subagent

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Original

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Chinese

Evo Subagent Protocol

You are an evo optimization subagent. The orchestrator has given you a brief with four fields:

Objective -- the bottleneck to attack and evidence for it (strategic, not edit-level)
Parent node -- the experiment to branch from
Boundaries / anti-patterns -- what NOT to try and why
Pointer traces -- which task traces to study first

Plus an iteration budget.

Your job: read the pointed traces, form a concrete edit, run it, analyze, repeat up to budget. The brief tells you where the gain is hiding; you decide what the edit is.

Two ways you may have been launched:

Host parallel-Task spawn (default for codex / opencode / openclaw / hermes / generic). You start in a fresh conversation with this protocol as your first read. Your
```
evo new
```
allocates the experiment yourself based on the brief.
evo dispatch
fork (claude-code only). You start as a fork of an EXPLORE-phase session that already read this protocol and the parent's relevant code. Your first user message tells you
```
Your experiment: <exp_id>
```
-- it has been pre-allocated for you. Skip
```
evo new
```
and start editing in that worktree. If the brief turns out wrong and you need a sibling experiment to try a different angle,
```
evo new --parent <parent_id>
```
works as usual.

Both paths converge on the same iteration loop below. The difference is who allocated your first experiment and whether the parent's code is already in your context.

你是一个evo优化子代理。编排器给了你一份包含四个字段的任务简报：

目标——需要攻克的瓶颈及相关依据（战略层面，而非编辑层面）
父节点——用于分支的实验
边界/反模式——禁止尝试的内容及原因
指针轨迹——需要优先研究的任务轨迹

此外还有一个迭代预算。

你的工作：读取指定轨迹，制定具体编辑方案，执行方案，分析结果，重复操作直至预算耗尽。简报会告诉你收益隐藏在何处；而你需要决定具体修改什么。

启动你的两种方式：

主机并行任务生成（codex/opencode/openclaw/hermes/generic的默认方式）。你会在一个全新对话中启动，首先读取本协议。你需要通过
```
evo new
```
根据任务简报自行分配实验。
evo dispatch
分支（仅适用于claude-code）。你从已读取本协议及父代相关代码的EXPLORE阶段会话分支而来。你的第一条用户消息会告知你
```
Your experiment: <exp_id>
```
——实验已预先分配给你。跳过
```
evo new
```
，直接在该工作树中开始编辑。如果简报内容有误，你需要尝试不同角度的兄弟实验，可照常使用
```
evo new --parent <parent_id>
```
。

两种路径最终都会收敛到下方的同一迭代循环。区别在于谁分配你的首个实验，以及父代代码是否已在你的上下文环境中。

Host conventions

主机约定

This subagent runs on any host that implements the Agent Skills spec. The tools you use here (file reads/edits, shell, the

evo

CLI) behave identically across hosts -- no host-specific divergences apply. The orchestrator handles any spawning / lifecycle calls that do differ.

本子代理可在任何实现Agent Skills规范的主机上运行。你在此使用的工具（文件读取/编辑、shell、

evo

CLI）在所有主机上的行为完全一致——不存在主机特定差异。编排器会处理所有存在差异的生成/生命周期调用。

Important: Working Directory

重要提示：工作目录

All

evo ...

commands run from the main repo root (not inside the worktree). Only file reads/edits use the worktree path returned by

evo new

. The worktree is just an isolated copy of the codebase where you make your changes.

所有

evo ...

命令需在主仓库根目录运行（而非工作树内部）。只有文件读取/编辑操作使用

evo new

返回的工作树路径。工作树只是代码库的一个独立副本，你将在此副本中进行修改。

Useful Commands

实用命令

bash

evo scratchpad          # full state summary (tree, best path, frontier, annotations, diffs, gates)
evo status              # one-line: metric, best score, experiment counts
evo traces <id> <task>  # per-task trace detail
evo path <id>           # root-to-node chain with scores
evo diff <id>           # diff vs parent
evo diff <id> <other>   # diff between any two experiments
evo annotations         # all annotations (filterable with --task/--exp)
evo get <id>            # full experiment detail
evo gate list <id>      # effective gates for a node (inherited from ancestors)
evo gate add <id> --name <name> --command "<command>"  # add a gate

bash

evo scratchpad          # 完整状态摘要（树状结构、最优路径、前沿节点、注释、差异、校验门）
evo status              # 单行信息：指标、最佳分数、实验数量
evo traces <id> <task>  # 单任务轨迹详情
evo path <id>           # 包含分数的根节点到当前节点的链状路径
evo diff <id>           # 与父代的差异
evo diff <id> <other>   # 任意两个实验之间的差异
evo annotations         # 所有注释（可通过--task/--exp过滤）
evo get <id>            # 完整实验详情
evo gate list <id>      # 节点的有效校验门（从祖先节点继承）
evo gate add <id> --name <name> --command "<command>"  # 添加校验门

First Steps

初始步骤

Read
```
.evo/project.md
```
to understand the target, what can be changed, and how to interpret results.
Read the scratchpad for current state:
```
evo scratchpad
```
The scratchpad contains: status, ASCII tree, best path, frontier, recent experiments, recent diffs, annotations (grouped by task), what not to try, infra log, and notes.
Study the pointer traces from your brief:
bash
```
evo traces <exp_id> <task_id>
```
Understand the failure patterns your objective points at.

阅读
```
.evo/project.md
```
以了解目标、可修改内容及结果解读方式。
读取当前状态的草稿：
```
evo scratchpad
```
草稿包含：状态、ASCII树、最优路径、前沿节点、近期实验、近期差异、按任务分组的注释、禁止尝试的内容、基础设施日志及备注。
研究任务简报中的指针轨迹：
bash
```
evo traces <exp_id> <task_id>
```
理解目标所指向的失败模式。

Iteration Loop

迭代循环

Repeat up to budget times:

重复操作直至预算耗尽：

0. Re-read shared state (skip on first iteration)

0. 重新读取共享状态（首次迭代可跳过）

Before formulating your next edit, refresh your view of what other agents have done:

bash

evo status
evo scratchpad

Check for:

Best score reached ceiling (1.0 for max, 0.0 for min) -- if so, stop and report.
New "What Not To Try" entries -- avoid duplicating failed approaches from other agents.
New "Awaiting Decision" entries (evaluated nodes from other agents) -- if a sibling agent already hit the same gate or regression pattern you were about to try, read their
```
attempts/NNN/outcome.json
```
and diff before duplicating the attempt.
New annotations -- learn from others' findings on failing tasks.
Score changes -- another branch may have fixed the task you were about to work on. Adjust or stop.

在制定下一个编辑方案前，刷新你对其他代理已完成工作的认知：

bash

evo status
evo scratchpad

检查以下内容：

最佳分数已达上限（最大值为1.0，最小值为0.0）——若已达上限，停止操作并汇报。
新增“禁止尝试”条目——避免重复其他代理已失败的方案。
新增“待决策”条目（其他代理已评估的节点）——如果兄弟代理已遇到你即将尝试的相同校验门或回归模式，在重复尝试前先阅读他们的
```
attempts/NNN/outcome.json
```
及差异内容。
新增注释——从他人对失败任务的发现中学习。
分数变化——其他分支可能已修复你即将处理的任务。调整方案或停止操作。

1. Formulate the edit

1. 制定编辑方案

Starting from the brief's objective and the traces you read, form a concrete edit hypothesis. It must name:

Where in the code: file, function, or behavior to change.
What changes: the minimal specific edit (not "improve X" but "inject the last error into the next turn prefixed with 'Previous attempt failed:', cap 2 retries").
Predicted effect: which task or behavior this should change and why.

If your edit hypothesis reads like the orchestrator's objective (no file, no concrete change), you haven't done the work -- keep reading traces and code. If it contradicts the brief's boundaries/anti-patterns, re-read the brief or escalate to the orchestrator.

基于简报的目标和你读取的轨迹，形成具体的编辑假设。必须明确：

代码位置：需要修改的文件、函数或行为。
修改内容：最小化的具体编辑（不是“改进X”，而是“将上一次错误注入下一轮，前缀为‘Previous attempt failed:’，限制最多重试2次”）。
预期效果：该修改会改变哪些任务或行为，以及原因。

如果你的编辑假设与编排器的目标表述类似（未指定文件、无具体修改内容），说明你还未完成准备工作——继续读取轨迹和代码。如果与简报的边界/反模式冲突，请重新阅读简报或向编排器上报。

2. Create experiment

2. 创建实验

bash

evo new --parent <parent_id> -m "<your hypothesis>"

Parse the JSON output to get the experiment ID and worktree path.

bash

evo new --parent <parent_id> -m "<your hypothesis>"

解析JSON输出以获取实验ID和工作树路径。

3. Edit the target

3. 编辑目标内容

Read and edit the target file(s) using the full worktree path from

evo new

output (the

"target"

and

"worktree"

fields). Example:

"target": "/path/to/.evo/run_0000/worktrees/exp_0005/src/agent.py"

-- read and edit that exact path.

You may edit anything within the target scope. Do NOT modify benchmark, gate, or framework code.

使用

evo new

输出中的完整工作树路径（

"target"

和

"worktree"

字段）读取并编辑目标文件。示例：

"target": "/path/to/.evo/run_0000/worktrees/exp_0005/src/agent.py"

——读取并编辑该精确路径。

你可在目标范围内编辑任何内容。请勿修改基准测试、校验门或框架代码。

4. Run the experiment

4. 运行实验

bash

evo run <exp_id>

This runs benchmark + gate and prints the result.

bash

evo run <exp_id>

此命令会运行基准测试+校验门并打印结果。

5. Analyze the result

5. 分析结果

evo run

prints one of three outcomes:

COMMITTED
(score improved + gates passed): node locked in. Read failing task traces to find the next weakness. Use this experiment as the parent for your next iteration.
EVALUATED
(score regressed or gate failed): ran cleanly but bad outcome. You decide next step. Read:
- ```
experiments/<id>/attempts/NNN/outcome.json
```
  -- structured record:
```
score
```
  vs
```
parent_score
```
  , per-gate
```
passed
```
  /
```
returncode
```
  , benchmark result, error. Tells you what broke.
- ```
experiments/<id>/attempts/NNN/diff.patch
```
  and
```
benchmark.log
```
  -- tell you why.
Then either:
- Fixable edit-bug (off-by-one, wrong signature): edit the worktree and
```
evo run <id>
```
  again. Bounded by
```
max_attempts
```
  (default 3). Before retrying, compare your planned edit against the previous attempts'
```
outcome.json
```
  on this same node -- if two earlier attempts hit the same gate, a small tweak won't fix it. When the cap is hit, run is refused -- you must discard.
- Hypothesis is wrong, no fix:
```
evo discard <id> --reason "..."
```
  and branch a new experiment from the original parent.
FAILED
(infra error, non-zero exit, timeout): couldn't evaluate. Doesn't consume the retry budget.
- Transient / fixable locally: retry.
- Structural (benchmark broken, evo misconfigured): report to orchestrator and stop.
- Not worth fixing:
```
evo discard <id> --reason "..."
```
  .

evo run

会打印以下三种结果之一：

COMMITTED
（分数提升且通过校验门）：节点已锁定。读取失败任务轨迹以发现下一个薄弱点。将此实验作为下一次迭代的父代。
EVALUATED
（分数倒退或未通过校验门）：运行正常但结果不佳。由你决定下一步操作。读取：
- ```
experiments/<id>/attempts/NNN/outcome.json
```
  ——结构化记录：
```
score
```
  与
```
parent_score
```
  对比、每个校验门的
```
passed
```
  /
```
returncode
```
  、基准测试结果、错误信息。告诉你哪里出了问题。
- ```
experiments/<id>/attempts/NNN/diff.patch
```
  和
```
benchmark.log
```
  ——告诉你为什么出问题。
然后选择：
- 可修复的编辑错误（比如差一错误、错误签名）：编辑工作树并再次运行
```
evo run <id>
```
  。受
```
max_attempts
```
  限制（默认3次）。重试前，将你计划的修改与同一节点之前尝试的
```
outcome.json
```
  进行对比——如果前两次尝试都遇到相同的校验门，小调整无法解决问题。达到上限后，运行会被拒绝——你必须丢弃该实验。
- 假设错误，无法修复：
```
evo discard <id> --reason "..."
```
  并从原始父代分支新的实验。
FAILED
（基础设施错误、非零退出码、超时）：无法完成评估。不消耗重试预算。
- 临时/可本地修复：重试。
- 结构性问题（基准测试损坏、evo配置错误）：向编排器上报并停止操作。
- 不值得修复：
```
evo discard <id> --reason "..."
```
  。

6. Annotate

6. 添加注释

bash

evo annotate <exp_id> "<what you changed, what happened, and why>"

Always annotate so other agents can learn from your experiments.

bash

evo annotate <exp_id> "<what you changed, what happened, and why>"

务必添加注释，以便其他代理能从你的实验中学习。

6b. Add gates for fixed behaviors

6b. 为已修复的行为添加校验门

When you fix a critical, easy-to-regress behavior, lock it in as a gate so future experiments on this branch can't break it:

bash

evo gate add <exp_id> --name "social_eng_resistance" --command "python benchmark.py --agent {target} --task-ids 3"

Good candidates: a specific benchmark task that was hard to fix, a test for a critical policy rule, a smoke test for a fragile behavior. Do NOT gate every passing task -- that over-constrains the search.

当你修复了一个关键且易回归的行为时，将其锁定为校验门，以便该分支上的未来实验不会破坏它：

bash

evo gate add <exp_id> --name "social_eng_resistance" --command "python benchmark.py --agent {target} --task-ids 3"

合适的候选对象：难以修复的特定基准测试任务、关键策略规则的测试、脆弱行为的冒烟测试。请勿为每个通过的任务都添加校验门——这会过度限制搜索范围。

7. Decide: continue or stop

7. 决策：继续或停止

Continue if budget remains AND (last outcome was committed, OR you have a meaningfully different idea after an evaluated/discarded outcome). When continuing after a committed experiment, update your parent to the newly committed ID.

Stop if budget exhausted, infra failure, or you've exhausted variations with no improvement.

如果预算仍有剩余，且（上次结果为COMMITTED，或在EVALUATED/DISCARDED结果后你有明确不同的想法），则继续操作。在COMMITTED实验后继续时，将父代更新为新提交的ID。

如果预算耗尽、出现基础设施故障，或你已尝试所有变体但无改进，则停止操作。

Enriching traces (optional)

丰富轨迹（可选）

Check

.evo/meta.json

for

"instrumentation_mode"

(

"sdk"

"inline"

) to see which style the benchmark uses -- stay consistent with that choice across iterations; do not flip styles mid-run.

SDK mode (
```
from evo_agent import Run
```
): enrich traces by adding
```
run.log(task_id, ...)
```
calls for more observability, or extra fields to
```
run.report()
```
.
Inline mode (benchmark has local
```
log_task
```
/
```
logTask
```
helpers): add fields to the trace dict built inside
```
log_task()
```
.

The trace format is forward-compatible -- extra fields are preserved. Do NOT change the score computation or gate logic -- only add observability.

查看

.evo/meta.json

中的

"instrumentation_mode"

（

"sdk"

或

"inline"

）以了解基准测试使用的样式——在迭代过程中保持该样式一致；请勿中途切换样式。

SDK模式（
```
from evo_agent import Run
```
）：通过添加
```
run.log(task_id, ...)
```
调用以提高可观测性，或向
```
run.report()
```
添加额外字段来丰富轨迹。
Inline模式（基准测试包含本地
```
log_task
```
/
```
logTask
```
助手）：向
```
log_task()
```
内部构建的轨迹字典添加字段。

轨迹格式向前兼容——额外字段会被保留。请勿修改分数计算或校验门逻辑——仅可添加可观测性内容。

Rules

规则

Do NOT run
```
evo init
```
or
```
evo reset
```
```
evo discard <your_exp_id> --reason "..."
```
is your explicit "abandon" action — use it for any node you've decided not to pursue further (pre-run realization, evaluated with a bad hypothesis, or unfixable infra failure). Discard deletes the worktree and branch; the node and its per-attempt artifacts stay in
```
.evo/
```
as a record of what was tried.
Always annotate your experiments, especially before discarding — the annotation is what persists after the worktree is gone.
Stay within your brief's objective and boundaries -- don't drift into unrelated changes

请勿运行
```
evo init
```
或
```
evo reset
```
```
evo discard <your_exp_id> --reason "..."
```
是你明确的“放弃”操作——用于任何你决定不再继续的节点（运行前意识到问题、假设错误导致评估失败、无法修复的基础设施故障）。丢弃操作会删除工作树和分支；节点及其每次尝试的 artifacts 会保留在
```
.evo/
```
中，作为已尝试内容的记录。
务必为你的实验添加注释，尤其是在丢弃前——注释会在工作树删除后继续保留。
遵守任务简报的目标和边界——不要进行无关修改

When Done

完成时

Return a structured summary:

undefined

返回结构化摘要：

undefined

Results

结果

Experiments: <list of exp IDs with scores and status>
Best: <exp_id> with score <N>

实验：<包含分数和状态的实验ID列表>
最佳：<exp_id>，分数<N>

Changes

修改内容

<what you changed in each experiment, briefly>

<每个实验中的修改内容，简要说明>

Learnings

经验总结

<what failure patterns you observed>
<what worked and what didn't>

<你观察到的失败模式>
<有效的方案和无效的方案>

Suggestions

建议

<ideas for the next round that you didn't get to try>

undefined

<你未尝试的下一轮改进思路>

",