BD byted-market-insight-agent
火山引擎市场洞察助手。帮助用户获取品牌在各大社交平台和媒体渠道上的公开内容数据，通过 AI 筛选出真正值得关注的信息，并发现潜在商机线索。当用户提到以下任何场景时使用此技能：想知道最近网上有没有人在讨论自己的品牌或产品、想看看竞品最近在社交媒体上有什么动态、想了解某个话题或事件在网上的讨论热度和趋势、想定期获取和自己业务相关的行业资讯和热点内容、想把筛选后的内容数据接入自己的系统做进一步分析、想查看自己账号下有哪些监测任务或拉取某个任务的数据、想了解最新的中东局势关税贸易战AI大模型等热点话题的网络讨论、想查询商机线索信息获取 ACOR 评估竞争分析公司画像、提到火山引擎市场洞察 Volcengine Insight PullPost ListCustomSubsTask QueryClueInfo。即使用户没有直接提到"市场洞察"，只要涉及品牌声量追踪、行业动态了解、热点事件网络讨论、公开内容数据获取、商机查询与分析等需求，都应触发此技能。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
net-redirectable-api-keyscripts/auth_resolver.py:236Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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 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 (bash, python) that frontmatter does not declare
- 100Steps. 28 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 793 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
- +1No license
- +2Single-language instructions
- +3Description length 427: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 28 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.