skills/paper-result-asset-builder/SKILL.md
Build paper-facing tables and figures from CSV experiment outputs. Use to inventory evidence, aggregate seeds, select result slices, generate LaTeX assets, and record provenance.
npx skillsauth add a-green-hand-jack/ml-research-skills paper-result-asset-builderInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Turn CSV experiment outputs into paper-facing evidence assets: tables, figures, captions/callouts, and provenance records. This skill is the bridge from raw result files to paper artifacts.
Use this skill for:
tables/*.tex, figures/*.pdf, figures/*.png, and figures/*.textable-results-review, figure-results-review, and experiment-story-writerDo not use this skill to decide whether a paper claim is worth making. Use paper-evidence-board or paper-evidence-gap-miner for claim/evidence triage. Do not use it to write the results narrative; use experiment-story-writer after assets exist. Do not use it for experiment-time debugging dashboards.
<installed-skill-dir>/
├── SKILL.md
├── scripts/
│ └── inventory_csv_results.py
├── references/
│ ├── csv-result-contract.md
│ └── paper-asset-rules.md
└── templates/
├── plot_style.yaml
├── result-inventory.md
└── result-asset-provenance.md
references/csv-result-contract.md and references/paper-asset-rules.md.scripts/inventory_csv_results.py to inspect CSV files before reading large result files into context.templates/result-inventory.md when creating paper/.agent/result-inventory.md.templates/result-asset-provenance.md when creating paper/.agent/result-asset-provenance.md.templates/plot_style.yaml when initializing code/config/plot_style.yaml for paper-facing figure generation.paper/.agent/writing-contract.md, paper/.agent/paper-evidence-board.md, paper/.agent/writing-memory/, paper/.agent/evidence-completion-plan.md, paper/.agent/visual-style.md, paper/.agent/style-lessons.md, code/config/plot_style.yaml, figures/*.tex, tables/*.tex, and current result sections when present.paper/.agent/visual-style.md or code/config/plot_style.yaml already defines them.paper-evidence-gap-miner before inventing placeholders.table-results-review or figure-results-review.Search likely locations:
code/docs/results/**/*.csv
code/docs/runs/**/*.csv
code/docs/reports/**/*.csv
code/outputs/**/*.csv
code/results/**/*.csv
outputs/**/*.csv
results/**/*.csv
If the project uses different result paths, follow the user's paths.
Run the inventory script when useful:
python3 <installed-skill-dir>/scripts/inventory_csv_results.py <project-dir>
Save the curated inventory to:
paper/.agent/result-inventory.md
If there is no paper/ directory and the current directory is the paper repo, save to:
.agent/result-inventory.md
For each requested table or figure, write:
- Asset ID:
- Asset type: table / figure
- Paper location:
- Claim supported:
- Reviewer question answered:
- Source CSV files:
- Required rows/slices:
- Metrics:
- Aggregation:
- Uncertainty:
- Styling:
- Output paths:
If this cannot be written, route to paper-evidence-board or paper-evidence-gap-miner.
Use structured CSV parsing, not ad hoc copy/paste.
For tables:
tables/<name>.texFor figures:
paper/.agent/visual-style.md and code/config/plot_style.yaml when present; if both exist, treat the Markdown file as the paper-facing rationale and the YAML file as the machine-readable generation contractfigures/<name>.pdf and optionally figures/<name>.pngfigures/<name>.tex with caption and label scaffoldingUpdate:
paper/.agent/result-asset-provenance.md
For every asset, record:
paper/.agent/visual-style.md or code/config/plot_style.yamlAfter creating assets:
table-results-review for tables/*.texfigure-results-review for figures/*.pdf, figures/*.png, and figures/*.texexperiment-story-writer for result prosepaper-evidence-board with asset-to-claim mappingspaper-writing-memory-manager when new or changed assets affect captions, result prose, abstract, introduction, limitations, or conclusionmemory/provenance-board.md with CSV/report-to-asset traceability, aggregation rules, scripts, and paper locations when project memory existsmemory/source-visibility-board.md or route to submit-paper when generated assets are intended for an author-visible, anonymous, arXiv, camera-ready, or publisher-visible source surfacememory/handoff-board.md with ready handoffs to figure-results-review, table-results-review, experiment-story-writer, or paper-evidence-boardmemory/phase-dashboard.md when the project moves from evidence production to paper asset building or draftingBefore finalizing:
paper-evidence-gap-minertesting
Bootstrap project-local ml-research-skills. Use from global installs when creating a new ML research project, enabling this collection in an existing ML research repo, or deciding whether to install the full bundle locally. Route to project-init for new projects; do not handle paper or experiment work directly.
development
Route project operations tasks — git, memory, bootstrap, remote, workspace, code review, timeline, ops — to the correct skill. Use when the task involves commits, pushes, worktrees, project memory, enabling project-local skills, SSH/server coordination, sidecar runners, or audits. Do not solve the ops task directly.
testing
Route ML/AI paper writing tasks to the correct skill — contract planning, prose drafting, section writing, consistency editing, review simulation, rebuttal, submission, or citation work. Use when the task involves writing, revising, reviewing, or submitting a paper instead of guessing between paper-writing-assistant, paper-writing-contract-planner, paper-reviewer-simulator, auto-paper-improvement-loop, or citation skills. Do not draft prose directly.
data-ai
Project-local router for ML research skill selection. Use inside an initialized ML research project, or while maintaining this skill repo, when the user describes an ML research/paper/experiment/discovery/ops/release workflow and may not know the skill; route to a domain router or high-signal leaf. Do not use for generic non-ML projects.