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edit-timeline-studio

价格未确认★ 792 GitHub Stars目录更新于 · 2026年9月17日agent-skill

概览

Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

AI Video Editing with Timeline Studio

Turn the user's exact editorial request and media into reversible Timeline Studio edits. Keep the editable timeline as the source of truth; never replace it with an opaque one-shot render.

Choose the execution path

On first local use after installation, read references/host-environment.md. If Node.js is unavailable, start with the zero-dependency Shell or PowerShell bootstrap; otherwise run node scripts/setup-host.mjs --check. Agent-driven Chinese and mixed Chinese/English voiceover uses Timeline Studio's owned browser-local Hojo TTS Light 80M two-voice bundle and does not require a separate Python voiceover capability. If language runtimes or dependencies are missing, show the exact installation plan and obtain explicit user approval before install mode; never treat Skill installation as permission to modify the host or download models.
Treat local project-file processing as the default for deterministic editing: inspect media locally, modify the portable .timeline through the command layer or local archive services, and render and verify decoded output when the task changes rendered media. Do not open a browser merely because the editor has a UI.
Treat https://video-editor.ai-creator.top/ as the canonical hosted editor only when the user explicitly asks to use the website, provides no local repository or project path, or requires a hosted-only capability.
When this repository is available, prefer its Agent command layer and local media tools. Start the local server and browser only for a verified UI-only operation that the local project pipeline cannot express and the user has not required a local-only workflow. Read the actual server URL from process output; never assume port 5173.
Prefer the bundled Timeline Studio MCP tools when the host exposes them. Read references/mcp-integration.md, inspect the project, and always call timeline_project_diff before timeline_project_apply with the same revision and operations. The MCP server is a transport over the repository command runner, not a separate editing implementation.
When MCP is unavailable, inspect package.json for an Agent command script. Do not use npm run ... --if-present as capability detection because it can succeed silently. If the command runner exists, read references/command-contract.md, inspect the project, build a versioned plan, run the structural validator, and use project.diff as the authoritative semantic dry run before project.run.
If a required operation is missing from the local contract, state the exact gap. For repository-development work, implement the smallest shared local operation and renderer support before falling back to UI. Otherwise ask before switching to the browser workflow.
Do not claim deterministic or idempotent execution when only UI automation was available. State the limitation and preserve an editable project archive when the UI supports it.

Workflow

For a marker-only request, read references/timeline-markers.md and follow its inspect → plan → validate → diff → apply → inspect workflow through annotation handoff. Markers, chapter cues, ranges, and notes are project annotations; adding them requires no narration, model download, browser session, or video render. The media-production steps below apply only when the user also requests a media edit.

1. Inspect before editing

Preserve the user's prompt verbatim as the creative brief.
Resolve every referenced asset to an explicit path or URL. Never sweep a directory without approval.
Inspect duration, dimensions, audio presence, and media type.
Read the current project summary before changing an existing project.
Before planning long videos, chapters, musical beat cues, review notes, or edit ranges, read references/timeline-markers.md. Inspect existing annotations and use evidence-backed markers to preserve editorial anchors and decisions where useful. Preserve user annotations and distinguish Agent observations from supplied feedback.
Ask only when an unresolved choice materially changes the edit, such as the desired output duration or aspect ratio.
For an automatic-editing request, read references/auto-edit-workflow.md. Inspect first, classify the content, goal, and delivery with an explicit confidence level, then ask only the minimum category-specific questions that can change the cut. Never ask for facts discoverable from the media.
For a request to reproduce, imitate, recreate, or reverse-engineer a reference video, read references/replication-workflow.md. Classify it as editing-style replication, AI-generation replication, or a hybrid; reconstruct filters, repetitions, source splits, speed curves, transitions, shots, and timing before building; and explicitly resolve whether the authorized original audio track must be retained. Do not start editing until the replication analysis-completeness gate passes. Use current web search to compare AI video platforms only when generation is required, and use lawful web-sourced footage only when the user has not supplied adequate material.
Before loading or downloading a model for media analysis, read references/local-model-routing.md. Inventory the repository's existing local and pinned mirrored capabilities, choose the minimum model chain needed for the evidence gap, run inference locally without driving the visible editor UI, and record exact model/runtime/fallback provenance. Never load every available model by default or create a duplicate cache.
When the user needs web-sourced footage or asks where downloadable material can be found, read references/web-footage-sourcing.md. Give current, task-specific platform suggestions from live search and rank them by source legitimacy, explicit download support, usage rights, visual fit, quality, and provenance. Keep the skill provider-neutral; never hard-code one platform or brand as the permanent route.
For every completed automatic edit, read references/professional-editing-workflow.md. Analyze images directly; analyze video with representative frames, speech/OCR, semantics, and global plus subject-region optical flow. Stabilize before tracking or enhancement. Ask about image-to-video or image-to-image models only after inspection proves that generation is materially useful.
When a video plan could benefit from temporal emphasis, shot-matched color shaping, foreground/background separation, a product or person hero treatment, an editable subject outline, or identity replacement, read references/curves-and-subject-effects.md. Consider these capabilities during shot design instead of waiting for the user to name a control. Analyze in source time before retiming, use speed curves only for motivated timing changes, use Color Wheels to preserve skin and product identity while shaping the look, derive cutouts and outlines from verified masks, require explicit authorization for face swap, and validate preview/export parity and temporal stability before delivery.
When remote generation is materially required, read references/remote-video-generation.md. Search current official documentation, compare only providers that fit the shot blueprint, obtain approval before any paid or privacy-sensitive job, normalize the asynchronous task and provenance, download expiring output bytes, and add verified results to My assets without automatic timeline placement.
For product, brand, marketing-commerce, website-promotion, or other promotional edits, read references/promotion-narrative-workflow.md. Proactively construct an ambitious evidence-backed umbrella narrative rather than a feature list or kinetic-typography montage. Unless the user explicitly requests a teaser, build a complete problem-to-transformation-to-proof-to-CTA arc and actively find several visually distinct cases—normally at least three—each with its own setup, product action, visible result, and connection to the final payoff. Never invent customers, outcomes, metrics, or product behavior to make the story feel larger.
For highlight edits and reference replications where emphasis or dramatic impact matters, read references/highlight-tension-workflow.md. Treat saliency as candidate evidence, assign setup/rise/pre-impact/peak/aftershock roles, design a non-flat tension envelope, protect the decisive hero frame, and reject completion when accurate cutting still lacks a dominant payoff.
For a website walkthrough or promotional recording, use a supported browser-control skill to inspect and rehearse the authorized journey before capture. Read that browser skill completely before browser actions, then follow references/website-promo-workflow.md. Build and complete a page/flow coverage manifest before drawing product conclusions. When required pages are gated, ask the user to sign in themselves in the selected browser; never request credentials or describe inaccessible behavior as verified. Confirm any consequential external action separately, protect signed-in and personal data, and never claim a real screen recording was captured when only screenshots or static assets were available.
Treat tutorial-demo, vlog-event, marketing-commerce, and narrative-documentary as narrated-by-default categories. Preserve and reuse authorized source speech when it already carries the required story; otherwise author the minimum complete narration needed for context, progression, visible result, consequence, and closure, then synthesize it before timing picture. Do not ask whether narration should exist unless the user explicitly requests a source-only, music-only, or silent treatment; ask only for choices that materially affect language, casting, claims, or delivery.
For narrated edits, read references/voiceover-workflow.md. Prefer Timeline Studio's owned, pinned browser-local voices for every supported language. Route Chinese and mixed Chinese/English narration through the owned Hojo TTS Light 80M browser bundle and cast one of its two stable built-in reference profiles—晴岚 / zh_f_qinglan or 若溪 / zh_f_ruoxi—for each narrator or character; never install or use Hojo 40M or MeloTTS for this route. Use a remote service, operating-system voice, or unowned runtime only when the requested language or voice is unavailable locally and the user explicitly approves that fallback. Unless the user requests anoth

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文件元数据

name: edit-timeline-studio
description: Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.

查看原始文本

---
name: edit-timeline-studio
description: Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.
---

# AI Video Editing with Timeline Studio

Turn the user's exact editorial request and media into reversible Timeline Studio edits. Keep the editable timeline as the source of truth; never replace it with an opaque one-shot render.

## Choose the execution path

1. On first local use after installation, read [references/host-environment.md](references/host-environment.md). If Node.js is unavailable, start with the zero-dependency Shell or PowerShell bootstrap; otherwise run `node scripts/setup-host.mjs --check`. Agent-driven Chinese and mixed Chinese/English voiceover uses Timeline Studio's owned browser-local Hojo TTS Light 80M two-voice bundle and does not require a separate Python voiceover capability. If language runtimes or dependencies are missing, show the exact installation plan and obtain explicit user approval before install mode; never treat Skill installation as permission to modify the host or download models.
2. Treat local project-file processing as the default for deterministic editing: inspect media locally, modify the portable `.timeline` through the command layer or local archive services, and render and verify decoded output when the task changes rendered media. Do not open a browser merely because the editor has a UI.
3. Treat `https://video-editor.ai-creator.top/` as the canonical hosted editor only when the user explicitly asks to use the website, provides no local repository or project path, or requires a hosted-only capability.
4. When this repository is available, prefer its Agent command layer and local media tools. Start the local server and browser only for a verified UI-only operation that the local project pipeline cannot express and the user has not required a local-only workflow. Read the actual server URL from process output; never assume port 5173.
5. Prefer the bundled Timeline Studio MCP tools when the host exposes them. Read [references/mcp-integration.md](references/mcp-integration.md), inspect the project, and always call `timeline_project_diff` before `timeline_project_apply` with the same revision and operations. The MCP server is a transport over the repository command runner, not a separate editing implementation.
6. When MCP is unavailable, inspect `package.json` for an Agent command script. Do not use `npm run ... --if-present` as capability detection because it can succeed silently. If the command runner exists, read [references/command-contract.md](references/command-contract.md), inspect the project, build a versioned plan, run the structural validator, and use `project.diff` as the authoritative semantic dry run before `project.run`.
7. If a required operation is missing from the local contract, state the exact gap. For repository-development work, implement the smallest shared local operation and renderer support before falling back to UI. Otherwise ask before switching to the browser workflow.
8. Do not claim deterministic or idempotent execution when only UI automation was available. State the limitation and preserve an editable project archive when the UI supports it.

## Workflow

For a marker-only request, read [references/timeline-markers.md](references/timeline-markers.md) and follow its inspect → plan → validate → diff → apply → inspect workflow through annotation handoff. Markers, chapter cues, ranges, and notes are project annotations; adding them requires no narration, model download, browser session, or video render. The media-production steps below apply only when the user also requests a media edit.

### 1. Inspect before editing

- Preserve the user's prompt verbatim as the creative brief.
- Resolve every referenced asset to an explicit path or URL. Never sweep a directory without approval.
- Inspect duration, dimensions, audio presence, and media type.
- Read the current project summary before changing an existing project.
- Before planning long videos, chapters, musical beat cues, review notes, or edit ranges, read [references/timeline-markers.md](references/timeline-markers.md). Inspect existing annotations and use evidence-backed markers to preserve editorial anchors and decisions where useful. Preserve user annotations and distinguish Agent observations from supplied feedback.
- Ask only when an unresolved choice materially changes the edit, such as the desired output duration or aspect ratio.
- For an automatic-editing request, read [references/auto-edit-workflow.md](references/auto-edit-workflow.md). Inspect first, classify the content, goal, and delivery with an explicit confidence level, then ask only the minimum category-specific questions that can change the cut. Never ask for facts discoverable from the media.
- For a request to reproduce, imitate, recreate, or reverse-engineer a reference video, read [references/replication-workflow.md](references/replication-workflow.md). Classify it as `editing-style replication`, `AI-generation replication`, or a hybrid; reconstruct filters, repetitions, source splits, speed curves, transitions, shots, and timing before building; and explicitly resolve whether the authorized original audio track must be retained. Do not start editing until the replication analysis-completeness gate passes. Use current web search to compare AI video platforms only when generation is required, and use lawful web-sourced footage only when the user has not supplied adequate material.
- Before loading or downloading a model for media analysis, read [references/local-model-routing.md](references/local-model-routing.md). Inventory the repository's existing local and pinned mirrored capabilities, choose the minimum model chain needed for the evidence gap, run inference locally without driving the visible editor UI, and record exact model/runtime/fallback provenance. Never load every available model by default or create a duplicate cache.
- When the user needs web-sourced footage or asks where downloadable material can be found, read [references/web-footage-sourcing.md](references/web-footage-sourcing.md). Give current, task-specific platform suggestions from live search and rank them by source legitimacy, explicit download support, usage rights, visual fit, quality, and provenance. Keep the skill provider-neutral; never hard-code one platform or brand as the permanent route.
- For every completed automatic edit, read [references/professional-editing-workflow.md](references/professional-editing-workflow.md). Analyze images directly; analyze video with representative frames, speech/OCR, semantics, and global plus subject-region optical flow. Stabilize before tracking or enhancement. Ask about image-to-video or image-to-image models only after inspection proves that generation is materially useful.
- When a video plan could benefit from temporal emphasis, shot-matched color shaping, foreground/background separation, a product or person hero treatment, an editable subject outline, or identity replacement, read [references/curves-and-subject-effects.md](references/curves-and-subject-effects.md). Consider these capabilities during shot design instead of waiting for the user to name a control. Analyze in source time before retiming, use speed curves only for motivated timing changes, use Color Wheels to preserve skin and product identity while shaping the look, derive cutouts and outlines from verified masks, require explicit authorization for face swap, and validate preview/export parity and temporal stability before delivery.
- When remote generation is materially required, read [references/remote-video-generation.md](references/remote-video-generation.md). Search current official documentation, compare only providers that fit the shot blueprint, obtain approval before any paid or privacy-sensitive job, normalize the asynchronous task and provenance, download expiring output bytes, and add verified results to My assets without automatic timeline placement.
- For product, brand, `marketing-commerce`, website-promotion, or other promotional edits, read [references/promotion-narrative-workflow.md](references/promotion-narrative-workflow.md). Proactively construct an ambitious evidence-backed umbrella narrative rather than a feature list or kinetic-typography montage. Unless the user explicitly requests a teaser, build a complete problem-to-transformation-to-proof-to-CTA arc and actively find several visually distinct cases—normally at least three—each with its own setup, product action, visible result, and connection to the final payoff. Never invent customers, outcomes, metrics, or product behavior to make the story feel larger.
- For highlight edits and reference replications where emphasis or dramatic impact matters, read [references/highlight-tension-workflow.md](references/highlight-tension-workflow.md). Treat saliency as candidate evidence, assign setup/rise/pre-impact/peak/aftershock roles, design a non-flat tension envelope, protect the decisive hero frame, and reject completion when accurate cutting still lacks a dominant payoff.
- For a website walkthrough or promotional recording, use a supported browser-control skill to inspect and rehearse the authorized journey before capture. Read that browser skill completely before browser actions, then follow [references/website-promo-workflow.md](references/website-promo-workflow.md). Build and complete a page/flow coverage manifest before drawing product conclusions. When required pages are gated, ask the user to sign in themselves in the selected browser; never request credentials or describe inaccessible behavior as verified. Confirm any consequential external action separately, protect signed-in and personal data, and never claim a real screen recording was captured when only screenshots or static assets were available.
- Treat `tutorial-demo`, `vlog-event`, `marketing-commerce`, and `narrative-documentary` as narrated-by-default categories. Preserve and reuse authorized source speech when it already carries the required story; otherwise author the minimum complete narration needed for context, progression, visible result, consequence, and closure, then synthesize it before timing picture. Do not ask whether narration should exist unless the user explicitly requests a source-only, music-only, or silent treatment; ask only for choices that materially affect language, casting, claims, or delivery.
- For narrated edits, read [references/voiceover-workflow.md](references/voiceover-workflow.md). Prefer Timeline Studio's owned, pinned browser-local voices for every supported language. Route Chinese and mixed Chinese/English narration through the owned Hojo TTS Light 80M browser bundle and cast one of its two stable built-in reference profiles—晴岚 / `zh_f_qinglan` or 若溪 / `zh_f_ruoxi`—for each narrator or character; never install or use Hojo 40M or MeloTTS for this route. Use a remote service, operating-system voice, or unowned runtime only when the requested language or voice is unavailable locally and the user explicitly approves that fallback. Unless the user requests anoth

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许可证: MIT
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许可证: MIT

Dependency or permission surface needs review
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The skill description is extremely broad, which might lead to overreach if not carefully scoped by the agent, but the workflow mitigates this.
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Quality score needs review
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1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: MartinDelophy/ai-video-editor
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月8日
目录更新于: 2026年9月17日
技能指令路径: skills/edit-timeline-studio/SKILL.md @ b1c6b774865d

版本来自目录元数据，使用前请核实来源发布记录。

质量

73/100

强

信任

59/100

Do not auto-install

审计

75/100

高风险

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Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
The SKILL.md excerpt is truncated; full documentation may contain additional details but the provided content is sufficient for review.
The skill description is extremely broad, which might lead to overreach if not carefully scoped by the agent, but the workflow mitigates this.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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结果: —

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      "lastPushed": "1mo since push",
      "license": "MIT",
      "repository": "https://github.com/MartinDelophy/ai-video-editor/tree/main/skills/edit-timeline-studio",
      "install": "The tracked source changed or could not be synchronized. Review the current source before installing.",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "The SKILL.md excerpt is truncated; full documentation may contain additional details but the provided content is sufficient for review.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "The SKILL.md excerpt is truncated; full documentation may contain additional details but the provided content is sufficient for review.",
      "The skill description is extremely broad, which might lead to overreach if not carefully scoped by the agent, but the workflow mitigates this.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 73,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Risky"
  },
  "alternative_skills": [
    {
      "slug": "latent-spaces-brag-slim",
      "name": "brag-slim",
      "url": "https://www.openagentskill.com/skills/latent-spaces-brag-slim",
      "stars": 13807,
      "install_command": "npx skills add latent-spaces/brag --skill brag-slim",
      "trust_score": 81,
      "audit_score": 84
    },
    {
      "slug": "orchestra-research-nemo-curator",
      "name": "nemo-curator",
      "url": "https://www.openagentskill.com/skills/orchestra-research-nemo-curator",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill nemo-curator",
      "trust_score": 81,
      "audit_score": 85
    },
    {
      "slug": "krillinai-krillinai-render-horizontal",
      "name": "krillinai-render-horizontal",
      "url": "https://www.openagentskill.com/skills/krillinai-krillinai-render-horizontal",
      "stars": 12690,
      "install_command": "npx skills add krillinai/OpenCreator --skill krillinai-render-horizontal",
      "trust_score": 82,
      "audit_score": 85
    },
    {
      "slug": "openclaw-openai-whisper",
      "name": "openai-whisper",
      "url": "https://www.openagentskill.com/skills/openclaw-openai-whisper",
      "stars": 391309,
      "install_command": "npx skills add openclaw/openclaw --skill openai-whisper",
      "trust_score": 81,
      "audit_score": 86
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The SKILL.md excerpt is truncated; full documentation may contain additional details but the provided content is sufficient for review.",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
  ],
  "agent_contract": {
    "task_input": "Use edit-timeline-studio in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 67/100 Manual review",
      "Audit: 75/100 Risky",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "martindelophy-edit-timeline-studio (edit-timeline-studio)",
      "install_command": "",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "martindelophy-edit-timeline-studio",
      "task": "Use edit-timeline-studio in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/martindelophy-edit-timeline-studio",
    "api": "https://www.openagentskill.com/api/agent/skills/martindelophy-edit-timeline-studio",
    "audit": "https://www.openagentskill.com/skills/martindelophy-edit-timeline-studio/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=martindelophy-edit-timeline-studio&task=Use%20edit-timeline-studio%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20edit-timeline-studio%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20edit-timeline-studio%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/martindelophy-edit-timeline-studio/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/martindelophy-edit-timeline-studio"
  }
}

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创作者: MartinDelophy
来源: MartinDelophy/ai-video-editor
收录方: OpenAgentSkill 社区索引

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