| name | gemini-loop |
| description | Sets up and manages an autonomous scheduler loop based on reflective machine learning metaphors (Pretrained Backbone, Local Adapters, Error Banks). Use when asked to setup or manage a gemini-loop. |
Gemini Loop Skill (Reflective ML Edition)
This skill provides an automated framework for running an autonomous coding loop using Gemini CLI, heavily inspired by ML training principles.
Architecture
The loop treats long-horizon agent work as a layered optimization process:
roadmap.md (Pretrained Backbone): Slow parameters. Represents the global prior, stable constraints, and overall mission. Only updated when structural failures are proven.active_task.json (PEFT/LoRA Adapter): Fast parameters. Represents the localized execution state. Continually updated with assumed risks, targets, and immediate actions.failure_bank.json (Reusable Failure Memory): A global registry of recurrent patterns and mistakes to prevent looping errors.optimize (Forward Pass): Agent executes precisely one bounded slice of the active_task.json.check (Backward Signal): Agent inspects the actual outcome vs the intended objective. Instead of just passing or failing, it produces a Local Patch (e.g. scope_patch, prompt_patch) which is passed into the active_task.json for the next forward pass.
Quick Start
To initialize the reflective loop in the current workspace, run the included initialization script:
node <path-to-skill-creator>/scripts/init_gemini_loop.cjs
Note to Gemini Agent: When asked to initialize the loop, you can execute the script above directly from the skill's scripts directory, using the path __DIRNAME__/scripts/init_gemini_loop.cjs.
