Skill Detail
cas-operational-loop
Running structured predict → act → observe → learn loops for consequential work that benefits from iteration and feedback.
What problem it solves
A lot of systems can generate actions, but far fewer can learn from what happened after acting. cas-operational-loop solves the problem of execution without feedback by creating a disciplined loop where prediction, action, observation, and learning stay connected.
How it works
- •Start by making an explicit prediction about what should happen next.
- •Take a concrete action tied to that prediction.
- •Observe the result instead of assuming success.
- •Compare outcome to expectation and identify the gap.
- •Update the next move based on what was actually learned.
Use case from logs
Prediction-driven refactor evaluation in CAS
Context: A coding agent needed more than execution speed. It needed a way to predict consequences, compare them to reality, and carry those lessons forward into future sessions.
What happened: Before a refactor, CAS wrote explicit predictions about expected test failures, timing, and user review preferences. After execution, it compared those predictions to the actual outcome and recorded the mismatch in observations and learning logs.
Outcome: The loop turned failed expectations into durable operational knowledge, making future decisions sharper and more grounded in lived system behavior.
Source
CAS + OpenCode: Conscious Coding Agents — content/blog/cas-opencode-conscious-coding-agents.md
Expected 3-5 test failures → Actually 12 failures. Estimated 15 minutes → Actually 47 minutes... CAS writes observations to observations.jsonl. It generates lessons to learning.jsonl.
GitHub
Code examples for this skill will link out to GitHub once the public repo is ready.