loop-engineering
SolidUse when a repeatable task must become a bounded Trigger -> Execute -> Verify -> State loop, scheduled automation, goal agent, or metric-driven research cycle.
AI & Automation 137 stars
20 forks Updated 3 weeks ago MIT
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# Loop Engineering
<skill_contract>
<input>A repeatable task with inspectable state, finite budgets, permissions, and an independent verifier.</input>
<output>A validated Trigger -> Execute -> Verify -> State contract plus resumable run receipts.</output>
<done>The declared metric or stop condition is supported by fresh validator and verifier evidence.</done>
<non_goals>Dependency-graph orchestration, unbounded autonomy, or self-certified completion.</non_goals>
Build loops only when repeated execution creates evidence. Every loop needs admission, a validated contract, durable state, independent evaluation, bounded retries, stop/recovery rules, and a final receipt.
## Usage Template
Provide: objective, trigger, scope/non-goals, inputs, state/artifact paths, metric, verifier, permissions, budgets, stop condition, recovery, and write-back. See `references/ci-repair-loop-example.md` for a worked contract.
## Workflow
<intake>
Select one mode:
- **Goal:** run until a defined end state or cap.
- **Loop:** poll/iterate while eligible work exists.
- **Automation:** start from an external schedule/event; the trigger is not execution evidence.
- **AutoResearch:** vary experiments against an objective metric in a sandbox.
Admit only if work is repeatable, outputs are inspectable, a verifier exists, failures are recoverable, and autonomy is worth the orchestration/review cost. Otherwise use a one-shot workflow.
Use `graph-engineering` instead when explicit data depende...
Details
- Author
- Mark393295827
- Repository
- Mark393295827/third-brain-v7-skills
- Created
- 4 months ago
- Last Updated
- 3 weeks ago
- Language
- Python
- License
- MIT
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