BD google-scholar-api
通过 SerpAPI 实现 Google Scholar 学术论文检索和下载。使用场景包括：1) 通过关键词搜索学术论文，2) 获取论文详细信息（标题、作者、摘要、年份、引用次数），3) 下载可用的 PDF 文件，4) 批量检索相关文献，5) 按年份、引用数等条件筛选论文。需要 SerpAPI 密钥（可从 serpapi.com 获取免费额度）。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.
The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
medium Secrets in code
secret-labelled-tokenREADME.md:23Labelled token / key literal (vendor format unknown — verify it is not a live credential)# api_key = "0430…53a" # ❌ 不要这样做！
-
low Secrets in code
secret-password-literalREADME.md:23Hard-coded password / key literal (may be an example)# api_key = "0430…53a" # ❌ 不要这样做！
Files scanned: 10. 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 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 (python) that frontmatter does not declare
- 100Steps. 95 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1460 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
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 174: enough signal without eating the budget
- +4Structure: 43 headings
- +3Step-by-step instructions: 95 items
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.