AD scientific-figure-analysis
科学论文图表完整分析流水线 — PDF图表提取(PyMuPDF) + Kimi K2.6多模态视觉分析 + 科学理解 + 逆向工程。 触发场景: (1) 分析论文PDF中的图表，(2) 逆向工程图表生成流水线，(3) 从文献中提取结构化知识，(4) 论文自动化文献挖掘。 Do NOT call: 纯文本阅读、非科学类PDF、无图表的纯理论论文。
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 41/100
- 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
- 40Consistency. Frontmatter name (scientific-figure-analysis) differs from the folder (9cf703b)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 35 steps
- 100Execution cost. Instruction body is 1344 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 173: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.
External checks
ClawHub: clean
This skill is a coherent scientific figure analysis workflow, but users should know it can upload extracted paper figures to Moonshot/Kimi for vision analysis.
LLM: benign (high) · VirusTotal: · 28 May 2026