a-green-hand-jack/experiment-story-writer

Experiment Story Writer

Write experiment and results sections as evidence stories. This skill maps claims to figures, tables, ablations, metrics, and paragraphs, then drafts or revises result prose that explains what the evidence supports.

Use this skill for:

• deciding experiment section order
• writing result paragraphs from tables and figures
• interpreting main results, ablations, diagnostics, efficiency results, and qualitative examples
• handling mixed, negative, or surprising results without undermining the main claim
• marking provisional result placeholders while experiments are running
• aligning result prose with paper claims and evidence status

Do not use this skill to design new experiments from scratch. Use paper-writing-memory-manager to record result-prose dependencies, stale claim locations, and open result-writing threads. Use paper-result-asset-builder when raw CSV results still need to become paper-facing tables or figures. Use paper-evidence-gap-miner when the result story exposes a missing claim support and existing results should be searched before new compute. Use experiment-design-planner for truly new experiment planning, baseline-selection-audit for baseline fairness, figure-results-review for visual audit, table-results-review for table audit, and paper-evidence-board for claim/evidence inventory.

Skill Directory Layout

<installed-skill-dir>/
├── SKILL.md
├── references/
│   ├── result-narrative-patterns.md
│   └── mixed-results-and-placeholders.md
└── templates/
    └── experiment-story-plan.md

Progressive Loading

• Always read references/result-narrative-patterns.md.
• Read references/mixed-results-and-placeholders.md when results are negative, mixed, unstable, provisional, or still running.
• Use templates/experiment-story-plan.md when creating paper/.agent/experiment-story-plan.md.
• Read local paper/.agent/writing-contract.md, paper/.agent/writing-memory/, paper/.agent/paper-evidence-board.md, paper/.agent/evidence-completion-plan.md, paper/.agent/result-inventory.md, paper/.agent/result-asset-provenance.md, paper/.agent/provisional-results.md, paper/.agent/visual-style.md, and figure/table review reports when present.
• Read paper files such as sections/experiments.tex, sections/results.tex, figures/*.tex, tables/*.tex, and experiment logs or reports under docs/results/, docs/reports/, or docs/runs/ when present.

Core Principles

• Result prose should answer a claim question, not merely describe a table.
• The order of experiments should match the paper's contribution hierarchy.
• Main results establish the claim; ablations explain why; diagnostics bound when; efficiency results explain cost; qualitative results make behavior concrete.
• Every result paragraph should name the comparison, metric, setting, and conclusion.
• Result prose should make comparison-affecting compute, data, training duration, tuning budget, hardware, evaluation protocol, and selection rules visible when they affect the claim.
• Do not hide mixed results. Explain how they narrow, qualify, or motivate the claim.
• Do not invent final numbers. Use marked provisional placeholders only as writing scaffolds.
• If a result contradicts a claim, route to result-diagnosis or narrow the claim before writing strong prose.

Step 1 - Build Results Snapshot

Extract:

## Experiment Story Snapshot
- Target venue:
- Paper archetype:
- Primary claims:
- Figures:
- Tables:
- Main metrics:
- Baselines:
- Comparison-affecting protocol details:
- Ablations:
- Verified results:
- User-stated results:
- Provisional or missing results:
- Mixed or negative results:
- Claims at risk:

Use claim IDs when available. If no IDs exist, assign local CLM-TMP-* IDs and suggest syncing to paper-evidence-board.

Step 2 - Choose Section Story

Read references/result-narrative-patterns.md and choose a section order:

• claim-first main results
• benchmark leaderboard plus analysis
• study findings sequence
• mechanism-first ablations
• systems performance stack
• diagnostic or failure-mode sequence

The section order should follow the paper's main claim, not the chronological order in which experiments were run.

Step 3 - Create Experiment Story Plan

Create or update:

paper/.agent/experiment-story-plan.md

If there is no paper/ directory and the current directory is the paper repo, save to:

.agent/experiment-story-plan.md

Use templates/experiment-story-plan.md.

For each figure, table, and result paragraph, record:

• evidence job
• claim supported
• result status
• required interpretation
• risk or caveat

Step 4 - Draft Result Prose

For each paragraph:

• open with the claim question or experiment purpose
• state the setup only as much as needed for interpretation
• describe the most relevant comparison
• interpret why the result supports, narrows, or complicates the claim
• close with the implication for the next experiment or paper claim

Avoid listing numbers without interpretation. Avoid writing around unfairness or hidden advantages. If the result depends on extra compute, data reuse, tuning budget, seed/slice selection, metric choice, or different hardware, disclose the scope or route to baseline-selection-audit / paper-evidence-board before final result prose.

Step 5 - Handle Provisional and Mixed Results

When results are not final:

• use searchable placeholders such as [[PROVISIONAL_RESULT:...]]
• add them to the placeholder ledger
• avoid final superlatives
• write surrounding prose so replacement is localized

When results are mixed:

• say where the method works
• say where it does not
• explain whether the limitation affects the main claim, scope, or future work
• route serious contradictions to result-diagnosis

Step 6 - Final Checks

Before finalizing:

• each main claim has at least one result paragraph
• each main table/figure has a narrative job
• ablations explain mechanism or design choices
• captions and prose do not disagree
• numbers match verified sources or are marked provisional
• mixed results are scoped rather than hidden
• missing blocker evidence is routed first to paper-evidence-gap-miner, then to experiment-design-planner only if existing CSV results cannot fill the gap
• result-claim dependencies and stale locations are written back through paper-writing-memory-manager — record which result paragraphs are drafted, which figures/tables are referenced, which claims now have prose support, and any provisional result placeholders in paper/.agent/provisional-results.md