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概要と使いどころ

Execute approved PWDEV Flow plans one atomic task at a time with test-first implementation, fresh verification, blocker handling, and durable evidence. Use after the plan gate is approved or for approved Flow correction tasks.

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。

pwdev-solucoes/pwdev-claude-marketplace32026年9月28日 更新

flow-plan

無料

Decompose an approved PWDEV Flow specification into dependency-aware waves and atomic executable tasks. Use when design is approved and the user needs exact file scope, test-first actions, verification commands, and an execution gate.

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。

pwdev-solucoes/pwdev-claude-marketplace32026年9月28日 更新

Design scalable, normalized database schemas with proper relationships, indexes, constraints, and migration strategies for relational and NoSQL databases. Use when designing database schemas, planning table relationships and foreign keys, creating indexes for query optimization, defining constraints and validations, designing data models for scalability, planning database migrations, choosing between SQL and NoSQL, implementing sharding strategies, optimizing query performance, or establishing data integrity rules.

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。

Fabric-Pro/fabric-oss32026年10月14日 更新

Trace bugs backward through call stacks and execution flows to identify the original source of errors, invalid data, or incorrect behavior. Use when debugging complex issues, tracing error origins, investigating data corruption, following execution paths, identifying where invalid data enters the system, or finding root causes of cascading failures.

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。

Fabric-Pro/fabric-oss32026年10月14日 更新

Use when a sprint tracking issue (label `sprint`) on peterdrier/Humans needs working — routine-fired cloud runs, or any unattended session told to execute the sprint queue.

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。

nobodies-collective/Humans32026年10月14日 更新

Tactical fast path: turn a small problem, bug report, edit request, or narrow refactor into three deliverables — a brief change-spec (docs/specs/), a detailed implementation-ready plan (docs/plans/), and a TODO.md with agent-pickable tasks and milestones. Load when the user describes a tactical problem and wants quick planning artifacts, says "plan this change", "create a TODO", "write a plan for this", "problem to plan", "break this into tasks for agents", "I want to change X — plan it", or when process-decomposer routes here after determining the user needs lightweight planning deliverables. Also triggers on "create tasks from this problem", "make this actionable", or "turn this into a plan agents can execute". For feature-sized work that needs an executable spec + constitution + cross-check gate, route to `spec-driven-development` (or `feature-spec`) instead.

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。

dvy1987/agent-loom32026年8月8日 更新

Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.

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。

dvy1987/agent-loom32026年8月8日 更新

run-trace

無料

Append structured execution traces across operational, cognitive, and contextual surfaces with minimal overhead. Load when inspecting agent runs, logging tool calls and observations, enabling post-run debugging, or pairing with structured-planning step IDs. Also triggers on "trace this run", "log execution", "agent observability", "run log", or when fault-localize needs evidence. Default-on during multi-step plans. Traces live at .agent-loom/traces/ — git-ignored by default.

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。

dvy1987/agent-loom32026年8月8日 更新

Decompose tasks into structured, outcome-defined process entries with complexity triage. Load when user says "decompose this", "break this down", "what steps do I need", "plan this out", "what's the process for", "how do I approach this", or when any complex task needs structured execution planning. Includes a problem-understanding pass before complexity triage. Routes to `problem-to-plan` when the user needs planning deliverables (spec + plan + TODO). Does NOT replace brainstorming — brainstorming is design approval (upstream), this is execution planning (downstream).

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。

dvy1987/agent-loom32026年8月8日 更新

Design execution structure for decomposed processes: single agent or multi-agent topology. Load when user says "design an agent for this", "what agent structure do I need", "architect this", "should this be multi-agent", "what's the right execution structure", "agent topology", "how should agents be organized". Takes process-decomposer output as primary input. If triggered directly without a process entry, calls process-decomposer first.

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。

dvy1987/agent-loom32026年8月8日 更新

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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。

q2805187159/Anvil32026年6月24日 更新

Use this skill when the user asks about Hours of Service (HOS) rules under 49 CFR 395 — the 11-hour driving limit, 14-hour on-duty window, 60/70 hour rule, 30-minute break, 10-hour off-duty reset, 34-hour restart, sleeper berth provisions, split sleeper berth, short-haul exemption (150-air-mile), agricultural exemption, adverse driving conditions, ELD malfunction handling, personal conveyance, yard moves, or anything related to driver duty status. Cite specific 49 CFR 395 subsection. If the question involves a non-standard operation (passenger carrier, oilfield, agricultural, military), confirm which set of rules applies.

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。

x3allamerican/skills32026年5月17日 更新

Use this skill when the user asks how to determine whether a CMV operation is interstate vs intrastate, why it matters (federal vs state rules apply), the "continuation of journey" rule, state-specific intrastate exemptions, and what happens when an intrastate carrier picks up an interstate load. Cite 49 CFR 390.5 + state-specific exemptions.

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。

x3allamerican/skills32026年5月17日 更新

ALIAS for dag-orchestrator. The original DAG execution skill, now unified with orchestrator into dag-orchestrator. Use dag-orchestrator for the full HTDAG experience.

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。

curiositech/port-daddy32026年10月15日 更新

Designs or reviews provenance-bound DAG attempt traces, coverage gaps, cancellation, and unknown-effect reconciliation. NOT for claiming execution completeness, deployment monitoring, or root-cause proof.

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。

curiositech/port-daddy32026年10月15日 更新

Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).

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。

curiositech/port-daddy32026年10月15日 更新

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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。

curiositech/port-daddy32026年10月15日 更新

Parses complex problems into candidate DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies and identifies candidate parallelization opportunities. Activate on 'build dag', 'create workflow graph', 'decompose task', 'execution graph', 'task graph'. NOT for simple linear tasks or when an existing DAG structure is provided.

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。

curiositech/port-daddy32026年10月15日 更新

The intelligence layer of Jury-rig. Decomposes natural language tasks into proposed Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, proposes wave and revision contracts, and expands nodes only when evidence supports a revised graph. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.

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。

curiositech/port-daddy32026年10月15日 更新

Combines and synthesizes outputs from parallel DAG branches. Handles merge strategies, conflict resolution, and result formatting. Activate on 'aggregate results', 'combine outputs', 'merge branches', 'synthesize results', 'fan-in'. NOT for execution (use dag-parallel-executor) or scheduling (use dag-task-scheduler).

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。

curiositech/port-daddy32026年10月15日 更新

Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.

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。

curiositech/port-daddy32026年10月15日 更新

Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).

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。

curiositech/port-daddy32026年10月15日 更新

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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。

curiositech/port-daddy32026年10月15日 更新

End-to-end execution of a scouted third-party provider access campaign. Use when a Provider Access Launch task is ready_to_submit or scouted easy_execute and needs account or app registration, scopes, reviewer assets, Vault credential custody, the canonical connection, and real verification.

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。

armanisadeghi/ai-matrx32026年10月15日 更新