AC amoeba-management-analysis
阿米巴经营分析技能。基于稻盛和夫阿米巴经营理念，提供单位时间核算、经营会计报表分析、阿米巴组织划分评估、业绩改善诊断等能力。 当用户需要做阿米巴经营分析、单位时间核算、经营会计、阿米巴组织划分、利润中心分析、内部交易定价、业绩评价时触发。 触发词：阿米巴、阿米巴经营、单位时间核算、经营会计、利润中心、内部交易、阿米巴划分、巴长、稻盛和夫、京瓷会计学
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 9. 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 51/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 27 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 518 tokens
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
- +1No license
- +2Single-language instructions
- +3Description length 175: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.
External checks
ClawHub: clean
The available evidence shows a coherent ClawHub/Convex agent-skill bundle with disclosed development and moderation workflows, and no supported indication of malicious behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026