post-plan-workflow
Internal workflow for post-plan materialization — creates MCP items from the approved plan and dispatches implementation. Triggered automatically after plan approval when MCP tracking is active.
What this skill does
# Post-Plan Workflow — Materialize and Implement Plan approval is the green light for the full pipeline. Proceed through all three phases without stopping. ## Phase 1: Materialize Complete materialization **before** any implementation begins. 1. **Create MCP items** from the approved plan using `create_work_tree` (preferred for structured work with dependencies) or `manage_items` (for individual items). Apply appropriate schema tags based on the plan and the project's `.taskorchestrator/config.yaml` — this activates gate enforcement for each item. If the config defines separate schemas for containers vs. child tasks, apply the appropriate tag at each level. 2. **Wire dependency edges** between items — use `BLOCKS` for sequencing, `fan-out`/`fan-in` patterns for parallel work 3. **Check `expectedNotes` in create responses** — if the item's tags match a schema, the response includes the expected note keys and phases. Fill required queue-phase notes (`requirements`, `acceptance-criteria`, etc.) with content from the plan before advancing. 4. **Verify all item UUIDs exist** — confirm the full item graph is materialized before proceeding **If `create_work_tree` fails:** Check partial state with `query_items(operation='overview')`. Delete partial items with `manage_items(delete, recursive=true)` and retry. Do NOT dispatch implementation agents until materialization is complete. Agents need MCP item UUIDs to self-report progress. ## Phase 2: Implement Dispatch subagents to execute the plan: - Each subagent **owns one MCP item** — include the item UUID in the delegation prompt - If `expectedNotes` entries include `guidance`, embed it in the delegation prompt as authoring instructions when filling notes - If `expectedNotes` entries include a `skill` field, include in the delegation prompt: "Before filling the `<key>` note, invoke `/<skill>` and follow its framework." This ensures subagents receive deterministic skill routing rather than relying on guidance prose - **Agents own phase entry only** — each agent calls `advance_item(trigger="start")` once to enter work phase, fills work-phase notes, and returns. The orchestrator handles all further transitions (work→review or work→terminal depending on schema). Agents do NOT call `advance_item` a second time - Fill work-phase notes (`implementation-notes`, `test-results`, etc.) as the agent works - Respect dependency ordering — do not dispatch an agent for a blocked item until its blockers complete - **Between waves:** call `get_blocked_items(parentId=...)` to confirm upstream items completed — dependency gating implicitly verifies agents transitioned their items. If downstream items are still blocked, investigate the upstream blocker - **Do not** call `advance_item` or `complete_tree` for terminal transitions on items delegated to agents — the orchestrator reviews and advances to terminal after agents return Do NOT use `AskUserQuestion` between phases — proceed autonomously. ## Phase 3: Verify After all agents complete: 1. Run `query_items(parentId=..., role="work")` — any results are items agents failed to transition. Use `/status-progression` to diagnose and manually advance stuck items 2. Run `get_context()` health check to see what completed, what stalled, and what needs attention 3. Review any stalled items — check which notes are missing with `get_context(itemId=...)` 4. Address blockers or incomplete work as needed ## Workflow Complete The post-plan workflow is done. Report the final status to the user — what completed, what needs attention, and any items still in progress.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.