BD deepsop-tiktokflow
TikTok 视频 AI 生成与发布技能（数字员工 Toby）。用户输入自然语言指令，AI 自动拆解任务参数，调用 deepsop 平台接口生成 AI 视频并发布到 TikTok，等待后查询并推送播放/点赞/评论/分享等数据。触发场景：用户说「发 TikTok 视频」「生成视频发布到 TikTok」「抖音国际版发视频」等与 TikTok 视频生成发布相关的指令；或收到包含 [DeepSOP-AutoQuery-Toby] 标记的系统定时事件（cron 回调）。需要提前配置环境变量 DEEPSOP_API_KEY。⚠️ 调用本 SKILL 前必须先完整阅读 SKILL.md。提交 agentSubmitTask **必须**走 scripts/submit_task.py（通过 heredoc 把 body 喂给 stdin），脚本内部串行跑 validate_employee_params.py + UTF-8 安全 HTTP 提交，**禁止**直接写 curl 命令（会因 Windows cp936 代码页导致 taskName/taskDescription 中文乱码）。脚本退出码 0 才算成功；非 0 必须把 summary/errors 原样回给用户后修正重试，禁止绕过校验或假装成功。
TikTok 视频 AI 生成与发布技能（数字员工 Toby）。用户输入自然语言指令，AI 自动拆解任务参数，调用 deepsop 平台接口生成 AI 视频并发布到 TikTok，等待后查询并推送播放/点赞/评论/分享等数据。触发场景：用户说「发 TikTok 视频」「生成视频发布到…
As a process D 42/100 · 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.
- 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: 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") - warning
body-longSKILL.md body ≈ 6152 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 42/100
- 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
- 30Running it twice. 10 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 6152 tokens
- 100Steps. 97 steps
- 100Consistency. Name and required fields are in place
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
- -230 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 555: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 97 items
- +4Has examples (16 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.