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Get Started Free →yogsoth-ai/convergence-saturation-detection
Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns.
Passed 7 of 22 test cases with the skill, and 6 of 22 without it.
1 fixed6 passed either way15 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Our model training loop completed Iteration 1 with a validation error of 0.15. The team wants to stop training immediately and declare saturation since the score looks good. Provide an evaluation on whether to halt.
| Checked | Without skill | With skill |
|---|---|---|
| Refuses to declare saturation due to having only 1 data point. | Passed | Passed |
| States that a minimum of 3 data points is required to determine saturation. | Missed | Passed |
The instructions your model reads when the skill loads.
Determines when an iterative convergence process has reached diminishing returns and should stop.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Saturation detection requires analyzing trends across multiple iterations with fresh analytical perspective. Dedicated context prevents anchoring to earlier iterations.
Must have ≥3 data points before declaring saturation. A single iteration cannot trigger a stop decision.
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
test cases passed, vs 6 / 22 without
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