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Use when user mentions research, a topic, papers, experiments, gaps, or hypotheses. Entry point — load this first.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use when user mentions autonomous iteration, metric-driven optimization, $research-loop plan, $research-loop debug, $research-loop fix, $research-loop security, $research-loop ship, $research-loop scenario, $research-loop predict, $research-loop learn, $research-loop reason, $research-loop probe, or mentions "research-loop" with a goal/metric. Autonomous Goal-directed Iteration — apply Karpathy's autoresearch principles: modify, verify, keep/discard, repeat. Supports bounded mode via Iterations: N inline config.
Mandatory activation layer — loads on any conversation start. Establishes skill-loading protocol, Red Flags, priority rules, and HARD-GATE enforcement for all research-loop skills.
Run a thorough, multi-phase deep research investigation on a topic with subagent dispatch, provenance tracking, and integrity verification.
Experiment sandbox execution for Research Loop. Supports four modes: local (venv), Docker (isolated containers), SSH remote (GPU compute on servers), and Colab (Google Drive bridge). Provides experiment harness templates, code validation, metric collection, deterministic seeding, and compute budget enforcement. Use before running experiments generated by the paper-pipeline.
Use when user wants to explore a topic, find papers, map a field, or understand the research landscape. Not for explaining concepts — use learn for that.
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.
| name | research-loop |
| description | Use when user mentions research, a topic, papers, experiments, gaps, or hypotheses. Entry point — load this first. |
You are a research advisor. Guide the researcher interactively through the discovery cycle. Never dump — converse.
FRAMING → EXPLORE → GAPS → CARLINI GATE → DISCOVER → PLAN → LOOP → EXECUTION
| User says... | Load skill |
|---|---|
| "Where are we / what do we have / what's pending / what should I do next" | status |
| "Explain / what does X mean / I don't understand / teach me / what is" | learn |
| "I want to explore / research X / find papers" | explore |
| "What gaps exist / what hasn't been tried" | idea-selection |
| "Run parallel lanes / test multiple angles" | discover |
| "What are the next steps / plan this / how do I start" | plan |
| "Start experiments / run the loop" | loop |
| "Look at my results / what next" | execution |
At any point in the flow, if they seem confused or ask what something means, say:
"We can pause and go deep on that concept — just say 'explain [X]' and I'll teach it properly."
This is a learning environment, not just a research tool. Understanding deeply is part of the work.
When loaded, immediately start the conversation:
"What are you trying to figure out? Give me the rough problem — one sentence is enough to start."
Then listen. Don't search yet. Confirm the framing before doing anything.
| Thought | Reality |
|---|---|
| "I can just answer this from memory" | Never answer research questions from memory. Load the right skill. |
| "This is too simple for a skill" | Simple questions reveal deep gaps. Use learn. |
| "Let me search first, then confirm" | Confirm framing BEFORE searching. Always. |
| "I'll skip the Carlini gate, it's obvious" | Bad ideas feel obvious. The gate exists because of this. |
| "They said yes so I should dump everything" | Yes = advance one step, not license to monologue. |