Keep long-running work recoverable, stateful, and honest.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlong-task-coordinatorExecute the skills CLI command in your project's root directory to begin installation:
Fetches long-task-coordinator from charon-fan/agent-playbook and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate long-task-coordinator. Access via /long-task-coordinator in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Keep long-running work recoverable, stateful, and honest.
Use this skill when the work:
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.
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.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:
Use the smallest role model that fits the task:
Simple tasks can collapse these roles into one agent. Long or delegated tasks should make the split explicit.
For each coordination round:
READ -> RECOVER -> DECIDE -> PERSIST -> REPORT -> END
Do not report conclusions before the state file has been updated.
awaiting-result as a valid stateIf 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 -> completeA coordination round is only valid if it does at least one of the following:
If nothing changed, do not pretend the task advanced.
Recovery answers:
Domain work answers:
Recover first, then continue domain work.
Use this skill when at least one is true:
Prefer a path that is easy to rediscover, such as:
docs/<topic>-execution-plan.mddocs/<topic>-state.mdworklog/<topic>-state.mdIf no durable state exists yet, create one from references/workflow.md.
At the start of every new round:
After deciding the next action:
End each round with one of these states:
runningawaiting-resultpausedblockedcompleteThe reported status should match the persisted status exactly.
When using this skill, produce updates that are grounded in saved state:
Treat the coordination work as complete only when all relevant items below are true:
If the task is not truly complete, end in running, awaiting-result, paused, or blocked rather than pretending the work is done
Avoid:
references/workflow.md - Detailed workflow, state template, and recovery checklistMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in long-task-coordinator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
long-task-coordinator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added long-task-coordinator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: long-task-coordinator is the kind of skill you can hand to a new teammate without a long onboarding doc.
long-task-coordinator reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend long-task-coordinator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: long-task-coordinator is the kind of skill you can hand to a new teammate without a long onboarding doc.
long-task-coordinator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend long-task-coordinator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
long-task-coordinator has been reliable in day-to-day use. Documentation quality is above average for community skills.
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