long-form-work-orchestrator
Run long-form engineering work in checkpointed phases with deterministic artifacts, resilience handling, and end-of-run reliability reporting.
Best use case
long-form-work-orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run long-form engineering work in checkpointed phases with deterministic artifacts, resilience handling, and end-of-run reliability reporting.
Teams using long-form-work-orchestrator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/long-form-work-orchestrator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How long-form-work-orchestrator Compares
| Feature / Agent | long-form-work-orchestrator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Run long-form engineering work in checkpointed phases with deterministic artifacts, resilience handling, and end-of-run reliability reporting.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Long-Form Work Orchestrator ## Purpose Use this skill for multi-hour or high-surface-area tasks that need: - staged execution, - periodic progress checkpoints, - resumable artifacts, - boundary/risk reporting at the end. ## Workflow 1. Define a run manifest: - `goal` - `scope` - `in-scope files` - `out-of-scope files` - `success criteria` - `stop criteria` 2. Split work into phases: - `stabilize` (fix blockers) - `improve` (feature/test hardening) - `evaluate` (full run and boundaries) - `handoff` (summary + next steps) 3. Write checkpoints every phase: - timestamp - phase status (`pending|in_progress|done|blocked`) - files changed - key metrics - blockers and decisions 4. Enforce deterministic outputs: - scripts - tests - JSON/YAML reports - no hidden/manual-only state 5. Handle failures with policy: - `environment` (permissions, missing toolchain): isolate and report separately - `regression` (new breakage): stop and patch immediately - `legacy debt` (pre-existing failures): log with severity and ownership hint ## Reliability/Growth Reporting Contract At run end, produce: - `baseline_counts`: pass/fail/error/skip/xfail - `post_counts`: pass/fail/error/skip/xfail - `delta`: net improvement/regression - `new_boundaries`: discovered weak points - `hardened_boundaries`: weak points that now have tests/guards ## Multi-Agent / Swarm Addendum For swarm/browser/autonomy work: - require membrane scan before high-risk actions - map membrane result to turnstile action by domain - persist events to primary + replicas (decentralized write path) - never rely on a single hub path for critical artifacts ## Required End Output Return: - concise change summary - explicit paths changed - unresolved blockers - tri-fold YAML `action_summary` with: - `build` - `document` - `route`
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