skills/plan/SKILL.md
Use when user has a selected hypothesis or route and needs a concrete execution plan broken into tasks. Triggered by "what are the next steps", "how do I start", "plan this out".
npx skillsauth add moralespanitz/research-loop planInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The difference between a plan that works and one that doesn't is specificity. "Set up the codebase" is not a task. "Fork IDSIA/recurrent-fwp, rename to dopamine-read-gate, run python train.py to confirm baseline, record output" is a task.
Read the lab notebook to understand exactly where the researcher is:
cat .research-loop/sessions/<slug>/lab_notebook.md
Summarize back to the researcher:
"You've selected [hypothesis/route]. You have [what's done]. The next phase is [what's needed]."
Ask one clarifying question if anything is ambiguous. Then proceed.
Break the work into tasks. Each task must have:
Format:
## Execution Plan — [hypothesis name]
Date: <date>
### Phase: [name]
**Task 1: [action]**
- Do: [exact steps]
- Verify: [how you know it worked]
- Time: [estimate]
**Task 2: [action]**
- Do: [exact steps]
- Verify: [how you know it worked]
- Time: [estimate]
...
Use TodoWrite to create one todo per task. Mark the first one as in_progress.
Show the researcher the plan and ask:
"Does this cover everything? Any task that's unclear or needs breaking down further?"
Wait for approval. Then say:
"Starting Task 1. Tell me when you're ready."
As each task completes:
[date]: Task [N] complete — [what was done]If you catch yourself writing any of these, stop and break it down further.
testing
Plan and execute a structured replication workflow for a paper, claim, or benchmark with environment selection and integrity checks.
testing
End-to-end paper generation pipeline ported from AutoResearchClaw (Aiming Lab). 14 phases covering topic initiation through export/publish, with human- in-the-loop gates and quality gating at each handoff. Use this when the user wants a full paper pipeline run — topic to submission-ready manuscript. Delegates to researcher/reviewer/writer/verifier subagents for stage execution and to autonomous-iteration for experiment optimization loops.
testing
Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis.
development
Publication-quality figure generation for research papers. Decision agent selects figure type (code plot vs architecture diagram). Generates Matplotlib/Seaborn code for quantitative figures with iterative improvement loop. Style-matches conference templates (NeurIPS, ICML, ICLR). Use when the paper-pipeline reaches the figure generation phase, or when a user requests figures for an existing draft.