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AD autonomous-science-loop

自主科学发现闭环——把「观测→假设→实验设计→反驳→定律归纳」做成一次可机器执行、可证伪的原创科学发现循环。给一组观测与可实验的候选点，引擎自动用符号回归拟合候选定律、按残差反驳被证伪的假设、主动设计"分歧最大"的下一次实验以最快收敛、并按 Occam 归纳出最简可解释定律。适用于规律发现、参数辨识、模型选择、主动学习/最优实验设计等场景。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 569 tokens Open the sourceclawhub.ai↗ analyzed 36 h ago

自主科学发现闭环——把「观测→假设→实验设计→反驳→定律归纳」做成一次可机器执行、可证伪的原创科学发现循环。给一组观测与可实验的候选点，引擎自动用符号回归拟合候选定律、按残差反驳被证伪的假设、主动设计"分歧最大"的下一次实验以最快收敛、并按 Occam…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 569 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 171: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (3 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The science-discovery tool itself is limited, but the bundled self-learning system persists user/workflow data and instructs future modification of the skill file without clear consent or retention limits.
LLM: suspicious (high) · 14 Aug 2026