BC data-analysis-for-feishu
📊 Powerful ECharts-based data visualization skill optimized for Feishu (Lark) ecosystem. Supports 12+ chart types, 6+ data sources (Excel/CSV/Bitable/Sheet/Markdown), auto chart recommendation, auto analysis reports, generates high-definition PNG charts perfectly displayed in Feishu. No configuration required, works out of the box.
As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Dangerous commands
cmd-privilegescripts/generate_echarts_screenshot.py:22Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)args=['--no-sandbox', '--disable-setuid-sandbox', '--disable-dev-shm-usage', '--force-device-scale-factor=2', '--high-dpi-support=1']
detectorcode literal
Files scanned: 13. 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 59/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (git, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 41 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2694 tokens
- 100Progress reporting. Reports progress
- low 11 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
- -233 emoji in the instructions: noise for the model
- -42 reference files, but SKILL.md never points to them: the model will not open them
- -32 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 334: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.