「systematic」の検索結果

Analyze and red-flag contracts systematically, identifying risks, unfavorable terms, and negotiation opportunities

日本語の概要は準備中です。原文の説明を表示しています。

Systematic debugging with root cause investigation. (gstack).

日本語の概要は準備中です。原文の説明を表示しています。

"Apply Grounded Theory (Glaser and Strauss) to build theory inductively from qualitative data through open, axial, and selective coding. Use this skill when the user needs to develop new theory from data rather than test existing hypotheses, conduct theoretical sampling and constant comparison, determine when theoretical saturation is reached, or when they ask 'what theory explains this phenomenon', 'how do I code qualitative data systematically', or 'when do I stop collecting data'.".

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"Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices. Use this skill when the user needs to analyze irrational market behavior, explain pricing anomalies through cognitive biases, diagnose investor decision errors, or when they ask 'why do investors hold losers too long', 'how does loss aversion affect pricing', or 'what biases drive this market pattern'.".

日本語の概要は準備中です。原文の説明を表示しています。

Triage and address GitHub PR review comments systematically—classify, respond, fix or defer with rationale. Use when a PR has review feedback, requested changes, or unresolved threads.

日本語の概要は準備中です。原文の説明を表示しています。

"Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.".

日本語の概要は準備中です。原文の説明を表示しています。

"Implement a web crawler pipeline covering URL discovery, fetching, parsing, and storage. Use this skill when the user needs to build a site crawler, audit website structure, or collect web data systematically — even if they say 'scrape a website', 'crawl all pages', or 'site audit spider'.".

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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'.".

日本語の概要は準備中です。原文の説明を表示しています。

"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'.".

日本語の概要は準備中です。原文の説明を表示しています。

Systematic debugging playbook for the Forge agentic loop (src/core/loop.ts and src/agents/executor.ts). Use when a task is stuck, looping, or producing wrong tool calls.

日本語の概要は準備中です。原文の説明を表示しています。

Systematic debugging playbook for the Forge agentic loop (src/core/loop.ts and src/agents/executor.ts). Use when a task is stuck, looping, or emitting wrong tool calls.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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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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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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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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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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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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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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

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。