AA biz-retro-analyzer
Turn conversations, field observations, and follow-up reasoning from complex collaborative projects into evidence-backed analysis of participant understanding, coordination structure, decision traces, reversals, and next-step actions. Use when the user asks to 复盘会后对话, 分析录音, 拆会议纪要, 提炼项目脉络, 做关键判断纠偏, or turn messy conversations into structured facts, judgments, and follow-up actions.
Turn conversations, field observations, and follow-up reasoning from complex collaborative projects into evidence-backed analysis of participant…
As a process A 82/100 · Runs to the end — weak spots: progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5044 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 82/100
- 0Progress reporting. Says nothing while it works
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Failures and branches. 8 branches
- 70Execution cost. Instruction body is 5044 tokens
- 85Steps. 197 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 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)
- +1No license
- +2Single-language instructions
- +3Description length 383: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 197 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.