Agent skill

Post Plan Workflow

by jpicklyk in jpicklyk/task-orchestrator

Internal, hook-triggered: materializes MCP items from the approved plan and dispatches implementation.

MITAuto-check passedAgent Workflows

Install Post Plan Workflow

skills CLI
$ npx skills add jpicklyk/task-orchestrator --skill post-plan-workflow -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jpicklyk/task-orchestrator post-plan-workflow --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jpicklyk/task-orchestrator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/claude-plugins/task-orchestrator/skills/post-plan-workflow .claude/skills/post-plan-workflow && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
post-plan-workflow
GitHub stars
207
Token cost
~2k tokens
SKILL.md length
1,042 words
Files
1
Skills in repo
28
Repo updated
First seen
Licence
MIT

At a glance

Internal, hook-triggered: materializes MCP items from the approved plan and dispatches implementation.

  • Works in 3 steps: Materialize → Implement → Verify
  • Tasks that involve MCP servers
  • SKILL.md covers Phase 1: Materialize, Phase 2: Implement, Phase 3: Verify and Workflow Complete
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Post Plan Workflow is an agent skill from jpicklyk/task-orchestrator. Internal, hook-triggered: materializes MCP items from the approved plan and dispatches implementation.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what… The licence is MIT.

When your agent uses it

  • Tasks that involve MCP servers

Example prompts

  • “/post-plan-workflow”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Materialize
  2. Implement
  3. Verify

What it can do on your machine

Read from SKILL.md and the folder at commit b688ea0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Post Plan Workflow loads about 2k tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 1,042 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jpicklyk/task-orchestrator at commit b688ea0, republished under its MIT licence (© jpicklyk). 1,042 words, ~1,987 tokens.

Download SKILL.mdSave it as .claude/skills/post-plan-workflow/SKILL.md (or your agent's skills folder).
name
post-plan-workflow
description
Internal, hook-triggered: materializes MCP items from the approved plan and dispatches implementation.
user-invocable
false

Post-Plan Workflow — Materialize and Implement

Plan approval is the green light for the full pipeline. Proceed through all three phases without stopping; the one expected stop is the turn that ends when a run-wave Method A launch hands control to the async notification (see Phase 2, Route).

Phase 1: Materialize

Complete materialization before any implementation begins.

Prefer a stashed docRef over re-authoring note bodies. On HTTP+REST workspaces, the plan-capture hook stashes the just-approved plan as a plan document as ExitPlanMode fires, and reports the slug via additionalContext (Task Orchestrator: plan stashed as plan document '<slug>' (root <rootId>)). When that context is present, or manage_plan_documents(operation="list", rootId=..., status="pending") confirms a pending document for this root, use it as the source of truth for materialization — quote/reference its content instead of retyping the plan into note bodies. Fall back to the plan text already in context when no stashed doc exists (stdio setups, or the hook failed open).

  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.
    • Anchor the root under the project when known: resolve the project rootId from session context (injected by the SessionStart hook) or .taskorchestrator/config.yaml's project.rootId. When known, set the new root item's parentId to that rootId (directly, or to the appropriate category container beneath it if one already exists) so materialized work lands inside the project's tree instead of at a bare depth 0. When no rootId is known, create at depth 0 as before.
    • Dedup check before each new root or feature item (children inherit their parent's check): run ONE unscoped query_items(operation="search", query="<title key terms>", limit=5). Unscoped on purpose — depth-0 process-global items such as agent-observations sit outside any project ancestor, so an ancestorId-scoped search misses them. Show close matches as one FYI line (role + short id each) and keep materializing — the user decides whether to act; never auto-link, auto-skip or auto-cancel.
  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 (feature-summary, task-scope, etc.) with content from the plan before advancing.
    • feature-implementation root: keep its feature-summary note lean — goal (2-3 sentences), a findings→tasks table mapping plan findings to the child items just created, dependency edges between those children, and a pointer to non-goals (target under 2k chars). Put full alternatives/blast-radius/risk-flags/test-strategy detail in each child's task-scope note instead — that's where /spec-quality's full bar applies.
  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(operation="delete", itemIds=["<uuid>"], 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

Route: run-wave or hand-dispatch

Hand off to /task-orchestrator:run-wave only when ALL four conditions hold; each is a concrete probe:

  1. Two or more unblocked leaf items. At least two leaf items (no children) were materialized and are unblocked: they are absent from get_blocked_items(ancestorId=<materialized root>).
  2. A rootId resolves (project or personal), in the order run-wave Step 1 uses: session context (Active project: or Personal root: line), else project.rootId from the file on that context's Config: line, else .taskorchestrator/config.yaml.
  3. Queue notes are filled. Each of those leaves has its required queue notes filled (get_context(itemId) shows no missing queue notes) or is schema-free (the response has no noteSchema).
  4. Protocol rules are served. query_rules(operation="list", rootId) lists protocol.entry-seat, protocol.in-phase-seat and protocol.read-only-agent. A seed made earlier this session (by init or the run-wave F3 repair) shows up in this list.

If all four hold, invoke /task-orchestrator:run-wave <materialized root id> and follow it. Plan approval counts as run-wave's Checkpoint 1. When a degradation forces a human look (meta.excluded or meta.degradations non-empty, or an empty run), show the explain table and end the turn; never use AskUserQuestion. A Method A launch ending the turn is expected. run-wave's post-run replaces Phase 3 below, which then applies only to the hand-dispatch path.

If any condition fails, hand-dispatch as described below and add one line naming the failed condition(s); for conditions 2 and 4, point at /task-orchestrator:init.

Show full SKILL.md (306 more words)Show less
Hand-dispatch

Dispatch subagents to execute the plan:

  • Each subagent owns one MCP item — include the item UUID in the delegation prompt
  • Resolve each note's guidance/skill via query_items(operation="schema", itemId=...) (expectedNotes itself is keys-only); embed guidance in the delegation prompt as authoring instructions
  • When a note's skill is set, 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, session-tracking, 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(ancestorId="<featureRootId>") to confirm upstream items completed — dependency gating implicitly verifies agents transitioned their items. ancestorId catches blockers anywhere in the feature's subtree (not just direct children, which parentId alone would miss). 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(operation="search", 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.

© jpicklyk, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in claude-plugins/task-orchestrator/skills/post-plan-workflow of jpicklyk/task-orchestrator.

Open the folder on GitHubat commit b688ea0

Compare with similar skills

Post Plan Workflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Post Plan Workflow compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Post Plan Workflow this skilljpicklyk/task-orchestrator207—~2kAutomated safety check: PassMIT
MCP Server Builderanthropics/skills180k62 repos~2.3kAutomated safety check: PassApache-2.0
MCP Server BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official37k11 repos~3.1kAutomated safety check: PassApache-2.0
Fastmcp Client CLIPrefectHQ/fastmcp28k1 repos~823Automated safety check: PassApache-2.0
Crush Configurationcharmbracelet/crush29k—~3.7kAutomated safety check: PassCustom licence

Similar skills

  • MCP Server Builder

    anthropics/skills

    Official

    Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.

    180k GitHub starsUsed in 62 repos~2.3k tokens
    Agent WorkflowsAuto-check passed
  • MCP Server Builder

    shareAI-lab/learn-claude-code

    Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.

    78k GitHub starsUsed in 5 repos~1.2k tokens
    Agent WorkflowsAuto-check passed
  • MCP Integration for Plugins

    anthropics/claude-plugins-official

    Official

    Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.

    37k GitHub starsUsed in 11 repos~3.1k tokens
    Agent WorkflowsAuto-check passed
  • Fastmcp Client CLI

    PrefectHQ/fastmcp

    Query and invoke tools on MCP servers using fastmcp list and fastmcp call.

    28k GitHub starsUsed in 1 repo~823 tokens
    Agent WorkflowsAuto-check passed
  • Crush Configuration

    charmbracelet/crush

    Explains how to configure the Crush coding agent with crushrc or crush.json, covering providers, models, LSPs, MCP servers, hooks, permissions and config precedence.

    29k GitHub stars~3.7k tokensUpdated today
    Agent WorkflowsAuto-check passed
  • Context Mode Output Sandbox

    mksglu/context-mode

    Routes large command, file, API and browser output through context-mode tools so only the needed result enters the agent's context, instead of dumping it via Bash.

    26k GitHub stars~4.1k tokensUpdated today
    Agent WorkflowsAuto-check passed

More from jpicklyk/task-orchestrator

All 28 skills in this repo
  • Task Orchestrator Server Setup

    jpicklyk/task-orchestrator

    Walks through how to launch and reach the MCP Task Orchestrator server container: transport, REST API, port publishing, config mounts and config-sync.

    207 GitHub stars~3.1k tokensUpdated yesterday
    Auto-check passed
  • Run Wave

    jpicklyk/task-orchestrator

    Resolves ready MCP work items into a run plan, shows it to you, then executes it through the Workflow tool or direct subagent dispatch, with post-run verification.

    207 GitHub stars~4.7k tokensUpdated yesterday
    Auto-check passed
  • Adopt Project Scope Migration

    jpicklyk/task-orchestrator

    Migrates an existing unscoped Task Orchestrator database to the project-scoping convention in place, creating one project anchor root and re-parenting work trees under it after a mandatory dry run.

    207 GitHub stars~3.7k tokensUpdated yesterday
    Auto-check passed
  • Bulk Task Completion

    jpicklyk/task-orchestrator

    Completes or cancels a whole feature subtree, a named list of items, or a batch of stale work items at once, previewing the impact and warning before force-completing anything active.

    207 GitHub stars~2.6k tokensUpdated yesterday
    Auto-check passed
  • Task Orchestrator Item Creator

    jpicklyk/task-orchestrator

    Creates an MCP work item from conversation context, anchoring it under the right container, inferring type and priority and pre-filling the required notes.

    207 GitHub stars~4k tokensUpdated yesterday
    Auto-check passed
  • Work Item Dependency Manager

    jpicklyk/task-orchestrator

    Views, creates, deletes and diagnoses BLOCKS, IS_BLOCKED_BY and RELATES_TO links between MCP work items, including why an item cannot start.

    207 GitHub stars~3.5k tokensUpdated yesterday
    Auto-check passed

Categories

Questions about Post Plan Workflow

What does Post Plan Workflow do?

Internal, hook-triggered: materializes MCP items from the approved plan and dispatches implementation. Post Plan Workflow is an agent skill from jpicklyk/task-orchestrator. Internal, hook-triggered: materializes MCP items from the approved plan and dispatches implementation.

When should I use Post Plan Workflow?

Post Plan Workflow fits situations like: tasks that involve MCP servers.

How do I install Post Plan Workflow in Claude Code?

Run `npx skills add jpicklyk/task-orchestrator --skill post-plan-workflow -a claude-code`. Or copy the skill folder (claude-plugins/task-orchestrator/skills/post-plan-workflow in jpicklyk/task-orchestrator) into .claude/skills/post-plan-workflow in your project. Claude Code loads it when a task matches its description.

How do I install Post Plan Workflow in Codex?

Run `npx skills add jpicklyk/task-orchestrator --skill post-plan-workflow -a codex`. Or copy the skill folder (claude-plugins/task-orchestrator/skills/post-plan-workflow in jpicklyk/task-orchestrator) into .agents/skills/post-plan-workflow in your project. Codex loads it when a task matches its description.

Can I use Post Plan Workflow in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jpicklyk/task-orchestrator --skill post-plan-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/post-plan-workflow, .gemini/skills/post-plan-workflow, .github/skills/post-plan-workflow and .opencode/skills/post-plan-workflow in your project.

What does Post Plan Workflow need to run?

SKILL.md names no scripts, command-line tools or credentials: Post Plan Workflow is instructions for the agent only.

Does Post Plan Workflow access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Post Plan Workflow safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Post Plan Workflow use?

Post Plan Workflow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Post Plan Workflow use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Post Plan Workflow?

Skills that share tags, products or a category with Post Plan Workflow: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 37k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Post Plan Workflow?

jpicklyk (a GitHub user) maintains it in jpicklyk/task-orchestrator, which has 207 GitHub stars. The repository holds 28 skills in this directory. The repository was last updated on October 6, 2026.

Source: jpicklyk/task-orchestrator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.