ablation-planner

Q: How do I install ablation-planner?

Run npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill ablation-planner in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ablation-planner/ (personal) or .claude/skills/ablation-planner/ (project).

by wanshuiyinProductivity & Automation

Use when main results pass result-to-claim (claimsupported=yes or partial) and ablation studies are needed for paper submission.

Installs: 511
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill ablation-planner

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads ablation-planner automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Ablation Planner

Systematically design ablation studies that answer the questions reviewers will ask. Codex leads the design (reviewer perspective), CC reviews feasibility and implements.

Context: $ARGUMENTS

When to Use

Main results pass /result-to-claim with claim_supported = yes or partial
User explicitly requests ablation planning
/auto-review-loop reviewer identifies missing ablations

Workflow

Step 1: Prepare Context

CC reads available project files to build the full picture:

Method description and components (from idea-stage/docs/research_contract.md, legacy docs/research_contract.md, or project CLAUDE.md)
Current experiment results (from EXPERIMENT_LOG.md, EXPERIMENT_TRACKER.md, or W&B)
Confirmed and intended claims (from result-to-claim output or project notes)
Available compute resources (from CLAUDE.md server config, if present)

Step 2: Codex Designs Ablations

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are a rigorous ML reviewer planning ablation studies.
    Given this method and results, design ablations that:

    1. Isolate the contribution of each novel component
    2. Answer questions reviewers will definitely ask
    3. Test sensitivity to key hyperparameters
    4. Compare against natural alternative design choices

    Method: [description from project files]
    Components: [list of removable/replaceable components]
    Current results: [key metrics from experiments]
    Claims: [what we claim and current evidence]

    For each ablation, specify:
    - name: what to change (e.g., "remove module X", "replace Y with Z")
    - what_it_tests: the specific question this answers
    - expected_if_component_matters: what we predict if the component is important
    - priority: 1 (must-run) to 5 (nice-to-have)

    Also provide:
    - coverage_assessment: what reviewer questions these ablations answer
    - unnecessary_ablations: experiments that seem useful but won't add insight
    - suggested_order: run order optimized for maximum early information
    - estimated_compute: total GPU-hours estimate

Step 3: Parse Ablation Plan

Normalize Codex response into structured format:

## Ablation Plan

### Component Ablations (highest priority)
| # | Name | What It Tests | Expected If Matters | Priority |
|---|------|---------------|---------------------|----------|
| 1 | remove module X | contribution of X | performance drops on metric Y | 1 |
| 2 | replace X with simpler Z | value of learned vs fixed | drops, especially on dataset A | 2 |

### Hyperparameter Sensitivity
| # | Parameter | Values to Test | What It Tests | Priority |
|---|-----------|---------------|---------------|----------|
| 3 | lambda | [0.01, 0.1, 1.0] | sensitivity to regularization | 3 |

### Design Choice Comparisons
| # | Name | What It Tests | Priority |
|---|------|---------------|----------|
| 4 | joint vs separate matching | whether joint adds value | 4 |

### Coverage Assessment
[What reviewer questions these ablations answer]

### Unnecessary Ablations
[Experiments that seem useful but won't add insight — skip these]

### Run Order
[Optimized for maximum early information]

### Estimated Compute
[Total GPU-hours]

Step 4: CC Reviews Feasibility

Before running anything, CC checks:

Compute budget: can we afford all ablations with available GPUs?
Code changes: which ablations need code modifications vs config-only changes?
Dependencies: which ablations can run in parallel?
Cuts: if budget is tight, propose removing lower-priority ablations and ask Codex to confirm

Step 5: Implement and Run

Create configs/scripts for each ablation (config-only changes first)
Smoke test each ablation before full run
Run in suggested order, using descriptive names (e.g., ablation-no-module-X)
Track results in EXPERIMENT_LOG.md
After all ablations complete → update findings.md with insights

Rules

Codex leads the design. CC does not pre-filter or bias the ablation list before Codex sees it. Codex thinks like a reviewer; CC thinks like an engineer.
Every ablation must have a clear what_it_tests and expected_if_component_matters. No "just try it" experiments.
Config-only ablations take priority over those needing code changes (faster, less error-prone).
If total compute exceeds budget, CC proposes cuts and asks Codex to re-prioritize — don't silently drop ablations.
Component ablations (remove/replace) take priority over hyperparameter sweeps.
Do not generate ablations for components identical to the baseline (no-op ablations).
Record all ablation results in EXPERIMENT_LOG.md, including negative results (component removal had no effect = important finding).

FAQ

What does the ablation-planner skill do?

Use when main results pass result-to-claim (claimsupported=yes or partial) and ablation studies are needed for paper submission.

How do I install ablation-planner?

Run npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill ablation-planner in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ablation-planner/ (personal) or .claude/skills/ablation-planner/ (project).

Which AI agents can use ablation-planner?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is ablation-planner safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains ablation-planner?

It is published by wanshuiyin in wanshuiyin/auto-claude-code-research-in-sleep. It has 511 recorded installs.