SKILLEMALL.ai
GitHub ↗ RU Sign in

CC 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.

ClawHub Agent Skills author: zane iris zhou v1.0.0 MIT-0 13 files · 6 scripts body ≈ 2 694 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

📊 Powerful ECharts-based data visualization skill optimized for Feishu (Lark) ecosystem.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerExcelGitHubData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
63
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-privilege scripts/generate_echarts_screenshot.py:22
    Privilege 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-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (git, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 41 steps, 2 vague phrases
  • 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.

In the sandbox The scripts kept to themselves

The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

Запущено 6 скриптов; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

Из них 5 не дошли до работы, и об их поведении мы ничего не узнали.

test.pyзавершился с кодом 1
scripts/auto_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pandas'
scripts/data_parser.pyне запустился: ModuleNotFoundError: No module named 'pandas'
scripts/generate_echarts_card.pyне запустился: generate_echarts_card.py: error: the following arguments are required: --type, --title
scripts/generate_echarts_screenshot.pyне запустился: ModuleNotFoundError: No module named 'pyppeteer'
scripts/main.pyне запустился: ModuleNotFoundError: No module named 'pyppeteer'

23 Oct 2026

External checks

ClawHub: suspicious
The skill is mostly a chart generator, but it quietly loads chart code from an external CDN inside a browser launched without sandboxing while describing the renderer as local.
LLM: suspicious (high) · 16 Sept 2026