src/autoskillit/skills_extended/prepare-research-pr/SKILL.md
Reads a research report and experiment plan, synthesizes a recommendation, selects 1-2 exp-lens lenses, writes a context file per lens, and writes a PR prep file. Does NOT open a PR. Part 1 of 3 in the decomposed research-PR flow.
npx skillsauth add talont-org/autoskillit prepare-research-prInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Reads a completed experiment report and plan, synthesizes a directional
recommendation, selects 1-2 exp-lens lenses, writes a context file per lens,
and writes a PR prep file for downstream use by compose-research-pr.
Does NOT invoke any exp-lens skills or create a PR.
/autoskillit:prepare-research-pr {report_path} {experiment_plan_path} {worktree_path} {base_branch}
research/{slug}/README.md (post-rename by commit_research_artifacts)${{ context.experiment_plan }})NEVER:
{{AUTOSKILLIT_TEMP}}/prepare-research-pr/ (relative to the current working directory)run_in_background: true is prohibited)ALWAYS:
prep_path, selected_lenses, and lens_context_paths as your final output| experiment_type | Primary Lens | Secondary Lens (optional) | |---|---|---| | benchmark | exp-lens-fair-comparison | exp-lens-estimand-clarity | | configuration_study | exp-lens-iterative-learning | exp-lens-sensitivity-robustness | | causal_inference | exp-lens-causal-assumptions | exp-lens-estimand-clarity | | robustness_audit | exp-lens-severity-testing | exp-lens-validity-threats | | exploratory | exp-lens-estimand-clarity | exp-lens-exploratory-confirmatory |
CONCLUSIVE_POSITIVE — hypothesis supported; results meet success criteriaCONCLUSIVE_NEGATIVE — hypothesis refuted; results clearly below thresholdINCONCLUSIVE — results ambiguous; insufficient signal or mixed evidenceFAILED — experiment could not be completed; results invalidParse positional args:
Derive feature_branch:
FEATURE_BRANCH=$(git -C "{worktree_path}" rev-parse --abbrev-ref HEAD)
Create temp directory:
mkdir -p {{AUTOSKILLIT_TEMP}}/prepare-research-pr/
Generate a timestamp ts (format: YYYY-MM-DD_HHMMSS) for unique file naming.
Spawn an Explore subagent to read {report_path} and extract:
title: fieldStore extracted content as report_content.
Spawn an Explore subagent to read {experiment_plan_path} and extract:
experiment_type (benchmark / configuration_study / causal_inference / robustness_audit / exploratory)Store extracted content as plan_content.
Spawn a sonnet subagent with the report's Conclusions, Recommendations, and
experiment status. Produce a 1-3 sentence directional recommendation (inverted pyramid —
most important finding first). Store as recommendation.
Spawn a sonnet subagent with experiment_type and the plan's IV/DV structure.
Select 1-2 exp-lens slugs using the Lens Selection Table above.
Output: a list of 1-2 slugs, e.g. ["fair-comparison", "estimand-clarity"].
Store as selected_lens_slugs.
For each slug in selected_lens_slugs, write a context file to:
{{AUTOSKILLIT_TEMP}}/prepare-research-pr/exp_lens_context_{slug}_{ts}.md
The context file must contain (enough for the lens to build its diagram without reading the entire CWD):
# Experiment Context: {slug} Lens
## Hypotheses
| | Hypothesis |
|---|---|
| H0 | {null hypothesis} |
| H1 | {alternative hypothesis} |
## Independent Variables
| Variable | Values / Levels | Role |
|----------|-----------------|------|
{rows from plan's IV table}
## Dependent Variables / Metrics
| Metric | Unit | Threshold |
|--------|------|-----------|
{rows from plan's DV table}
## Controlled Variables
{list from plan}
## Success Criteria
{from plan}
## Experiment Type
{experiment_type}
Record the absolute paths in lens_context_paths list (same order as slugs).
From the report's Results section, construct:
Store as results_summary (plain text, will be embedded in prep file).
Write to {{AUTOSKILLIT_TEMP}}/prepare-research-pr/pr_prep_{ts}.md:
# PR Prep: {title from report}
## Metadata
- report_path: {absolute path}
- experiment_plan_path: {absolute path}
- feature_branch: {branch}
- base_branch: {branch}
- experiment_type: {type}
- status_badge: {CONCLUSIVE_POSITIVE | CONCLUSIVE_NEGATIVE | INCONCLUSIVE | FAILED}
## Recommendation
{recommendation from Step 3}
## Results Summary
{results_summary from Step 6}
## Hypothesis Table
| | Hypothesis |
|---|---|
| H0 | {H0} |
| H1 | {H1} |
## Metrics Table
| Metric | Unit | Threshold |
|--------|------|-----------|
{DV rows}
## Methodology
{condensed from plan's Methodology section}
## Selected Lenses
{comma-separated slugs}
## Lens Context Paths
{comma-separated absolute paths}
Store the absolute path as prep_file_path.
Emit these tokens as literal plain text (no markdown formatting on the token names) as your final output:
prep_path = /absolute/path/{{AUTOSKILLIT_TEMP}}/prepare-research-pr/pr_prep_{ts}.md
selected_lenses = fair-comparison,estimand-clarity
lens_context_paths = /abs/ctx_fair-comparison_{ts}.md,/abs/ctx_estimand-clarity_{ts}.md
Where:
selected_lenses is a comma-separated list of lens slugs (no spaces)lens_context_paths is a comma-separated list of absolute context file paths in the
same order as selected_lensesdevelopment
Generate YAML recipes for .autoskillit/recipes/. Use when user says "make script skill", "generate script", "script a workflow", "write a script", "create a script", "new recipe", "write a pipeline", or when loaded by other skills for script formatting.
data-ai
Create Uncertainty Representation visualization planning spec showing error bar definitions, distribution-aware alternatives, and multi-seed variance protocols. Statistical lens answering "How is uncertainty honestly represented?"
data-ai
Create Temporal Dynamics visualization planning spec showing axis scaling (linear vs log), smoothing disclosure, epoch/step alignment, run aggregation (mean + variance bands), early-stopping markers, and wall-clock vs step-count x-axis. Temporal lens answering "Are training dynamics shown clearly and honestly?"
data-ai
Create Narrative Story Arc visualization planning spec showing visual consistency across the report (same color = same model everywhere), logical figure progression, redundant figure detection, and narrative dependency between figures. Narrative lens answering "Do the figures tell a coherent story across the report?"