N
nikserbur/research-lab
A research lab for Claude Code: hypotheses in a simulation with written requirements, an independent reviewer…
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
- ★ 数取值
- 0 ★ · nikserbur/research-lab · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
- 来源明细
- 从该仓库的 GitHub 页面直接读取 · 条目类型:开源项目
- 收录与活跃
- 收录于 2026-11-08 · 最近见到 2026-11-14 · 共见到 7 次 · 被 1 个来源列出
- 评分构成
- 综合分 103 · 1 个来源 · repo
常见问题(答案都来自我们的记录)
它适合谁 / 用来做什么?
我们把它归在这些标签下: 记忆增强、Agent 技能。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库
nikserbur/research-lab;我们收录于 2026-11-08;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。# research-lab
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
A research lab inside Claude Code: hypotheses, simulations, an independent reviewer, and a knowledge base that remembers every result.
Claude runs research the way a careful lab does: it writes down what it expects before every run, tests in a simulation that meets stated requirements, judges the winner once on data it never saw, has a reviewer compare what happened with what was expected, and turns every surprise into the next hypothesis. Everything it learns lands in a local knowledge base that answers later questions in any project.
## Install
text
/plugin marketplace add nikserbur/research-lab
/plugin install research-lab@research-lab
Restart Claude Code. The first session creates the data directory ~/.claude/research-lab/ (a git repo). Prefer plain files in ~/.claude? Use the install script. Coming from autolearn? See Upgrading from autolearn.
text
/research "train a model that predicts next-minute direction better than the baseline, net of costs"
/research research/nifty-mm rounds 3 # continue an existing research folder
/knowledge-ingest repo # give this repo a page in the knowledge base
## Two modes, one knowledge base
| | Background mode | Direct mode: /research |
| What it is for | answering from what is already known | producing new knowledge |
| Who works | the main session, no subagents | the session as controller (plans, keeps state, talks to you), rl-source-scout (collects), rl-executor (runs), rl-hypothesis-reviewer (judges) |
| Parts | knowledge-search, knowledge-ingest, three hooks | the research loop, its environments, lab.py |
| Writes to | the global knowledge base | research/ /KB/ in the project; durable facts are promoted to the global base |
| Cost | close to zero, never interrupts | real compute and tokens; starts when you ask for research |
Both read one index. A research KB answers its own research (and any search with --everywhere); what lasts is promoted into the global base, and from then on it answers today's question in any repo. A conclusion is not quietly overwritten: a section says what it is (kind: measurement, conjecture, expectation) and whether it still holds (status: invalid, superseded), search shows the label, leaves retracted sections out, and never grades a prediction or a guess CORRECT.
mermaid
flowchart LR
U[Your message] --> M{Claude matches it against skill descriptions}
M -- "research, train a model, test hypotheses, what is known about X" --> RS[/research: direct mode/]
M -- "how does X work here, did we try Y, what did we decide" --> KS[knowledge-search]
M -- "remember this, add to the knowledge base" --> KI[knowledge-ingest]
RS -- "conclusions, sources, reviews" --> KB[(knowledge base + research KBs)]
KI --> KB
KS --> KB
subgraph Hooks["Hooks: always, no decision involved"]
H1[SessionStart: brief with pointers]
H2[PostToolUse Write/Edit: embed KB writes]
H3[PreToolUse Bash: secret guard]
H4[PreToolUse: holdout guard]
end
/research starts by itself when a request matches its description, and always with /research or /research-lab:research.
## A research round
mermaid
flowchart TD
G[Goal + your hard rules] --> PR[PROGRAM.md]
PR --> SC[rl-source-scout: KB, code, papers, docs]
Claude runs research the way a careful lab does: it writes down what it expects before every run, tests in a simulation that meets stated requirements, judges the winner once on data it never saw, has a reviewer compare what happened with what was expected, and turns every surprise into the next hypothesis. Everything it learns lands in a local knowledge base that answers later questions in any project.
## Install
text
/plugin marketplace add nikserbur/research-lab
/plugin install research-lab@research-lab
Restart Claude Code. The first session creates the data directory ~/.claude/research-lab/ (a git repo). Prefer plain files in ~/.claude? Use the install script. Coming from autolearn? See Upgrading from autolearn.
text
/research "train a model that predicts next-minute direction better than the baseline, net of costs"
/research research/nifty-mm rounds 3 # continue an existing research folder
/knowledge-ingest repo # give this repo a page in the knowledge base
## Two modes, one knowledge base
| | Background mode | Direct mode: /research |
| What it is for | answering from what is already known | producing new knowledge |
| Who works | the main session, no subagents | the session as controller (plans, keeps state, talks to you), rl-source-scout (collects), rl-executor (runs), rl-hypothesis-reviewer (judges) |
| Parts | knowledge-search, knowledge-ingest, three hooks | the research loop, its environments, lab.py |
| Writes to | the global knowledge base | research/ /KB/ in the project; durable facts are promoted to the global base |
| Cost | close to zero, never interrupts | real compute and tokens; starts when you ask for research |
Both read one index. A research KB answers its own research (and any search with --everywhere); what lasts is promoted into the global base, and from then on it answers today's question in any repo. A conclusion is not quietly overwritten: a section says what it is (kind: measurement, conjecture, expectation) and whether it still holds (status: invalid, superseded), search shows the label, leaves retracted sections out, and never grades a prediction or a guess CORRECT.
mermaid
flowchart LR
U[Your message] --> M{Claude matches it against skill descriptions}
M -- "research, train a model, test hypotheses, what is known about X" --> RS[/research: direct mode/]
M -- "how does X work here, did we try Y, what did we decide" --> KS[knowledge-search]
M -- "remember this, add to the knowledge base" --> KI[knowledge-ingest]
RS -- "conclusions, sources, reviews" --> KB[(knowledge base + research KBs)]
KI --> KB
KS --> KB
subgraph Hooks["Hooks: always, no decision involved"]
H1[SessionStart: brief with pointers]
H2[PostToolUse Write/Edit: embed KB writes]
H3[PreToolUse Bash: secret guard]
H4[PreToolUse: holdout guard]
end
/research starts by itself when a request matches its description, and always with /research or /research-lab:research.
## A research round
mermaid
flowchart TD
G[Goal + your hard rules] --> PR[PROGRAM.md]
PR --> SC[rl-source-scout: KB, code, papers, docs]
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
[](https://anclum.com/zh/s/nikserbur-research-lab/)
- 来源
- github · https://github.com/nikserbur/research-lab
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