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BF auto-research

OpenClaw 原生的自动化研究 pipeline。从一个研究 topic 出发，经过 23 个 stage 产出完整论文。 每个 Phase 由独立 sub-agent 执行（context 隔离），Phase 间通过文件系统传递产出。 触发词：Research X、跑研究、文献调研、写论文、研究 pipeline、auto research。 灵感来源：AutoResearchClaw，但完全用 OpenClaw 原生能力实现，不依赖外部 Python 包。

ClawHub Agent Skills author: lixiang1076 v1.0.0 MIT-0 13 files body ≈ 1 714 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 36/100 · Will not run — References files that are not bundled: references/phase-*.md

ProcedureInfrastructuretype 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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
73
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: references/phase-*.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-password-literal scripts/literature_search.py:441
    Hard-coded password / key literal (may be an example)
    query, limit=limit_per_query, year_min=year_min, api_key=s2_api_key,

Files scanned: 13. 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")
  • warning missing-ref reference to a missing file: references/phase-*.md

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: references/phase-*.md
  • 0Tools and files. 1 referenced file(s) missing: references/phase-*.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (auto-research) differs from the folder (openclaw-auto-research)
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 28 steps
  • 100Execution cost. Instruction body is 1714 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 234: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
This skill is a disclosed research automation pipeline, but it asks the agent to run generated code, send research summaries externally, and persist lessons with controls that are not clearly scoped.
LLM: suspicious (high) · VirusTotal: · 29 May 2026