execution

name	execution
description	Use when experiments are running or just completed, or user shares results and wants to decide what to do next.

If you were dispatched as a subagent to analyze specific results, skip this skill. Analyze and return structured findings immediately.

Execution Skill — Annotate + Decide

Step 0 — Load context and create todos

Read the active session:

ls -t .research-loop/sessions/
cat .research-loop/sessions/<latest>/lab_notebook.md

Summarize the state:

"You're in session [slug]. [N] experiments run. Best so far: [metric]. Last decision: [continue/pivot/kill]."

Create todos with TodoWrite:

Task 1: Annotate run #N — record result, mechanistic explanation, decision
Task 2: Update knowledge graph
Task 3: Write conclusion paragraph (before checking if result matched prediction)
Task 4: Decide — continue / pivot / kill

After each experiment run

Read the latest result:

tail -1 .research-loop/sessions/<slug>/autoresearch.jsonl | python3 -m json.tool

Ask the researcher three questions — one at a time:

Q1:

"What happened? Walk me through the result — metric value, direction, was it what you expected?"

Q2:

"Why do you think it happened? I want a mechanistic explanation, not 'the model improved'. What did the change actually do?"

Q3:

"What does this tell you about the next step?"

Append the full exchange to lab_notebook.md:

## Run #N — <node name>
Date: <date>
Mutation: <what changed>
Result: <metric value> (Δ <delta> from baseline)
Researcher explanation: <their answer to Q2>
Causal annotation: <your synthesis of why>
Decision: <continue / pivot / kill>
Next mutation rationale: <why>

Also append a node to knowledge_graph.md:

## [node name] → [result] → [next]
- Mutation: <what changed>
- Result: <metric> Δ<delta>
- Why it worked/failed: <mechanistic>
- Implication: <what to try next>

Kill/pivot/continue decision

Apply this tree — ask the researcher first, then give your recommendation:

Improved in last 5 runs?
├── YES → continue this direction
└── NO
    ├── > 10 runs total with no improvement?
    │   └── YES → KILL. Update status, move to next hypothesis.
    └── NO → PIVOT. Suggest a different mutation direction.

Show your recommendation explicitly:

"My recommendation: [continue/pivot/kill]. Here's why: [one sentence]."

Update lab_notebook.md status:

## Status
<date>: Run #N complete. Decision: <continue/pivot/kill>. Reason: <why>

When to declare success

Declare success when ALL of these are true:

1. Best metric is meaningfully better than baseline (not noise — run it twice)
2. You can explain WHY in one sentence
3. You have at least 2 negative results that tell you what doesn't work

Then say:

"You have enough to write the paper. Run /write or load the writing-papers skill."