AD feishu-literature-manager
Automated literature retrieval and Feishu Bitable management. Use when user requests to create a literature database, search PubMed for specific topics, or manage research papers in Feishu tables. Triggers on phrases like "create a literature table", "search papers and add to Feishu", "build a research database", "补充文献", "添加文献到表格", or "检索文献并建立表格". Supports complete workflow from topic definition to populated Feishu table with all required fields including Chinese translations, impact factors, and reference formatting. Also supports supplementing existing databases with new papers.
Automated literature retrieval and Feishu Bitable management.
As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 48/100
- 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
- 30Running it twice. 10 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4054 tokens
- 100Steps. 80 steps
- 100Consistency. Name and required fields are in place
- high The skill tells the model to perform an irreversible action with no human approval
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 587: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 80 items
- +4Has examples (22 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.