experiment-running

Agent Building

Execute the plan by dispatching fresh subagents per task, monitoring status, and collecting results

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How to use this skill

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/experiment-running/SKILL.md

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Strategy: Experiment Running

Key Question: How to execute?

Methodology

实现的执行直接交给 superpowers 现成链路，不再自写 fresh-subagent / 三段 review。 plan（上游 plan-writing 产出）就绪后，本策略是一串决策节点：

Skill load superpowers:using-git-worktrees —— 建隔离工作区 + 跑 baseline 测试。
Skill load ponytail:ponytail —— 进入写代码前开启精简反射（边写边 lean）。
二选一执行引擎：
- superpowers:executing-plans（本 session 批量执行，带 checkpoint），或
- superpowers:subagent-driven-development（每任务 fresh 子代理 + 两段 review）。多数实验实现用前者；任务高度独立、需强隔离时用后者（详见 subagent-execution-loop）。
Skill load superpowers:verification-before-completion —— claim 完成前先跑证明命令。
Skill load ponytail:ponytail-debt —— 收尾前收集 ponytail: 欠债标记。
Skill load superpowers:finishing-a-development-branch —— 验证测试 → merge/PR/branch。

DARE 原生的 checkpoint-and-recover（高风险操作前存档）与 subagent-execution-loop （执行循环细节）作为 tactic 仍在编排内保留。

Execution Flow

[plan from plan-writing]
    → superpowers:using-git-worktrees   (隔离区 + baseline)
    → ponytail:ponytail                 (精简反射开启)
    → superpowers:executing-plans  或  superpowers:subagent-driven-development
    → superpowers:verification-before-completion  (claim 前验证)
    → ponytail:ponytail-debt            (收欠债)
    → superpowers:finishing-a-development-branch  (收尾)

Budget Gate

Step	Max Budget	Output
Per-task execution	50% of execution budget / N tasks	Task result
Monitoring overhead	5% of execution budget	Status log
Retry budget	10% of execution budget	Unblocked tasks

Key Decisions

执行引擎二选一：批量 → executing-plans；强隔离/逐任务审查 → subagent-driven-development
model 选择 / retry / 并行：交给所选 superpowers 引擎，不在本策略重定义
Abort：>50% 关键路径 BLOCKED 时中止并报告（DARE 排程层判据）

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
checkpoint-and-recover	Checkpoint state before risky operations, detect anomalies, and recover gracefully
subagent-execution-loop	Orchestrate task execution via fresh subagents with dispatch, monitoring, and result collection

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
execution-monitoring	Monitor execution progress, detect anomalies, and report status
implementer-dispatch	Dispatch execution subagent — select model by complexity, construct prompt with full task context
ponytail:ponytail	Lazy-senior reflex: simplest thing that holds; mark every deliberate shortcut
ponytail:ponytail-debt	Harvest ponytail debt markers before finishing
result-collection	Collect experiment outputs — metrics, logs, artifacts — into structured result set
superpowers:executing-plans	Execute the plan task-by-task in the current session with checkpoints
superpowers:finishing-a-development-branch	Verify tests -> merge / PR / branch cleanup
superpowers:subagent-driven-development	Execute the plan via a fresh subagent per task with two-stage review
superpowers:using-git-worktrees	Create an isolated worktree + run baseline tests before implementing
superpowers:verification-before-completion	Run the proving command and confirm output before claiming done