跳到正文
cccskills
免费在 GitHub 公开

watch

Observe actual video and sound over the whole file or named ranges, with timestamped evidence and honest coverage. Use complementary playback, listening and targeted frames; return observations without edits or verdicts.

中文简介正在准备中。当前显示原文说明。

实测评分 未实测

查看安装方法

包含的文件(8)

  • SKILL.md3.7 KB
  • references/parameters.md2.3 KB
  • skill.meta.json296 B
  • tests/expected-output.md724 B
  • tests/human-test.md1.1 KB
  • tests/sample-input.md692 B
  • tests/smoke-test.md652 B
  • tests/verifier.md588 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Human version

Report what the actual file shows and sounds like, with timestamps. Keep sampled frames, moving-picture review, listening and transcription separate. Missing a capability stays explicit; observations are not an approval verdict.


Agent version

Inputs and modes

Accept the video, optional timestamp ranges, sampling rate, frame budget, resolution, audio dimensions and focus. Defaults describe whole-file scope, not completed review. Read the parameter reference for how to partition work without omitting intervals.

Before reporting, resolve exact source/version, checksum, duration and actual reviewer capabilities. Returned audio bytes or transcription do not establish perception. Use complete normal-speed moving-picture and sound review for a full audiovisual observation. Add sampled frames for scene/text evidence and suitably dense, original-resolution motion/mouth inspection for suspicious intervals. Sparse frames cannot certify action continuity or fine lip-sync. There is no universal two-fps ceiling for targeted inspection; remain within source rate and available tools.

Workflow

  1. Read the exact file and its real duration. Resolve all requested ranges and keep actual decoded source timestamps. Never substitute another master or thumbnails.
  2. Observe the complete requested moving-picture ranges at normal speed when capable. Record uncovered ranges/capabilities. A frame budget cannot silently omit a scene; divide work into explicitly covered passes when needed.
  3. Inspect sampled or targeted frames at sufficient resolution for the question. Record sampling density, actual frame times and coverage separately from playback.
  4. Hear requested sound, including speech, music and effects. Listen to isolated speech when available and the mix. Transcribe speech as supporting word evidence; unavailable ASR leaves transcription incomplete but does not erase actual listening. Unavailable listening cannot be cleared by ASR, waveform or inclusion flags.
  5. Report observed picture/action/text and heard speech/sound with exact intervals, source evidence and observation method. Label transcript-only words as ASR output, not heard speech. Corroborate suspicious ASR against audio; silence can yield false words. Do not invent music taste, voice character or lip-sync observations.
  6. Save timestamped observations and the coverage manifest. Keep each duty complete, partial or unavailable, with covered ranges and remaining gaps. No edits, quality verdicts or recommendations are supplied by this primitive.

Outputs

Return readable observation report, sampled frames and their source times, optional transcript/word timing, available audio artifacts and manifest. The manifest records source identity/checksum/duration, requested and observed ranges, reviewer capability, separate frame/motion/listening/transcription coverage, sampling/resolution, file paths and remaining gaps. Do not rename frames plus transcript as frames-and-audio.

Boundaries

A full-file request with partial coverage remains partial. Audio disabled intentionally is frames/motion-only; audio enabled without perception is unverified. A missing backend does not justify an invented transcript. The downstream judge compares these observations with the creative contract and makes its own evidence-bound verdict.

References

Read parameters for observation controls and coverage.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

查看 gooseworks-ai 的全部技能

报告此技能的问题