BF scisurvey
SciSurvey — Systematic Survey × Sciverse。全自动系统性学术综述生成器：自动识别领域 → 选择 Survey/Review 类型 → 领域自适应关键词 → meta-catalog 确认 → 多策略并行检索（语义 + 结构化 meta-search + 引用链追踪）→ 质量过滤 → 国内/国际分层 → 深度全文提取 → 引用证据映射 → 主题化综合 → 综述写作 → 多格式输出。支持 --type systematic-survey/systematic-review/scoping-review；输出格式 Markdown/LaTeX/DOCX/PDF；参考文献 APA/Chicago/MLA/IEEE/Vancouver/GB/T 7714；含阻塞性引用完整性门控。当用户说'做一个关于 X 的综述'、'帮我综述 X 领域'、'调研 X 的研究进展'、'生成 X 的文献综述'时使用。
SciSurvey — Systematic Survey × Sciverse。全自动系统性学术综述生成器：自动识别领域 → 选择 Survey/Review 类型 → 领域自适应关键词 → meta-catalog 确认 → 多策略并行检索（语义 + 结构化 meta-search + 引用链追踪）→ 质量过滤…
As a process F 32/100 · Will not run — References files that are not bundled: file_name
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- The text references files that are not there: add them or drop the references.
- 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: 2. 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") - warning
body-longSKILL.md body ≈ 9818 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: file_name
Process rating: all ten parameters 32/100
- 0Tools and files. 1 referenced file(s) missing: file_name
- 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
- 40Execution cost. Instruction body is 9818 tokens: crowds the task out of the window
- 100Steps. 89 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low The response is described with custom markup (6 tags): a typed call is more reliable
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 417: enough signal without eating the budget
- +4Structure: 61 headings
- +3Step-by-step instructions: 89 items
- +4Has examples (53 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.