一键导入
explore
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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.
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.
Use when user asks what something means, says "explain", "I don't understand", "teach me", "what is X", or asks about a term mid-session.
| name | explore |
| description | 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. |
Before anything else, create the session directory and open the lab notebook:
mkdir -p .research-loop/sessions/<slug>/
Where <slug> = topic lowercased, spaces to dashes, e.g. policy-compression-ai-agents.
Create .research-loop/sessions/<slug>/lab_notebook.md with:
# Lab Notebook — <topic>
Started: <date>
Status: exploring
---
## Session log
This file is the single source of truth for the entire research session. Every phase appends to it.
Say:
"I'll explore [topic]. Is this the right framing, or do you want to adjust it first?"
Wait for confirmation. Then append to lab_notebook.md:
## Framing
Topic: <confirmed topic>
Date: <date>
Researcher notes: <anything they said about prior knowledge>
Dispatch ALL four researcher subagents simultaneously. Each task references the researcher agent definition (.claude/agents/researcher.md) and must follow its integrity commandments (no fabricated sources, URL-or-it-didn't-happen, read-before-summarize), use the numbered evidence table format (ID, Source, URL, Key claim, Type, Confidence), and apply the source quality tiers (A = peer-reviewed papers/official docs, B = reputable secondary, C = accept with caveats).
Agent 1 (researcher):
Task: Find the top 10 most important papers on [topic].
Use web search. For each paper return: title, year, authors, 1-sentence contribution.
Prioritize foundational and recent (2020–2025) papers. Return a clean numbered list.
Use the numbered evidence table format from the researcher agent definition.
Save findings to .research-loop/sessions/<slug>/findings-papers.md.
Agent 2 (researcher):
Task: Find active GitHub repos and benchmarks for [topic].
Use web search. For each repo return: name, URL, stars (if available), 1-sentence description.
Return 5–10 actively maintained repos. Use cited evidence table format.
Save findings to .research-loop/sessions/<slug>/findings-repos.md.
Agent 3 (researcher):
Task: Find 3 places where experts in [topic] fundamentally disagree.
Use web search. For each debate: name it, state Side A's strongest argument, state Side B's strongest argument, explain why it matters.
Use evidence table format with numbered sources.
Save findings to .research-loop/sessions/<slug>/findings-debates.md.
Agent 4 (researcher):
Task: Find explicit open problems stated in the [topic] literature.
Use web search. Look for "future work" sections, unsolved challenges, gaps mentioned by field leaders.
For each gap: state the problem, cite where it appears, explain why it's hard.
Return 5–8 specific open problems. Use numbered evidence table format.
Save findings to .research-loop/sessions/<slug>/findings-gaps.md.
Tell the user: Searching papers, repos, debates, and open problems in parallel using researcher agents...
IMPORTANT: Each dispatched subagent must follow the researcher agent's 6 integrity commandments and output format defined in .claude/agents/researcher.md. Key rules: every entry needs a URL, no fabricated sources, mark status honestly, triage before fetching full content, write findings to file progressively.
When all 4 return, append the full results to lab_notebook.md:
## Literature (Phase 1)
<full list of papers with title, year, contribution>
## Active Repos
<list of repos>
## Field Debates
<3 debates with both sides and strongest arguments>
## Stated Open Problems
<list of gaps found in literature>
Then show the user a synthesis (not the raw dump):
"Here's the landscape in 3 sentences: [synthesis]
The 3 most interesting angles I see: A) [angle] — [why interesting] B) [angle] — [why interesting] C) [angle] — [why interesting]
Which direction do you want to go deeper on?"
Wait for response. Append their choice to lab_notebook.md:
## Researcher direction choice
Chose: <their answer>
Reasoning they gave: <anything they said>
Now spawn 1 focused search:
Agent: What are the 5 core mental models every expert in [chosen direction] carries?
Not facts — the intuitions and ways of thinking that take years to develop.
Show the models conversationally, one at a time:
"Here's the first mental model experts in this space share: [model 1 + explanation]. Does this match your intuition?"
Append all 5 to lab_notebook.md:
## Mental Models
1. [name]: [description]
2. ...
Say:
"Let me give you 3 questions that would expose whether someone truly understands this vs. memorized it. Try answering them — every wrong answer tells us something."
Show questions one at a time. Wait for answers. For each wrong answer:
"Here's what you're missing: [explanation]"
Append to lab_notebook.md:
## Diagnostic Q&A
Q1: [question]
Researcher answer: [their answer]
Expert answer: [correct answer]
Gap identified: [what they didn't know]
...
Say:
"Exploration complete. Lab notebook saved to
.research-loop/sessions/<slug>/lab_notebook.mdReady to find the gaps and run the Carlini gate? →
/gapor just tell me which open problem interests you most."
This skill dispatches the researcher subagent (.claude/agents/researcher.md) for parallel evidence gathering. The researcher agent definition encodes: