internal/embed/claude/skills/getting-started/SKILL.md
# SKILL: Getting Started > The bootstrap entry point for the Research Loop framework. > Every research session starts here. This skill tells you what skills exist, when to use them, and what the mandatory workflow is. --- ## You have skills. They give you superpowers. <session-start-hook> <EXTREMELY_IMPORTANT> You are operating inside Research Loop — an Agent OS for scientific research. RIGHT NOW, read this file fully before doing anything else. **Core rules:** 1. You have skills. They g
npx skillsauth add moralespanitz/research-loop internal/embed/claude/skills/getting-startedInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The bootstrap entry point for the Research Loop framework. Every research session starts here. This skill tells you what skills exist, when to use them, and what the mandatory workflow is.
You are operating inside Research Loop — an Agent OS for scientific research.
RIGHT NOW, read this file fully before doing anything else.
Core rules:
idea-selection → execution → writing-papers → review-prep in order. Never jump ahead.idea-selection has produced a written, checked hypothesis.execution has produced a complete, annotated experiment record.To list all available skills:
ls skills/*/SKILL.md
To read a skill:
cat skills/<name>/SKILL.md
</EXTREMELY_IMPORTANT> </session-start-hook>
Every investigation follows this sequence. No shortcuts.
idea-selection
│
│ Output: hypothesis.md with core claim, uniqueness axis, best-case conclusion
│
▼
execution
│
│ Output: autoresearch.jsonl with full experiment record, knowledge_graph.md with causal annotations
│
▼
writing-papers
│
│ Output: complete draft with one-sentence core idea, self-contained figures, story-structured intro, conclusion that answers "so what?"
│
▼
review-prep
│
│ Output: submitted paper + rejection protocol documented in lab_notebook.md
▼
| Skill | When to use | Invoke with |
|-------|-------------|-------------|
| idea-selection | At the start of any new investigation; when evaluating whether to continue a stalled one | /idea-selection |
| execution | Throughout the experiment loop; at every checkpoint to kill/pivot/continue | /execution |
| writing-papers | When transitioning from experiments to drafting; when reviewing a draft | /writing-papers |
| review-prep | Before submission; when a rejection arrives; when deciding whether to resubmit | /review-prep |
If you are starting a new investigation:
skills/idea-selection/SKILL.mdhypothesis.md with the outputskills/execution/SKILL.md and start the experiment loopIf you are resuming an investigation:
knowledge_graph.md — understand the full state of what has been tried and whylab_notebook.md — understand where the investigation was leftautoresearch.jsonl — identify the last completed loop stateexecution skill to decide: continue, pivot, or kill?If you are starting to write:
skills/writing-papers/SKILL.md fully before writing a single sentenceIf you are preparing for submission:
skills/review-prep/SKILL.md@reviewer for methodology auditlab_notebook.md before submittingTo understand where you are, read these files in order:
hypothesis.md # What you are trying to prove
knowledge_graph.md # What has been tried and what was learned
lab_notebook.md # Human-readable log of the investigation
autoresearch.jsonl # Machine-readable checkpoint record
If none of these exist, you are starting fresh. Begin with idea-selection.
The framework encoded in these skills is based on one principle:
Write papers with the goal of having an impact. That is what matters, is entirely under your control, and is lots of fun.
A best paper award is one sample from a distribution. You do not control the sampling process — that is determined by the committee, the timing, and who else submitted. What you control is the distribution.
Every skill in this framework is designed to shift that distribution: toward work that is important, well-executed, and clearly communicated. The award, if it comes, is someone noticing where your distribution ended up.
Focus on the distribution.
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.