AC feishu_lark_sheets_edit
Read, write and manage Lark/Feishu Sheets (spreadsheets) and download Lark/Feishu cloud files via Lark OpenAPI. Reads Feishu app credentials (appId/appSecret) from ~/.openclaw/openclaw.json to authenticate with the Lark OpenAPI. Use when a user provides a Lark/Feishu sheet link (URL path like /sheets/TOKEN) and you need to fetch cell values, write/update cells, add/clone sheet tabs, convert to CSV/JSON, or feed the data into summaries/reports/analysis. Also use when a user provides a Lark/Feishu file link (URL path like /file/TOKEN) and needs to download the file (PDF, etc.) locally. Triggers: 'feishu sheet', 'lark sheet', 'spreadsheet', 'write to sheet', 'update sheet', 'export sheet', 'feishu file', 'lark file', 'download file', 'feishu download', 'lark download', 'cloud file'.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens)
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (feishu_lark_sheets_edit) differs from the folder (feishu-lark-sheets-edit)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 30 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 2033 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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 790: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (5 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.