「exec」の検索結果

Phase 10.6 — sync the project's execution roadmap (project.json → roadmap.path, default docs/roadmap-execution-plan.md) to the just-landed work. Flips the tasks named in workflow.json → roadmap_tasks[] from ⬜ to ✅ and promotes their epic headings ⬜→🟡→✅, preserving the standup parser format contract. On the epic track it appends instead — the epic's heading plus one row per slice — and stamps roadmap_epic back into the epic state. Also hosts the ad-hoc backfill that puts every epic already on disk onto the roadmap. Fail-open: never throws, no-ops when roadmap.path is unset/absent or no task matches, and never blocks a commit. Runs after /archive and before /memory-sync on every committing track.

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Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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Write executable failing tests from a recipe handed to you by the main context. Used by `/tdd` Step 2 and ad-hoc when a phase needs tests-first to drive implementation. Decisions about which scenarios to cover, which categories matter, and which fixtures to use are made by the caller — this skill executes that recipe with code-structure discipline.

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Split a dirty working tree into single-concern Conventional Commits. The deterministic inventory.mjs helper groups dirty paths by concern (docs, source + paired test, config, governance); main context refines the grouping and presents a commit plan. Read-only until the user approves the plan — it never stages, never commits, never touches consent paths. Disjoint from the commit skill (Phase 11 executor): the planner proposes a split; the executor lands one consented commit. Use when a working tree has accumulated heterogeneous changes that should not land as one commit.

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Execute implementation plans in batches with review checkpoints

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Use when you have a written implementation plan to execute in a separate session with review checkpoints

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Produce a short executive summary for the current rolling-wave project. Use when the user wants a concise status update, investor/team-friendly summary, leadership summary, project progress snapshot, or TL;DR of completed slices, the current slice plan, and upcoming slices.

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Use when you have a written implementation plan to execute in a separate session with review checkpoints

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Execute the implementation plan by processing and executing all tasks defined in tasks.md

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。

Execute the implementation plan by processing and executing all tasks defined in tasks.md

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。

Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).

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Use when implementing, debugging, refactoring, or reviewing code — enforces the think → analyze → verify → execute workflow — even when the user just says 'implement X' without naming it.

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Use quando o usuário pede para executar Codex CLI (codex exec, codex resume) ou referencia OpenAI Codex para análise de código, refatoração ou edição automatizada. Usa GPT-5.2 por padrão para engenharia de software de ponta.

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Quando o usuário quer otimizar onboarding pós-signup, ativação de usuários, experiência de primeira execução ou time-to-value. Use também quando o usuário menciona "fluxo de onboarding", "taxa de ativação", "ativação de usuários", "experiência de primeira execução", "empty states", "checklist de onboarding", "aha moment" ou "experiência do novo usuário". Para otimização de signup/registro, veja signup-flow-cro. Para sequências de email contínuas, veja email-sequence.

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Plataforma GPU serverless em nuvem para executar workloads de ML. Use quando você precisar de acesso GPU sob demanda sem gerenciamento de infraestrutura, fazendo deploy de modelos de ML como APIs, ou executando jobs em batch com auto-scaling.

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Agentes autônomos são sistemas de IA que conseguem decompor objetivos, planejar ações, executar ferramentas e se auto-corrigir sem orientação humana constante. O desafio não é torná-los capazes - é torná-los confiáveis. Cada decisão extra multiplica a probabilidade de falha. Esta habilidade cobre loops de agentes (ReAct, Plan-Execute), decomposição de objetivos, padrões de reflexão e confiabilidade em produção. Insight-chave: taxas de erro compostas matam agentes autônomos. Uma taxa de sucesso de 95% por passo cai para 60% b

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Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.

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Execute tasks based on GitHub Issue content

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Use when you have a written implementation plan to execute in a separate session with review checkpoints

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Diagnose and safely document Hermes cron script interpreter mismatches, especially when shell scripts are executed as Python or Python wrappers are executed as shell.

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Parse and interpret execution debug logs generated by the EMULATORS framework, correlating each CPU instruction block with its VIC-II, CIA, SID, interrupt, stop, raster, badline, and memory-access cycles. Use for .LOG traces from EMULATORS when diagnosing cycle timing, raster effects, CPU stalls, IRQ/NMI entry, instruction/chip synchronization, or when reconstructing execution around a PC or mapped register.

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Runnable driver for the deterministic campaign-execution protocol — the automaton that takes a validated campaign DAG to a correctly merged branch. Trigger when: - A campaign has a validated DAG + worker IBCs and the composer (or a cheap-tier runner) must DRIVE dispatch ⇄ reconcile as a loop rather than by hand. - Resuming a partially-executed campaign from its git history + sketch checkpoint after a halt or crash. - Prompt contains: /orchestrate, orchestration, drive the DAG, run the campaign, layer schedule, reconcile-and-merge, resume campaign.

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Use when an approved plan has independent tasks ready for delegated execution with fresh subagents and two-stage review.

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Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

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。