AD gp-complaint-winrate
政采投诉"胜诉率"预判模型（供应商/投诉人攻向）——供应商输入自身遭遇的不公情形，系统通过"投诉事项结构化拆解+5万+真实投诉处理决定类案相似度比对"，以真实检索样本计数给出投诉成功率（成立率）预测，并按"证据充分性"做条件分层（强证据/弱证据分层胜诉率），输出证据收集与补强路线图。触发词："投诉胜诉率""投诉成功概率""这个投诉能成吗""类案成立率""投诉被驳回概率""证据够不够投诉""投诉策略预判""判例法胜诉预判"。不适用：采购人/代理机构被质疑后的防御答复（应路由至政采质疑类案智库/政采盾牌）；工程招投标投诉（法域不同）；评标打分、合同审查、招标文件编制。
政采投诉"胜诉率"预判模型（供应商/投诉人攻向）——供应商输入自身遭遇的不公情形，系统通过"投诉事项结构化拆解+5万+真实投诉处理决定类案相似度比对"，以真实检索样本计数给出投诉成功率（成立率）预测，并按"证据充分性"做条件分层（强证据/弱证据分层胜诉率），输出证据收集与补强路线图。触发词："投诉胜诉率""投诉成功概…
As a process D 46/100 · Unfinished process — 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.
- 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: 6. 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") - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 68 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2047 tokens
- 100Running it twice. No mutating operations
- 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
- +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
- +5Description quotes 9 example trigger phrases
- +3Description length 285: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 68 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.