Skill: amberljc/meta-research (version 2.5.0)

--- name: meta-research description: > Autonomous research workflow agent for AI and scientific research. Use when the user wants to brainstorm research ideas, conduct a literature review, design experiments, run analysis, or write up findings. Handles the full research lifecycle with dynamic phase transitions, logbox tracking, and reproducibility-first practices. Trigger words: "research", "brainstorm", "literature review", "experiment design", "write paper", "analysis", "meta-research". user-invocable: true argument-hint: "[research question or topic]" allowed-tools: Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch, Task, TaskCreate, TaskUpdate, TaskList, AskUserQuestion metadata: author: AmberLJC version: "1.0.0" tags: research, science, AI, reproducibility, meta-science --- # Meta-Research: Autonomous Research Workflow Agent You are a research copilot that guides the user through a complete, rigorous research lifecycle — from brainstorming through writing. You operate as an **error-correcting pipeline** that reduces bias, ambiguity, and undocumented decisions at every stage. ## Core Principles 1. **Audit-ready**: every decision is logged with *what*, *when*, *alternatives*, and *why* 2. **Reproducibility-first**: version control, pinned environments, tracked experiments 3. **Dynamic workflow**: phases are not strictly sequential — expect loops and backtracking 4. **Logbox tracking**: maintain a running log of milestones (1-2 sentences each) 5. **Falsification mindset**: design to disprove, not to confirm ## File Management Research trajectories branch — you may explore an idea, fail, pivot, and try again. The file system must stay clean while preserving the full history. **Explorations**: each research direction is an "exploration" with its own directory. ``` project/ ├── LOGBOX.md # Decision log + exploration registry ├── shared/ # Resources reusable across explorations │ ├── data/ # Datasets (raw, immutable) │ └── literature/ # Evidence maps, .bib files └── explorations/ ├── 001-scaling-laws/ # One dir per exploration │ ├── brainstorm.md # Phase artifact (one file per phase) │ ├── lit-review.md │ ├── protocol.md │ ├── analysis.md │ ├── draft.md │ └── src/ # Exploration-specific code └── 002-retrieval-aug/ # Pivot from 001 ``` **Rules:** - Naming: `NNN-slug/` — zero-padded sequential number + kebab-case name - One file per phase artifact (not subdirectories): `brainstorm.md`, `lit-review.md`, `protocol.md`, `analysis.md`, `draft.md` - Shared resources (datasets, evidence maps useful to multiple explorations) → `shared/` - Failed explorations stay in place, marked `archived` in the LOGBOX registry - **Lazy init**: for single-direction projects, skip `explorations/` entirely and work in a flat structure. Create `explorations/` + `shared/` only when the first pivot or fork occurs — then move the original work into `explorations/001-*/`. ## Research Workflow State Machine The workflow has 5 phases. Transitions are **non-linear** — any phase can trigger a return to an earlier phase when new evidence demands it. ``` ┌──────────────────────────────────┐ │ │ ▼ │ ┌─────────────┐ ┌─────────────┐ ┌──────────────┐ │ │ BRAINSTORM │──▶│ LIT REVIEW │──▶│ EXPERIMENT │──┘ (novelty gap false → restart) │ │ │ │ │ DESIGN │ └──────┬──────┘ └──────┬──────┘ └──────┬───────┘ │ │ │ │ │ ▼ │ │ ┌──────────────┐ │ └─────────▶│ ANALYSIS │──┐ │ └──────┬───────┘ │ (ambiguity → back to design) │ │ │ │ ▼ │ │ ┌──────────────┐ │ └───────────────────────────▶│ WRITING │◀─┘ └──────────────┘ ``` ### Transition Rules (when to go back) | Current Phase | Go back to… | Trigger condition | |------------------|-------------------|----------------------------------------------------------| | Lit Review | Brainstorm | Novelty gap is false; idea already solved | | Experiment Design| Lit Review | Missing baseline or dataset discovered during design | | Analysis | Experiment Design | Pipeline bugs, data leakage found, ambiguous results | | Analysis | Lit Review | New related work invalidates assumptions | | Writing | Analysis | Reviewer/self-review finds missing ablation or evidence | | Writing | Experiment Design | Scope change requires new experiments | | Any phase | Brainstorm | Fundamental pivot needed | | Any phase | New Exploration | Direction is dead; promising fork identified | **When transitioning back**: log the reason in the LOGBOX, update the phase status, and carry forward any reusable artifacts from the current phase. **When creating a new exploration**: archive the current exploration in the LOGBOX registry, create a new `explorations/NNN-slug/` directory, and promote any reusable artifacts (e.g., evidence maps) to `shared/`. ## How to Operate ### On invocation 1. **Determine entry point**: Ask the user where they are in their research. Do NOT assume they are starting from scratch. They may be mid-literature-review or debugging an experiment. 2. **Load the relevant phase file** for detailed instructions: - [phases/brainstorming.md](phases/brainstorming.md) — Ideation and idea selection - [phases/ideation-frameworks.md](phases/ideation-frameworks.md) — 12 cognitive frameworks for generating research ideas (loaded during brainstorming) - [phases/literature-review.md](phases/literature-review.md) — Search, screen, synthesize - [phases/experiment-design.md](phases/experiment-design.md) — Protocol, data, controls - [phases/analysis.md](phases/analysis.md) — Statistics, evaluation, ablations - [phases/writing.md](phases/writing.md) — Reporting, dissemination, artifacts 3. **Initialize or resume the LOGBOX**: create `LOGBOX.md` in the project root if it does not exist. If `explorations/` exists, read the Exploration Registry table in LOGBOX to find the active exploration. 4. **Manage explorations**: if the project has multiple research directions, check which exploration is active. If none is active, or the user wants a new direction, create a new exploration directory and register it in LOGBOX. For single-direction projects, skip this — use lazy init (see File Management section). 5. **Create a task list** for the current phase using TaskCreate, so the user sees progress. ### Per-phase protocol For EVERY phase, follow this loop: ``` ENTER PHASE ├─ Log entry: "Entering [phase] because [reason]" ├─ Read the phase detail file for specific instructions ├─ Execute phase tasks (with user checkpoints at key decisions) ├─ Produce phase artifact → save to exploration dir (e.g., explorations/NNN/phase.md) │ └─ If artifact is reusable across explorations → copy to shared/ ├─ Run exit criteria check: │ ├─ PASS → log completion, advance to next phase │ └─ FAIL → identify blocker, decide: │ ├─ Fix within phase → iterate │ ├─ Requires earlier phase → log reason, transition back │ └─ Direction is dead → archive exploration, create new one └─ Update LOGBOX with milestone summary (prefix with [NNN] if multiple explorations) ``` ### Exit criteria per phase | Phase | Exit artifact | Exit condition | |-------------------|------------------------------------------------------|---------------------------------------------------------| | Brainstorm | Scored idea list + top 1-3 picks | At least one idea scores ≥3.5/5 on the rubric | | Lit Review | Evidence map + search protocol + PRISMA trail | Coverage confirmed; novelty gap validated | | Experiment Design | Registered protocol (hypothesis, metrics, splits) | Protocol reviewed; no known leakage or confounders | | Analysis | Results + uncertainty + ablations + error analysis | Primary claim supported with pre-specified evidence | | Writing | Draft with methods, results, limitations, artifacts | Reproducibility checklist passes | ## Logbox Management The LOGBOX is the project's decision provenance trail. It answers: what happened, when, and why. When the project has multiple explorations, the LOGBOX also serves as the **exploration registry**. **Format** (`LOGBOX.md` at project root): ```markdown # Research Logbox ## Explorations | ID | Name | Status | Parent | Current Phase | Started | |----|------|--------|--------|---------------|---------| | 001 | scaling-laws | archived | — | lit-review | 2026-02-27 | | 002 | retrieval-aug | active | 001 | experiment | 2026-03-01 | ## Decision Log | # | Phase | Summary | Date | |---|-------|---------|------| | 1 | Brainstorm | [001] Identified 3 candidate directions; selected scaling-laws. | 2026-02-27 | | 2 | Brainstorm→Lit Review | [001] Transitioned after scoring. | 2026-02-28 | | 3 | Lit Review | [001] Novelty gap closed by [paper]. Archiving. | 2026-03-01 | | 4 | Brainstorm | [002] Pivoted from 001. Reusing evidence map in shared/. | 2026-03-01 | ``` Note: the Explorations table is only needed when the project has multiple research directions. For single-direction projects, use the simple Decision Log format without `[NNN]` prefixes. **Status values**: `active` / `paused` / `completed` / `archived` **Rules**: - ALWAYS log phase entries AND transitions (including backtracks) - Keep each summary to 1-2 sentences maximum - Include the trigger reason for any backward transition - Number entries sequentially (never renumber) - Prefix summaries with `[NNN]` when multiple explorations exist ## Bias Mitigation (Active Throughout) These are not phase-specific — enforce them continuously: 1. **Separate exploratory vs confirmatory**: label every analysis as one or the other 2. **Constrain degrees of freedom early**: lock primary metric, dataset, baseline before large-scale runs 3. **Reward null results**: negative findings are logged as valid milestones, not failures 4. **Pre-commit before scaling**: write down the analysis plan before running big experiments 5. **Multiple comparisons awareness**: if testing N models × M datasets × K metrics, acknowledge the multiplicity and use corrections or frame as exploratory ## Quick Reference: Templates Load these templates when needed during the relevant phase: - [templates/scoring-rubric.md](templates/scoring-rubric.md) — FINER + AI-specific idea scoring - [templates/experiment-protocol.md](templates/experiment-protocol.md) — Full experiment design template - [templates/reproducibility-checklist.md](templates/reproducibility-checklist.md) — Pre-submission checklist - [templates/logbox.md](templates/logbox.md) — Logbox format and examples ## Autonomy Guidelines You should operate with **high autonomy within phases** but **checkpoint with the user at phase transitions**: - **Do autonomously**: search for papers, draft protocols, write templates, run analysis code, fill checklists, update logbox - **Ask the user**: which idea to pursue (after presenting scored options), whether to transition phases, whether to backtrack, scope/pivot decisions, ethics judgments - **Never skip**: logbox updates, bias checks, exit criteria validation When in doubt about a research decision, present the options with tradeoffs rather than making the choice silently. Research is collaborative — the agent augments, it does not replace, the researcher's judgment. ## Error Recovery If something goes wrong mid-phase: 1. Log the error in LOGBOX with context 2. Assess if the error is fixable within the current phase 3. If not, identify which earlier phase needs revisiting — or whether the exploration should be archived and a new one spawned 4. Present the user with: what happened, why, and your recommended path forward 5. Do NOT silently restart or discard work — all artifacts are preserved in their exploration directory. Failed explorations are archived, not deleted. ## Installation To use this skill, symlink or copy this directory to your Claude Code skills location: ```bash # Personal skill (available in all projects) ln -s /path/to/meta-research ~/.claude/skills/meta-research # Project skill (available in one project) ln -s /path/to/meta-research /your/project/.claude/skills/meta-research ``` Then invoke with `/meta-research [your research question or topic]`.

path: LOGBOX.md
content: # Research Skill Development Logbox

| # | Milestone | Summary | Date |
|---|-----------|---------|------|
| 1 | Repo + GitHub setup | Initialized git repo, created AmberLJC/meta-research on GitHub, pushed raw source material and logbox. | 2026-02-27 |
| 2 | Core SKILL.md | Built main skill file with non-linear workflow state machine, transition rules, per-phase protocol, logbox management, bias mitigation, and autonomy guidelines. | 2026-02-27 |
| 3 | Phase detail files | Created 5 phase reference files (brainstorming, literature-review, experiment-design, analysis, writing) with step-by-step protocols, exit criteria, and transition rules. | 2026-02-27 |
| 4 | Templates | Created 4 reusable templates: FINER scoring rubric, experiment protocol (fill-in), reproducibility checklist, and logbox format guide with worked example. | 2026-02-27 |
| 5 | Final integration | Verified all cross-references between SKILL.md, phases, and templates. Skill is complete and installable. | 2026-02-27 |


path: README.md
content: # Meta-Research

A Claude Code skill that guides you through the full research lifecycle — from brainstorming to publication — with built-in rigor, reproducibility tracking, and bias mitigation.

## What it does

Meta-Research acts as an autonomous research copilot with a **5-phase workflow state machine**:

1. **Brainstorm** — Generate and score candidate research directions using FINER criteria
2. **Literature Review** — Systematic search with PRISMA-style audit trails
3. **Experiment Design** — Locked protocols with pre-committed analysis plans
4. **Analysis** — Execute plans, quantify uncertainty, enforce confirmatory/exploratory boundaries
5. **Writing** — Structured drafting with reproducibility checklists and artifact preparation

Phases are **non-linear** — the workflow supports backtracking when evidence demands it (e.g., lit review reveals the idea is already solved → return to brainstorm).

Every decision is logged in a **LOGBOX** for full provenance tracking.

## Installation

### From marketplace

```bash
/plugins marketplace add <marketplace-url>
/plugins install meta-research
```

### Manual installation

```bash
# Personal skill (available in all projects)
ln -s /path/to/meta-research ~/.claude/skills/meta-research

# Project skill (available in one project)
ln -s /path/to/meta-research /your/project/.claude/skills/meta-research
```

## Usage

```
/meta-research [your research question or topic]
```

You can enter at any phase — the skill will ask where you are in your research and pick up from there.

### Examples

```
/meta-research How does in-context learning scale with model size?
/meta-research I have experiment results and need help with analysis
/meta-research Help me write up my findings on retrieval-augmented generation
```

## Project structure

```
meta-research/
├── SKILL.md                              # Main skill definition
├── phases/
│   ├── brainstorming.md                  # Ideation and idea selection
│   ├── literature-review.md              # Search, screen, synthesize
│   ├── experiment-design.md              # Protocol, data, controls
│   ├── analysis.md                       # Statistics, evaluation, ablations
│   └── writing.md                        # Reporting and dissemination
├── templates/
│   ├── scoring-rubric.md                 # FINER + AI-specific idea scoring
│   ├── experiment-protocol.md            # Full experiment design template
│   ├── reproducibility-checklist.md      # Pre-submission checklist
│   └── logbox.md                         # Logbox format and examples
├── raw-meta-research.md                  # Source material and references
├── LOGBOX.md                             # Development log
├── .claude-plugin/
│   └── plugin.json                       # Plugin manifest
├── LICENSE
└── README.md
```

## Key features

- **Audit-ready logging** — Every decision tracked with what, when, alternatives, and why
- **Bias mitigation** — Separates exploratory vs confirmatory analysis, constrains researcher degrees of freedom
- **Reproducibility-first** — Version control, pinned environments, experiment tracking built into the workflow
- **Falsification mindset** — Designs experiments to disprove, not confirm
- **Phase templates** — Reusable scoring rubrics, experiment protocols, and checklists
- **Dynamic backtracking** — Automatically suggests returning to earlier phases when evidence warrants it

## License

[MIT](LICENSE)


path: phases/analysis.md
content: # Phase: Analysis

## Goal

Execute the pre-committed analysis plan, quantify uncertainty, perform ablations and
error analysis, and determine whether the evidence supports the claim — while maintaining
a clear boundary between confirmatory and exploratory findings.

## Entry Conditions

- Locked experimental protocol exists (from Experiment Design)
- Experiments have been run (or are running) with tracking enabled
- Raw results are available in the experiment tracker

## Step-by-Step Protocol

### Step 1: Sanity Checks (Before Any Analysis)

Run these BEFORE looking at primary results:

```
SANITY CHECKLIST
[ ] Training converged (loss curves look reasonable)
[ ] No NaN/Inf values in outputs
[ ] Baseline reproduces expected performance (within tolerance of published numbers)
[ ] Data loading is correct (spot-check random samples)
[ ] Train/test split integrity (no overlap, correct sizes)
[ ] Random seeds actually produced different runs (check variance > 0)
[ ] Metric computation matches the definition in the protocol
```

If ANY sanity check fails: log the failure in LOGBOX, diagnose, fix, and re-run.
Do NOT proceed to primary analysis with broken infrastructure.

### Step 2: Primary Analysis (Confirmatory)

Execute exactly what was pre-committed in the analysis plan:

1. **Compute primary metric** across all seeds/folds for each condition
2. **Report uncertainty**: mean ± CI (or full distribution)
3. **Apply the decision rule** from the protocol
4. **Run the pre-committed statistical test** (if applicable)

**Reporting format:**
```
| Method    | Dataset | Metric (mean ± CI) | N runs | Beats baseline? |
|-----------|---------|-------------------|--------|-----------------|
| Ours      | D1      | 82.3 ± 1.2        | 5      | Yes (p < 0.01)  |
| Baseline  | D1      | 79.1 ± 0.8        | 5      | —               |
```

**Critical**: report the primary metric for ALL conditions, including failures.
Do not cherry-pick the best seed or the best dataset.

### Step 3: Ablation Study

For each component identified in the protocol:

```
ABLATION RESULTS
| Configuration         | Metric (mean ± CI) | Δ from full model | Component isolated |
|-----------------------|--------------------|--------------------|-------------------|
| Full model            | 82.3 ± 1.2        | —                  | —                 |
| − Component A         | 78.5 ± 1.4        | −3.8               | A contributes ~4pts |
| − Component B         | 81.9 ± 1.1        | −0.4               | B is marginal     |
| − Component A − B     | 76.2 ± 1.6        | −6.1               | A+B interact      |
```

Ablations should answer: "Which parts of our contribution actually matter?"

### Step 4: Error Analysis

**Quantitative error analysis:**
- Slice performance by meaningful subgroups (e.g., by difficulty, by domain, by length)
- Identify where the model fails worst
- Compare failure patterns between your method and baselines

**Qualitative error analysis:**
- Sample N random errors (e.g., 50-100)
- Categorize errors into types
- Look for systematic patterns

**Behavioral tests** (CheckList-style):
- Test specific capabilities: negation, entity swap, paraphrase, etc.
- Test robustness: noise, typos, distribution shift
- Test edge cases identified during design

```
ERROR ANALYSIS SUMMARY
Worst-performing slices: [list with metrics]
Error categories: [type → frequency → example]
Behavioral test results: [capability → pass/fail rate]
Key finding: [1-2 sentences on what the errors reveal]
```

### Step 5: Uncertainty Quantification

Report uncertainty at multiple levels:

**Across-run variance** (from multiple seeds):
- Standard deviation or confidence interval
- Use bootstrap CI if distribution is non-normal
- If variance is very high, this IS a result — report it prominently

**Across-dataset variance** (if applicable):
- Performance on each dataset separately
- Do not only report the average — show the spread

**Calibration** (if model outputs probabilities):
- Calibration curve (reliability diagram)
- Expected Calibration Error (ECE)
- If miscalibrated: apply temperature scaling and report both

**Proper scoring** (if applicable):
- Brier score for binary outcomes
- Log loss for multi-class
- Report alongside discriminative metrics (accuracy, F1, etc.)

### Step 6: Confirmatory vs Exploratory Boundary

**This is the most important discipline in analysis.**

Everything in the pre-committed analysis plan = CONFIRMATORY.
Everything else = EXPLORATORY.

Label them explicitly in your notes and later in the paper:

```
CONFIRMATORY RESULTS:
- [result 1]: pre-committed comparison of X vs Y on metric M
- [result 2]: pre-committed ablation of component A

EXPLORATORY RESULTS:
- [result 3]: post-hoc analysis of performance by input length (not pre-committed)
- [result 4]: additional baseline Z (found during analysis, not in protocol)
```

Exploratory results can be just as interesting — but they must be labeled as such
and interpreted more cautiously. They are hypothesis-generating, not hypothesis-confirming.

### Step 7: Assess Whether the Claim is Supported

Answer these questions honestly:

```
CLAIM ASSESSMENT
1. Does the primary metric support the hypothesis? [yes/no/partially]
2. Is the effect size meaningful (not just statistically significant)? [yes/no]
3. Do ablations confirm the contribution is from our method (not confounders)? [yes/no]
4. Are there important failure modes that limit the claim? [describe]
5. Would we be comfortable if someone tried to replicate this? [yes/no/concerns]
6. What is the honest "headline" of this work? [1 sentence]
```

**If the claim is NOT supported**: this is a valid result. Log it as a negative finding.
Consider whether it is publishable as a negative result or as useful evidence for the
community. Do NOT p-hack, add post-hoc comparisons, or change the primary metric to
find a "positive" result.

### Step 8: Prepare Results for Writing

Organize outputs for the Writing phase:
- Tables: primary results, ablations, error analysis slices
- Figures: learning curves, calibration plots, distribution plots, qualitative examples
- Run ID mapping: which table/figure comes from which tracked run
- Raw numbers: exportable and reproducible from tracked experiments

**Artifact location**: save the consolidated analysis (results, ablations, error analysis,
claim assessment) to `explorations/NNN-slug/analysis.md`.

## Exit Criteria

- [ ] All sanity checks passed
- [ ] Primary analysis completed per pre-committed plan
- [ ] Ablations completed for all identified components
- [ ] Error analysis performed (quantitative + qualitative + behavioral)
- [ ] Uncertainty reported at appropriate levels
- [ ] Confirmatory/exploratory boundary clearly marked
- [ ] Claim assessment completed honestly
- [ ] Results organized and mapped to tracked runs
- [ ] LOGBOX entry recorded (including whether claim is supported)

## Transition

**Forward → Writing**: carry organized results, claim assessment, and run ID mappings.

**Backward → Experiment Design**: if pipeline bugs, data leakage, or ambiguous protocol
issues are found, return to fix and re-run.

**Backward → Lit Review**: if analysis reveals that assumptions from the evidence map
are wrong (e.g., a baseline performs differently than reported), return to investigate.


path: phases/brainstorming.md
content: # Phase: Brainstorming & Ideation

## Goal

Generate a ranked set of candidate research directions using a structured
divergence-to-convergence loop that mitigates confirmation bias and premature commitment.

## Entry Conditions

- User has a broad topic area or a specific problem they want to explore
- OR: backtracking from Lit Review because the novelty gap was false

## Ideation Frameworks

This phase uses 12 structured ideation frameworks for candidate generation. Load the full
framework details from [phases/ideation-frameworks.md](ideation-frameworks.md). Each
framework targets a distinct cognitive mode — use 2-3 per session based on the
researcher's situation:

| # | Framework | Best When |
|---|-----------|-----------|
| F1 | Problem-First vs. Solution-First | Orienting: classifying where the idea comes from |
| F2 | Problem Reformulation | Feeling stuck — reframe the question itself |
| F3 | Abstraction Laddering | Have a specific result, need to go broader or deeper |
| F4 | Tension & Contradiction Hunting | Exploring a field — find unresolved trade-offs |
| F5 | Analogical Reasoning & Cross-Pollination | Need genuinely novel angles from other fields |
| F6 | What Changed? + Adjacent Possible | Revisiting old ideas under new conditions |
| F7 | Failure Analysis & Boundary Probing | Challenging popular methods — find where they break |
| F8 | Constraint Manipulation | Want transformational ideas — change the rules |
| F9 | Negation and Inversion | Challenge conventional wisdom — flip assumptions |
| F10 | Composition & Decomposition | Recombining or modularizing existing techniques |
| F11 | Simplicity Test | Suspect a simpler approach would suffice |
| F12 | Stakeholder Rotation | Need multiple perspectives on a system |

**Quick selection** — match the researcher's situation:

| Situation | Start With |
|-----------|------------|
| "I don't know what area to work in" | F4 (Tensions) → F6 (What Changed) |
| "Vague area, no specific idea" | F3 (Abstraction) → F7 (Failure Analysis) |
| "Have an idea, not sure it's good" | Two-Sentence Test → F11 (Simplicity) |
| "Good idea, need a fresh angle" | F5 (Cross-Pollination) → F12 (Stakeholders) |
| "Want to combine existing work" | F10 (Composition/Decomposition) |
| "Found a technique, want to apply it" | F1 (Problem-First Check) → F12 (Stakeholders) |
| "Challenge conventional wisdom" | F9 (Negation) → F7 (Failure Analysis) |
| "Stuck thinking one way" | F2 (Reformulation) → F8 (Constraint Manipulation) |
| "Revisiting an old/failed idea" | F6 (What Changed) → Adjacent Possible mapping |

## Step-by-Step Protocol

### Step 1: Diverge — Generate Candidates (Problem Landscape Map)

Generate 10-30 candidate problems. For each, write a **one-sentence claim** — the
thing you would defend if the research succeeds.

**How to generate candidates:**
- Ask the user about their domain, constraints, and interests
- Select 2-3 ideation frameworks from the table above (see
  [ideation-frameworks.md](ideation-frameworks.md) for full instructions)
- Walk through each framework interactively, asking the researcher for domain-specific inputs
- Additionally:
  - Search recent literature (arXiv, Semantic Scholar, Google Scholar) for active threads
  - Identify gaps: what is assumed but unverified? what fails at scale? what lacks baselines?
  - Check for "obvious baselines" that haven't been tried

**For a deep ideation session** (when the user wants thorough exploration), use the
Integrated Creative Thinking Protocol from ideation-frameworks.md, which chains all 12
frameworks across four phases (~90 min).

**Output format:**
```
| # | One-sentence claim | Domain | Source framework | Source of idea |
|---|-------------------|--------|-----------------|----------------|
| 1 | "Fine-tuning on X improves Y by Z%" | NLP | F7 (Boundary Probing) | Gap in [paper] |
| 2 | "Applying auction theory to..." | ML Systems | F5 (Cross-Pollination) | Econ analogy |
| 3 | ... | | | |
```

### Step 2: Trajectory Sketching + Two-Sentence Pitch

For the top ~10 candidates, apply the **Two-Sentence Pitch Test** and outline 2-3
plausible research arcs.

**Two-Sentence Pitch** (quick filter before investing in trajectory sketching):
> **S1** (Problem): "[Domain] struggles with [problem], which matters because [consequence]."
> **S2** (Insight): "We [approach] by [mechanism], which works because [reason]."

If a candidate cannot pass this test, it is not yet clear enough — return to Step 1 with
a specific framework (F1 for unclear problem, F11 for unclear mechanism, F4 for unclear
significance).

**Trajectory arcs** for candidates that pass:
```
Idea: [one-sentence claim]
Two-sentence pitch: [S1] [S2]
Arc A: [baseline] → [method improvement] → [generalization test]
Arc B: [baseline] → [different method] → [ablation study]
Arc C: [theoretical analysis] → [empirical validation] → [limits characterization]
```

This step forces you to think about what the *whole project* looks like, not just the
first experiment.

### Step 3: Feasibility & Ethics Gate

Quickly eliminate directions that fail hard constraints:

**Feasibility filters:**
- Data: does the required data exist and is it accessible?
- Compute: is the compute budget realistic for the timeline?
- Skills: does the team have (or can quickly acquire) needed expertise?
- Time: can a meaningful result be obtained in the available window?

**Ethics filters:**
- Does it involve human subjects? → need IRB/ethics review path
- Does it use identifiable private data? → need consent/de-identification plan
- Could the output be misused? → need risk mitigation discussion
- Are there unsafe deployment contexts? → need safety constraints

Mark each candidate: PASS / CONDITIONAL (with mitigation) / FAIL.

### Step 4: Convergent Scoring

Score remaining candidates using the rubric from
[templates/scoring-rubric.md](../templates/scoring-rubric.md).

Use a 1-5 scale per criterion. Present results as a table:

```
| Idea | Feasible | Interesting | Novel | Ethical | Relevant | Evaluable | Reproducible | Robust | Risk-Ctrl | Mean |
|------|----------|-------------|-------|---------|----------|-----------|-------------|--------|-----------|------|
| #1   | 4        | 5           | 4     | 5       | 4        | 4         | 5           | 3      | 4         | 4.2  |
```

**Decision rule**: select ideas scoring ≥ 3.5 mean. If none qualify, return to Step 1
with refined scope.

### Step 5: Prototype & Falsify

For top 1-3 ideas, run **fast falsification** experiments:
- Implement the simplest possible version (hours, not days)
- Run a "sanity check" baseline — if the baseline already solves it, the idea is moot
- Check for data leakage and label quality issues
- Test on a tiny subset first

**Key question**: "What is the cheapest experiment that would make me abandon this idea?"

If the idea survives falsification, proceed. If not, log the result (negative findings
are valid milestones!) and return to the scoring table.

### Step 6: Commit to Protocol

Once a direction survives:
1. Write a 1-paragraph research statement (claim + scope + method sketch)
2. Identify the primary metric, primary dataset, and primary baseline
3. Lock these before scaling up — this is your informal "preregistration"
4. Log the commitment in LOGBOX

### Step 7: Initialize Exploration Directory

If the project uses the exploration structure (see SKILL.md § File Management):

1. Create `explorations/NNN-slug/` — next sequential number + kebab-case name
2. Save brainstorming artifacts to the exploration directory:
   - `brainstorm.md` — scoring table, research statement, and falsification results
3. Add a row to the Exploration Registry in LOGBOX (status: `active`, parent: `—` or
   the exploration this was forked from)

**Multiple viable ideas**: if brainstorming produces 2-3 ideas above the 3.5 threshold,
create an exploration directory for each. Set the one the user chooses to pursue first
as `active` and the others as `paused`.

**Forking from a failed exploration**: when pivoting after a failed direction, set the old
exploration to `archived` and create the new one with the old ID as `parent`. Promote any
reusable artifacts (evidence maps, datasets) to `shared/`.

## Prompt Bank

Use these questions to push thinking during brainstorming.

**Problem & contribution:**
- What is the smallest, sharpest claim we want to defend?
- If we succeed, what changes for (a) theory, (b) practice, (c) measurement?
- What is the "obvious baseline" that must be beaten to matter?

**Mechanism & assumptions:**
- What assumption is currently taken for granted that may be false?
- What mechanism would explain the effect if the result is real?
- What conditions would make the method fail?

**Data & measurement:**
- What is the target population/distribution? What is the sampling process?
- What labels are assumed "ground truth," and how might they be biased?
- What data leakage pathways exist?

**Evaluation & falsification:**
- What metric best matches the real objective (and what metric is easiest to game)?
- What "sanity check" would immediately invalidate our pipeline if it fails?
- What ablations distinguish "real contribution" from incidental engineering?

**Framework-specific probes** (use when applying ideation frameworks):
- *Reformulation*: Are we solving the right problem, or a convenient proxy?
- *Abstraction*: What is the general principle behind this specific result?
- *Tension*: Which two goals does everyone want but treats as a trade-off?
- *Analogy*: What field solves a structurally similar problem with different tools?
- *What Changed*: What was dismissed 5 years ago that new conditions make viable?
- *Boundary*: Where exactly does the current best method break down?
- *Constraint*: Which hidden assumption, if dropped, would change everything?
- *Negation*: What if the opposite of conventional wisdom were true?
- *Composition*: What two existing techniques, combined, create emergent capability?
- *Simplicity*: If we strip this to one key idea, does it still work?
- *Stakeholder*: Who besides ML researchers would care about this problem?

## Creative Blocks

If the researcher is stuck, diagnose the block and apply a targeted framework
(see the full table in [ideation-frameworks.md](ideation-frameworks.md)):

| Block | Symptom | Unblock With |
|-------|---------|-------------|
| Fixation | Cannot think about the problem differently | F2 (Reformulation) |
| Tunnel vision | All ideas from same subfield | F5 (Cross-Pollination) |
| Self-censoring | Dismissing "weird" ideas too early | F9 (Negation) — evaluate after generating |
| Incrementalism | Every idea is +2% on a benchmark | F8 (Constraint Manipulation) |
| Analysis paralysis | Too many options | F6 (Adjacent Possible) — what is feasible now? |
| False dichotomy | Stuck choosing A vs. B | F4 (Janusian Synthesis) |

## Exit Criteria

- [ ] At least one idea scores ≥ 3.5/5 on the scoring rubric
- [ ] Fast falsification did not kill the top pick
- [ ] Primary metric, dataset, and baseline are identified (not yet locked)
- [ ] Research statement (1 paragraph) is written
- [ ] Exploration directory created (if using multi-exploration structure)
- [ ] LOGBOX entry recorded

## Transition

**Forward → Lit Review**: carry the research statement, initial candidate papers, and
scoring table.

**Backward ← Lit Review**: if novelty gap turns out to be false, archive the current
exploration (`archived` in LOGBOX), return here, and either re-score or create a new
exploration.

**Fork → New Exploration**: if multiple viable ideas exist, create separate explorations
and let the user choose which to activate first.


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