guipi888/video-skill-router

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):

Category	Typical Needs	Determination Keywords
Commercial	Product 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
Education	Math/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
General	Vlog, short film, talking head, mixed cut, subtitles/dubbing, daily editing	vlog、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.

Category	Tier 1 (Catalog, Zero Network)	Tier 2 (GitHub Real-Time)	Tier 3 (Web Search)
Commercial	catalog category=commercial: motion-design-skill / video-talkcraft / OpenMontage / ad-video-skills	topic: motion-design, lottie, remotion + search terms promo / ad / product	"product promo video generator"
Education	catalog category=education: manim_skill / MathLens	topic: manim, data-visualization + search terms math / education	"manim animation tutorial"
General	catalog category=general: herk-edit-kit / OpenStoryline / baocut	topic: 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 Point	Action
Network completely down	Use only local skills + catalog; if still no match, provide manual solution (candidate links + install commands), do not pretend to complete.
Installation validation rejected	Switch to next candidate; report rejection reason (which restriction was violated).
Execution failure	Switch to next candidate and retry (≤2 times); report failure point, list of installed skills, and suggestions.
User requirement ambiguous	First 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

Version	Date	Summary of Changes	Author
v2.0.0	2026-10-23	Repositioned 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 environments	Guipi

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