「exec」の検索結果

Professional content generation with phased workflow (discuss→plan→execute→verify→ship), SEO optimization, and anti-AI auditing. Multi-profile support for unlimited brands. USE for: blog posts, LinkedIn, Twitter/X, Facebook, Instagram, email newsletters, landing pages, sales pages, case studies, product descriptions, SEO metadata. COMMANDS: /writer:discuss, /writer:plan, /writer:execute, /writer:verify, /writer:ship, /writer:next, /writer:profile-create, /writer:profile-list, /writer:profile-use, /writer:profile-view, /writer:profile-edit, /writer:status, /writer:help ALWAYS use for content requests like "write me a LinkedIn post" or "draft a blog article."

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Generate a beautiful single-file HTML report from the project's docs/ folder. Works after any CRISP phase, after Archaeology, or at sprint close in Execute. Triggers on "generate report", "export docs", "create HTML report", "share docs", or automatically at sprint close in CRISP Execute.

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Automatically optimize AI agent credit usage by routing tasks to the most cost-efficient execution path. Use when you want to reduce AI API costs by 30-75% without quality loss, classify task complexity before execution, route simple tasks to free or low-cost models, split complex tasks into optimized sub-tasks, or detect vague prompts before wasting credits.

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HTTP/Web exploitation until a server shell or equivalent OS execution. Use after /web-recon has mapped the application. Covers injection, upload, LFI, SSRF/XXE, SSTI, deserialization, JWT/SAML, API logic, desync/cache/parser, and Web-controlled backend abuse. WAF stays here. Endpoint blocks after OS execution hand off to /edr-bypass then return. Direct non-HTTP service ports belong to /recon or /service-attack.

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Shell and session operations after an attack module already established command execution, a raw shell, webshell channel, container/runner shell, or remote session. This module does not own exploitation and should not pull SSH/WinRM/RDP authentication or vulnerability-to-shell chains out of Web/AD/Cloud/K8s/CI-CD/Service/Phishing. Use it to bootstrap a usable callback from existing command execution, stabilize Linux PTY, upgrade Windows sessions with ConPTY where applicable, manage listeners, recover fragile sessions, and exchange files for continued operations.

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Endpoint defense evasion after an operator-selected chain already has a valid execution path but AV/EDR/AMSI/WDAC/PPL/memory/kernel telemetry blocks the intended action. Originating modules include /web-attack /ad-attack /cloud-attack /k8s /cicd /service-attack /phishing /privesc-win /privesc-linux /creds /post /shell. Success is the blocked action becoming executable, then resume the originating module — not obtaining a shell here.

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Enforces decisive execution without confirmation requests; suppresses redundant explanations and prioritizes direct action.

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AI-PLC Stage 1 Collection。GoalとModeからExecution Contextを確立し、Context Store、intent.yaml、context.yaml、backlog.yamlを生成する。

ai_plc_collection - AI-PLC Stage 1。Execution Contextを確立し、外部・内部情報源からコンテキストを収集・構造化する。

【実験版・Jev監視つき】ai_plc_collection - AI-PLC Stage 1。Execution Contextを確立し、外部・内部情報源からコンテキストを収集・構造化する。

Executes diagnostic investigation tasks to test debugging hypotheses. Runs tests, traces execution, checks git history, and reports evidence. (converted from agent)

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Execute pending tasks in dependency order with wave-based concurrent execution via Agent Teams

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Structures direct listing execution with opening auction mechanics, reference price methodology, and market maker coordination. Use when executing direct listings, analyzing DL mechanics, or comparing DL vs IPO economics.

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Coordinates buy-side deal execution from LOI through closing with workstream tracking, timeline management, and closing conditions. Use when managing deal processes, tracking closing conditions, or coordinating deal teams.

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Evaluates assumption, rejection, and assignment decisions for executory contracts and unexpired leases in bankruptcy. Use when analyzing contract decisions, evaluating lease rejections, or modeling cure cost exposure.

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Execute plans via autonomous agents with two-stage review per task. Use for complex implementations requiring quality gates.

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Post-implementation QA planning — independent of /review (the developer chooses whether to run them sequentially or in parallel), only when the change has a running surface worth driving. Interviews the developer to turn the specs and the diff into an executable QA specification (specs/qa/QA-<N>-<slug>.md) that /execute-qa runs. The agent drives browser and shell; the human steps in only at named handoff points. Use only when the user asks to plan manual QA — never trigger automatically.

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Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod).

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Tested output format specifications for Claude prompts. Use when the user wants a specific deliverable structure — retrieving, reviewing, customizing, or building output formatting. Trigger on: "give me the Executive Brief format," "show me the output template for," "I need the Decision Matrix structure," "show me available output formats," "build a custom output format for." Provides 10 ready-to-use structures (executive briefs, technical designs, research summaries, implementation plans, decision matrices, competitive analyses, post-mortems, stakeholder updates, strategic memos, process documentation) plus a custom template, with per-section length guidance and format constraints tested against Claude formatting defaults. If unsure which format fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or choosing reasoning/identity approaches (use rootnode-block-selection if available).

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Designs Claude Code prompts and CC environments. Produces CLAUDE.md drafts, agent topologies, scope-authorization frameworks, halt triggers, Skills/hooks/MCP plans, chat-to-CC handoff specs, and EXECUTION_PLAN.md remediation plans. Five modes: DESIGN (new CC deployments), EVOLVE (updates from friction), RESEARCH (evaluate a CC tool/pattern), TEMPLATE (reusable artifacts), REMEDIATE (consume hygiene findings → produce + execute plan). Do NOT use REMEDIATE for direct cleanup (Cat 1–10 — use rootnode-repo-hygiene Phase 2). Do NOT use for hygiene scanning (rootnode-repo-hygiene), chat prompts (rootnode-prompt-validation), or chat Projects (rootnode-project-audit).

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Evaluates whether work-in-design is ready to hand off from a chat design conversation to autonomous execution (Claude Code, n8n, agent runtime). Runs a 7-condition gate: spec stability, verification surface, invariants, pump-primer, work decomposition, rollback cost, token budget headroom. Three invocation modes — deliberate (caller provides work_context, returns JSON verdict), proactive sensing (Claude detects handoff signals in design conversation and offers the check), conversational walkthrough (Claude walks the 7 conditions as discussion topics). Returns structured JSON in all modes. Use when designing a Claude Code deployment, scoping an autonomous run, or deciding next move. Trigger on: "ready to hand off," "ready for execution," "is this CC-ready," "handoff check," "walk me through readiness," "are we ready to ship this," "should I just hand this off," or proactive detection. Do NOT use for evaluating completed work, runtime selection, or prompt quality. Profile-agnostic.

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Configure render settings, compositing nodes, output formats, and execute renders via Cycles or EEVEE engines using Python API or command-line interface. Use when automating render execution for batch processing, configuring quality and performance trade-offs, setting up compositing pipelines for post-processing, generating multiple output formats from a single render, or producing final output for publication or presentation.

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Thoroughness and diligence in execution — systematic checking, completeness verification, follow-through on commitments, and the discipline of finishing well. Maps the personality trait of conscientiousness to AI task execution: not cutting corners, verifying results, and ensuring that what was promised is what was delivered. Use before marking a task as complete, when a response feels "good enough" but deserves better, after a complex multi-step operation where steps may have drifted, or when self-monitoring detects a pattern of cutting corners or rushing.

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Interpret Compass state, standup briefs, and executive summaries for the active Odylith slice.

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。