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Video Creation Skill Router

Want to create a video but don't know which tool to use? Want AI to help you "find skill → install skill → make video → full pipeline"? The Video Creation Skill Router is a meta-skill for AI video/animation skills—a discovery and orchestration layer for video creation capabilities. It doesn't produce videos itself, only handles matching, downloading, orchestration, and post-task inquiries. You just describe your needs, and it automatically: ① determines the video category and prioritizes locally installed skills; ② if none match locally, discovers and installs skills via the appropriate channel; ③ orchestrates the installed skill to produce the final video; ④ explains the installed skill's purpose and asks whether to keep or uninstall it.

What is Video Creation Skill Router?

The Video Creation Skill Router (video-skill-router) is a meta-skill designed to help users seamlessly discover, install, and orchestrate video creation AI skills. Instead of directly generating videos, it acts as a discovery and orchestration layer: it parses user requests (e.g., "make an explainer video on the Pythagorean theorem," "create a promo for my app," "edit out a slip-of-the-tongue from my talking head video") to determine the video category (commercial, educational, or general), then matches the request against locally installed skills first (for zero-latency reuse). If no local match is found, it falls back through a curated catalog, GitHub search, or general web search to find, download, and install the most suitable skill. Once installed, it reads the skill's documentation, executes its workflow, and outputs the final video file. After completion, it explains what the skill does and asks the user whether to keep or uninstall it—with full safety protocols (snapshot, validation, rollback) to ensure users never fear trying new skills. This skill is ideal for developers and content creators using platforms like Claude Code, Cursor, or Codex who want to automate video production without manually searching for and managing multiple video-skills repositories. It streamlines the entire process of making explainer videos, product demos, talking-head edits, motion graphics, Manim math animations, and Remotion videos.

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Documentation

Video Creation Skill Router (video-skill-router)

1. Positioning

This skill is a discovery and distribution layer for video creation capabilities. It serves regular users who have installed this skill—even if they have zero video skills locally, they can go from "state a need" to "get the final video."

It does not produce video content itself; it only does four things: match, download, orchestrate, and post-task inquiry. The execution layer is handled by specific skills (Manim math animation, Remotion motion graphics, editing suites, etc.), and this skill ensures the right skill enters and exits at the right time.

2. Core Pipeline (Five Steps)

After the user states a video requirement, execute in order; if any step fails, follow Section 6 fallback logic—do not silently skip.

Step 1: Requirement Parsing & Category Determination

Extract from the request: topic, source material (available/none), duration, style, platform. Then determine the video category (choose one of three):

CategoryTypical NeedsDetermination Keywords
CommercialProduct demo, ad, e-commerce, brand promotion, launch event产品、宣传、广告、电商、带货、促销、品牌、演示、营销、promo、product
EducationMath/science explanation, course, data chart narration, formula derivation知识、科普、教学、课程、数学、公式、数据、图表、讲解、科学、math、education
GeneralVlog, short film, talking head, mixed cut, subtitles/dubbing, daily editingvlog、日常、创意、短片、口播、混剪、字幕、配音、剪辑、edit

The category with the most keyword hits wins; ties are broken by Commercial > Education > General (more specific first). The user may also directly specify the category.

Step 2: Local Matching (Priority, Zero Cost)

python scripts/match_skill.py --query "<original user requirement>" [--category <category>]

The script merges scores from two pools: locally installed skills (scan ~/.workbuddy/skills/) + curated catalog (data/curated-catalog.json, 11 verified skill packs + 11 engine libraries). Decision based on Top1 source and score:

  • Local score ≥ 25 → Local skill available, no download, go directly to Step 4 execution (user's first priority: check local first). If the skill is registered in references/repos.md, report its upstream repository.
  • Local score < 25 and Top1 = catalog with score ≥ 35 → Verified installable but not installed, go to Step 3 Tier 1 direct install (no real-time search).
  • All scores < 35 → Local and catalog don't match, go to Step 3 Tier 2 real-time discovery.

Step 3: External Discovery & Download (Channel Selection by Category)

Three-tier channel escalation, stop when a hit is found:

  1. Curated Catalog (zero network): If Step 2 provided a catalog hit, install directly using:
    python scripts/manage_skill.py install --repo <owner/name> --subdir <skill_path> --reason "<why selected>"
    
    For multi-skill packs (multiple SKILL.md in one repo), must use --subdir to install the most relevant one, or --all-skills for full pack.
  2. GitHub Real-Time Search (when catalog doesn't cover):
    python scripts/search_anim.py --category <category> --query "<English keywords>" --json <candidates_file>
    python scripts/match_skill.py --query "<original requirement>" --candidates <candidates_file>
    
    The script uses the channel matrix in references/categories.md to select topics and query templates.
  3. Web Search Fallback: Use WebSearch tool to search for " video skill github 2026" or "awesome animation", to supplement candidates.

Candidate ranking rules: Skill packs with SKILL.md > Engine libraries > Personal new repos (new repos marked low confidence); keep at least 2 candidates per category, with fallback if primary fails. Before installation, brief the user: what will be installed, where it comes from, what it will do.

Step 4: Execution & Output (Orchestrate to Final Video)

  1. Understand the skill: Read the installed skill's SKILL.md thoroughly, or call the skill by name using the Skill tool (if this session's skill list has been refreshed; if the newly installed skill is not refreshed, the read-through path works just as well).
  2. Execute: Strictly follow the installed skill's own workflow, passing the user requirement, source material, and category information. Do not modify steps without permission.
  3. Output storage: Unified output to <current working directory>/video-output/<YYYY-MM-DD>-<topic-slug>/. The final product must be a video file (.mp4 preferred).
  4. Gap filling: If the skill only outputs intermediate files (e.g., scene files, clips), synthesize into a final video using FFmpeg—first run ffmpeg -version to check, if missing, report and suggest installation; do not let the pipeline stall.
  5. Presentation: Use present_files to show the final video.
  6. Failure & candidate switch: If execution fails → report the failure point → switch to the next candidate, reinstall, and re-run (up to 2 candidates) → if still failing, go to Section 6.

Step 5: Post-Task Inquiry (Explain Purpose + Keep/Uninstall)

After delivering the final video, output the installation summary in fixed format and ask:

📦 Skills installed for completing the requirement:

| Skill | Purpose | Source |
|---|---|---|
| <name> | <description from the skill's SKILL.md, one sentence> | <repo link> |

Its core capability: <one-sentence explanation for non-technical users about what this skill can do>.
Both installation and uninstallation have snapshots for easy recovery.

Reply "1" to keep it locally—next time you make a similar video, it will be reused directly without re-download.
Reply "2" to uninstall—safely removed with rollback support.
  • Reply "1" → python scripts/manage_skill.py keep --name <name> (manifest marks kept; no more prompts in the future)
  • Reply "2" → python scripts/manage_skill.py uninstall --name <name> (git rm or move to state/.trash/, output recovery method)
  • User no reply → keep installed; list again in the end-of-day task report, do not ask again.

3. Category → Channel Matrix (Summary)

Full matrix in references/categories.md.

CategoryTier 1 (Catalog, Zero Network)Tier 2 (GitHub Real-Time)Tier 3 (Web Search)
Commercialcatalog category=commercial: motion-design-skill / video-talkcraft / OpenMontage / ad-video-skillstopic: motion-design, lottie, remotion + search terms promo / ad / product"product promo video generator"
Educationcatalog category=education: manim_skill / MathLenstopic: manim, data-visualization + search terms math / education"manim animation tutorial"
Generalcatalog category=general: herk-edit-kit / OpenStoryline / baocuttopic: video-editing, ffmpeg + search terms editing / video"ai video editing skill"

For users with Jianying/CapCut installed: General category primarily uses pyJianYingDraft / capcut-cli engine direction (catalog engines section).

4. Output Specifications

  • Output directory: video-output/<date>-<topic>/, one directory per requirement, intermediate files and final video in the same directory.
  • File naming: <topic>-<purpose>.<ext>, e.g., product-demo-30s.mp4.
  • Every video delivered to the user must pass checks: file exists, size > 0, duration reasonable (matching requirement scale).
  • Report language: plain language; no jargon overkill; clearly explain "what was installed, what it can do, keep or remove."

5. Safety Protocol (Summary, Full in references/install-protocol.md)

  • Installation six steps: Snapshot → Download → Hard validation → Persist → Git record → Manifest log; abort immediately if any step fails.
  • Hard validation: Must include SKILL.md with frontmatter containing name+description, SKILL.md < 500KB, no sensitive patterns (reading credentials, sending data, rmtree, eval).
  • Uninstall protection: Only uninstall skills registered in manifest; user's original skills cannot be removed.
  • No rm -rf: Uninstall via git rm (git environment) or move to state/.trash/ (non-git environment); both are recoverable.
  • Batch limit: If installing more than 5 skills at once, pause and confirm with user.

6. Failure Fallback

Failure PointAction
Network completely downUse only local skills + catalog; if still no match, provide manual solution (candidate links + install commands), do not pretend to complete.
Installation validation rejectedSwitch to next candidate; report rejection reason (which restriction was violated).
Execution failureSwitch to next candidate and retry (≤2 times); report failure point, list of installed skills, and suggestions.
User requirement ambiguousFirst ask for three minimum elements: what topic, any source material, what duration—before proceeding.

7. Resources

  • references/categories.md: Category determination keywords, full channel matrix, typical pipelines per category
  • references/catalog.md: Human-readable curated catalog (11 skill packs + 11 engine libraries) + maintenance rules
  • data/curated-catalog.json: Machine-readable catalog (data source for match_skill.py)
  • references/sources.md: External channel list, GitHub search syntax, proxy detection rules, candidate pool
  • references/repos.md: Local skill → upstream repository mapping (Kyle environment with 34 skills, including shared base integration rules)
  • references/install-protocol.md: Complete install/uninstall/keep protocol and manifest format
  • scripts/match_skill.py: Requirement-to-skill matching scoring (local scan + catalog + candidates merging)
  • scripts/search_anim.py: Execute GitHub aggregated search by category channel
  • scripts/manage_skill.py: Install (--repo/--subdir/--all-skills) / List / Keep / Uninstall

8. Version History

VersionDateSummary of ChangesAuthor
v2.0.02026-10-23Repositioned as a video creation router for regular users (from anim-skill-router): new five-step pipeline including category-based channel discovery, orchestration-until-output, post-task keep/uninstall inquiry; added categories.md channel matrix and data/curated-catalog.json (11 skill packs all verified with SKILL.md); manage_skill.py now supports --repo/--subdir/--all-skills/keep, soft uninstall to state/.trash for non-git environmentsGuipi

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