long-task-coordinator

charon-fan/agent-playbook · updated Apr 8, 2026

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$npx skills add https://github.com/charon-fan/agent-playbook --skill long-task-coordinator
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summary

Keep long-running work recoverable, stateful, and honest.

skill.md

Long Task Coordinator

Keep long-running work recoverable, stateful, and honest.

When to Use This Skill

Use this skill when the work:

  • Spans multiple turns or multiple sessions
  • Involves handoffs to workers, subagents, or background jobs
  • Needs explicit waiting states instead of "still looking" updates
  • Must survive interruption and resume from a durable state file

Skip this skill for small, single-turn tasks. Use planning-with-files when simple planning is enough and recovery logic is not the main concern.

Related Skills

  • planning-with-files keeps multi-step work organized in files.
  • workflow-orchestrator chains follow-up skills after milestones.
  • long-task-coordinator makes long-running work resumable, auditable, and safe to hand off.

Core Rules

1. Create one source of truth

For any real long task, maintain one durable state file. Chat history is not a reliable state store.

The state file should capture at least:

  • Goal
  • Success criteria
  • Current status
  • Current step
  • Completed work
  • Next action
  • Next checkpoint
  • Blockers
  • Active owners or workers

2. Separate roles only when needed

Use the smallest role model that fits the task:

  • Origin: owns the goal and acceptance criteria
  • Coordinator: owns state, sequencing, and recovery
  • Worker: executes bounded sub-work
  • Watchdog: checks liveness and recovery only

Simple tasks can collapse these roles into one agent. Long or delegated tasks should make the split explicit.

3. Run every cycle in this order

For each coordination round:

READ -> RECOVER -> DECIDE -> PERSIST -> REPORT -> END

Do not report conclusions before the state file has been updated.

4. Treat awaiting-result as a valid state

If a worker or background job was dispatched successfully, the task is not failing just because the result is not back yet.

Valid transitions include:

  • running -> awaiting-result
  • awaiting-result -> running
  • running -> paused
  • running -> complete

5. Non-terminal rounds must create real progress

A coordination round is only valid if it does at least one of the following:

  • Dispatches bounded work
  • Consumes new results
  • Updates the current stage or decision
  • Persists a new next step or checkpoint
  • Performs explicit recovery

If nothing changed, do not pretend the task advanced.

6. Keep recovery separate from domain work

Recovery answers:

  • Did execution drift from the saved state?
  • Is the expected worker result still pending?
  • Do we need to wait, retry, or re-dispatch?

Domain work answers:

  • What should we build, analyze, or deliver next?

Recover first, then continue domain work.

Operating Workflow

Step 1: Decide whether the task needs coordination

Use this skill when at least one is true:

  • The task will outlive the current turn
  • The task will hand off work to another execution unit
  • The task needs checkpoints, polling, or scheduled follow-up
  • The task has enough complexity that loss of state would be expensive

Step 2: Create or load the state file

Prefer a path that is easy to rediscover, such as:

  • docs/<topic>-execution-plan.md
  • docs/<topic>-state.md
  • worklog/<topic>-state.md

If no durable state exists yet, create one from references/workflow.md.

Step 3: Recover before acting

At the start of every new round:

  1. Read the state file
  2. Check whether the recorded next step still makes sense
  3. Confirm whether any delegated work returned
  4. Repair stale assumptions before new action

Step 4: Persist before reporting

After deciding the next action:

  1. Update the state file
  2. Record new status, owners, blockers, and checkpoint
  3. Only then report progress to the user or caller

Step 5: Close the round honestly

End each round with one of these states:

  • running
  • awaiting-result
  • paused
  • blocked
  • complete

The reported status should match the persisted status exactly.

Output Expectations

When using this skill, produce updates that are grounded in saved state:

  • What status the task is in now
  • What changed this round
  • What is expected next
  • What would unblock or complete the task

Acceptance Criteria

Treat the coordination work as complete only when all relevant items below are true:

  • A durable state file exists in a predictable path
  • The saved status matches the real task state
  • Completed work, next action, and blockers are recorded explicitly
  • Any delegated work has a named owner and a return condition
  • The final report is derived from the persisted state, not from transient reasoning

If the task is not truly complete, end in running, awaiting-result, paused, or blocked rather than pretending the work is done

Anti-Patterns

Avoid:

  • Reconstructing progress from memory instead of the state file
  • Reporting a conclusion before saving it
  • Marking waiting as failure
  • Ending a round with no new action and no state change
  • Mixing recovery checks with domain decisions in one fuzzy step

References

  • references/workflow.md - Detailed workflow, state template, and recovery checklist
how to use long-task-coordinator

How to use long-task-coordinator on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add long-task-coordinator
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/charon-fan/agent-playbook --skill long-task-coordinator

The skills CLI fetches long-task-coordinator from GitHub repository charon-fan/agent-playbook and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/long-task-coordinator

Reload or restart Cursor to activate long-task-coordinator. Access the skill through slash commands (e.g., /long-task-coordinator) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.536 reviews
  • Alexander Harris· Dec 28, 2024

    Useful defaults in long-task-coordinator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Liam Mehta· Dec 28, 2024

    long-task-coordinator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Kwame Bhatia· Nov 19, 2024

    We added long-task-coordinator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yusuf Thomas· Nov 19, 2024

    Keeps context tight: long-task-coordinator is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Alexander Mehta· Oct 10, 2024

    long-task-coordinator reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Fatima Malhotra· Oct 10, 2024

    I recommend long-task-coordinator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Piyush G· Sep 21, 2024

    Keeps context tight: long-task-coordinator is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Hiroshi Verma· Sep 21, 2024

    long-task-coordinator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • James Sanchez· Sep 21, 2024

    I recommend long-task-coordinator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Noor Zhang· Sep 17, 2024

    long-task-coordinator has been reliable in day-to-day use. Documentation quality is above average for community skills.

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