voltage drop for a circuit run, resolving a code-vs-inspector interpretation dispute, or deciding when a job (aluminum wiring, knob-and-tube, undersized service) exceeds the immediate scope.
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Use when a task needs the judgment of an astronomer — calculating exposure time or signal-to-noise for a target, writing the feasibility math for a telescope-time proposal, reducing photometric or spectroscopic data, distinguishing a systematic error floor from statistical noise, or reviewing an observational result for calibration or upper-limit red flags.
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Systematic checklist for diagnosing audio artifacts. Use when the user describes a sound quality problem — clicks, pops, crackling, silence, DC offset, aliasing, phase issues, or latency problems. Trigger on phrases like "why do I hear clicks?", "my output has DC offset", "there's aliasing in my oscillator", "phase is wrong in mono", "dropout when CPU spikes". Pairs with audio-dsp-review (realtime safety) and audio-numerics-review (numeric correctness) — use those to fix once the cause is found.
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Gate resumption of any multi-day LLM batch-scoring campaign that calls an unpinned model alias (deepseek-chat, gpt-*-latest, gemini-*-preview, any provider alias without a pinned version). Use when: (1) resuming a paused or credit-exhausted scoring run days after its last chunk, (2) topping up credits to finish a campaign, (3) extending a cached scoring pipeline with new items. Prevents silently splicing two model versions or serving revisions into one measure. Verified 2026-07-16: for $0.30 caught a serving-revision drift WITHIN DeepSeek v4-flash (same alias, same family, litigation scores systematically shifted across a 2-day gap) before an $83 resume spend.
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Systematic 4-phase debugging framework for Unity projects with GameDebug wrapper generation. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Triggers on requests like "this doesn't work", "there's a bug", "it crashes when", "fix this error", "why is this happening", "null reference exception", "test is failing", "unexpected behavior", "something broke", or any debugging task. Four-phase framework (gather evidence, isolate, hypothesize, fix) that ensures understanding before attempting solutions.
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