BD topic-factory
跨平台自媒体选题日报系统 — 每天自动从 6 大数据源（头条/微博/B站/抖音/知乎/36氪） 抓取热点新闻，按 6 大类 119 词关键词库筛选，LLM 生成抖音钩子和公众号标题， 输出 Markdown 选题日报，推送到飞书 webhook。 适用场景： - 自媒体运营：每天 10 个候选选题，⭐ 优先 3 个 - 双平台发布：抖音 60s 口播 + 公众号 2000-3000 字图文 - "只看数据说话"人设：严格禁词清单 + 数据点必填 - 跨平台热点捕获：6 大数据源 + 36h 时间窗口过滤 本 skill 含完整工作流：4 个脚本 + 4 个 cron 配置 + 飞书 webhook 推送 + 限流 fallback。
跨平台自媒体选题日报系统 — 每天自动从 6 大数据源（头条/微博/B站/抖音/知乎/36氪） 抓取热点新闻，按 6 大类 119 词关键词库筛选，LLM 生成抖音钩子和公众号标题， 输出 Markdown 选题日报，推送到飞书 webhook。 适用场景： - 自媒体运营：每天 10 个候选选题，⭐ 优先 3 个…
As a process D 48/100 · 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.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
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 · 2
✓ No critical or high findings
Medium and low: 2
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medium Exfiltration
net-redirectable-api-keyscripts/generate_topics.js:691Helper 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
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medium Dangerous commands
cmd-shell-rcSKILL.md:93Writes to a shell startup fileecho 'export ANTHROPIC_AUTH_TOKEN="sk-ant-..."' >> ~/.zshrc
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 48/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2066 tokens
- low 13 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 322: enough signal without eating the budget
- +4Structure: 38 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (14 code blocks)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.