subagent

Solid

Protocol that evo optimization subagents follow when dispatched from /optimize. Auto-loaded by spawned subagents via their host's skill loader. The orchestrator may also invoke this skill to understand the brief shape its dispatched subagents expect + what they're required to emit -- useful when writing briefs or debugging a subagent's behavior.

AI & Automation 1,101 stars 81 forks Updated today Apache-2.0

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Skill Content

# Evo Subagent Protocol **Orchestrators reading for context**: this is the protocol your dispatched subagents follow. You don't act on it yourself -- write briefs that satisfy the four required fields described below, and rely on each spawned subagent to drive the loop on its end. Stop reading at "Host conventions" if you only need the brief shape; the rest is for the subagent. ## Evo surface -- subagent perspective What you can pull/dispatch/read as a subagent. Each line is a triggering condition. ``` skills you may pull (Skill tool) └── evo:finetuning before writing or changing any train.py -- technique choice, training recipe, observability, retry discipline. subagents you dispatch (Task tool, subagent_type=...) ├── evo:verifier MANDATORY pre AND post every `evo run`. │ Pre: static analysis before the experiment runs │ (block on failure -- fix and retry). │ Post: result-validity audit after it commits. └── evo:benchmark-reviewer POST-COMMIT only, mode=review-experiment -- per-task failure classification + annotations. Skip on evaluated/discarded/failed outcomes. references (Read tool, on demand) ├── discover/references/ │ ├── sdk_python.py / sdk_node.js wiring per-task instrumentation -- preferred │ ├── inline_instrumentation.py inline fallback. Copy as-is; do not ...

Details

Author
evo-hq
Repository
evo-hq/evo
Created
2 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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