BC china-im-workflow-cli
Orchestrate cross-platform IM workflows using Feishu CLI, DingTalk CLI, and WeCom CLI. Teach AI agents how to combine lark-cli (200+ commands), dws (DingTalk Workspace CLI), and wecom-cli to automate reporting, task management, notifications, and marketing across China's three major enterprise IM platforms. Covers: auto-send weekly reports via Feishu, sync tasks between DingTalk and WeCom, cross-platform notification broadcasting, marketing content distribution to all three platforms simultaneously. Triggers on: 飞书CLI工作流, 钉钉CLI自动化, 企微CLI集成, 跨平台IM, IM workflow automation, Feishu DingTalk WeCom CLI orchestration, 中国企业IM自动化, agent CLI workflow, multi-platform notification, 跨平台周报, 多平台消息分发
As a process C 63/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting
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 · 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")
Process rating: all ten parameters 63/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 7 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2171 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 693: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.