BD AIRS-具身智能订单信息采集器
AIRS 产业研究订单采集技能集，专注公开招投标订单采集与产业信息追踪。面向具身智能、机器人、新能源汽车、低空经济、半导体装备等多行业产业研究，将企业主体确认、天眼查招投标/中标公告订单采集、第三方订单核查、大模型案例提取、标准入库表生成和案例质量复查组织为一套可复用研究流程。适用于采集公开证据、验证企业订单、沉淀产业案例库和生成产业研究知识资产。 Keywords: AIRS, 具身智能, 订单采集, 机器人, 新能源汽车, 低空经济, 半导体装备, 天眼查, 招投标, 中标公告, 订单核查, 案例提取, 研究技能, 知识资产.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches
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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:94High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…bKX+KS9G…yMA/NhKJ…RGz/Q==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:176High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…uyQ+a1ko…Tn4+E0Ti6C2AA==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:182High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…Wwy+ghLE…vfU/YnxW…fdg==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:210High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…DYr+NSUK…Itc/irNR…37y+AMujNyNtG+1Rggw==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:283High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…2BV+FY5ZFezP/ypmwayk68+NzzA…NFD/uUmBJuGoXw==",
detector
Files scanned: 26. 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 49/100
- 0Result and completion. Does not say what the result is
- 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
- 40Consistency. Frontmatter name (AIRS-具身智能订单信息采集器) differs from the folder (embodied-bidding-tracker)
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 31 steps
- 100Execution cost. Instruction body is 599 tokens
- 100Running it twice. No mutating operations
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
- +2Single-language instructions
- +3Description length 268: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (2 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.