long-task-coordinator▌
charon-fan/agent-playbook · updated Apr 8, 2026
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Keep long-running work recoverable, stateful, and honest.
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-fileskeeps multi-step work organized in files.workflow-orchestratorchains follow-up skills after milestones.long-task-coordinatormakes 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-resultawaiting-result -> runningrunning -> pausedrunning -> 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.mddocs/<topic>-state.mdworklog/<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:
- Read the state file
- Check whether the recorded next step still makes sense
- Confirm whether any delegated work returned
- Repair stale assumptions before new action
Step 4: Persist before reporting
After deciding the next action:
- Update the state file
- Record new status, owners, blockers, and checkpoint
- Only then report progress to the user or caller
Step 5: Close the round honestly
End each round with one of these states:
runningawaiting-resultpausedblockedcomplete
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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches long-task-coordinator from GitHub repository charon-fan/agent-playbook and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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.
List & Monetize Your Skill
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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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★36 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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