| name | agentic-video |
| description | How an AI agent drives the fully-agentic video pipeline (plan → acquire → verify → decide → gate → render) with complete control over every image, video, and music asset. |
| version | 5.0.0 |
Agentic Video Pipeline — Agent Skill
This skill turns the Automated Video Generator into a fully agentic director. Instead
of only writing a script and pressing "generate", the agent controls every asset:
it downloads candidate images, videos, and background music into separate folders,
verifies each one (vision + signal checks), decides approve / reject / replace,
and only then lets the final gate render — but only after EVERYTHING is verified.
The six stages (MCP tools)
| Stage | Tool | What the agent does |
|---|
| 1. Plan | agentic_plan | script → plan.json (scenes, per-scene visual intent, music query) |
| 2. Acquire | agentic_acquire | download N candidates into assets/images/<scene>/, assets/videos/<scene>/, assets/music/ |
| 3. Verify | agentic_verify_all | runs full matrix → verification/*.json (I1–I7, V1–V7, M1–M5) |
| 4. Decide | list_pending_assets, get_asset_preview, approve_asset, reject_asset | agent SEES each asset (base64 thumb) and decides |
| 5. Gate | agentic_gate | holistic X1–X6 cross-checks; BLOCKS render if anything unverified |
| 6. Render | renderAgenticSlideshow() (CLI npm run agentic) | renders only approved assets into a real MP4 via bundled ffmpeg |
Drive it from ANY coding agent (not just Hermes)
This skill is tool-agnostic. Any agent that can run a shell or call an MCP
server can use it:
- Claude Code / Cursor / Windsurf / Antigravity(Gemini) / OpenCode / Codex /
GitHub Copilot — all read the repo's instruction files
(
AGENTS.md, GEMINI.md, .github/copilot-instructions.md,
.cursor/rules/agentic-video.mdc, opencode.json, .codex/AGENTS.md) and
can run:
npm run agentic -- --topic "5 home workout exercises" --title "Home Workout" --backend agent --orientation landscape --images
npm run agentic:batch
- MCP-capable agents — connect
npx tsx src/mcp-server.ts and call
agentic_run (one-shot) or the granular tools below.
- Library import (TypeScript agents):
import { runAgenticPipeline, renderAgenticSlideshow } from './src/agentic/orchestrate.js';
const res = await runAgenticPipeline({ topic, title, backend: 'agent' });
const mp4 = await renderAgenticSlideshow(res);
Autonomy levels
Set AGENT_AUTONOMY (env):
L2-autonomous (default): agent approves/rejects itself, logs rationale to approval-manifest.json.L0-manual / L1-suggest: surface approve/reject to a human instead.
Backends: "Hermes IS the AI" vs "vision assist"
The pipeline runs with one of two backends (set per call):
| Backend | What does the AI? | External AI key needed? |
|---|
agent (default) | Hermes/OpenClaw writes the script, expands keywords, and DECIDES every asset by reading the verification matrix. Verification uses deterministic signal checks (resolution, duration, license, silence, watermark-heuristic, NSFW-heuristic). | NONE — works fully offline, free. |
vision | Same agent control, BUT vision relevance (does the image/video match the keyword?) is scored by Gemini/Ollama when MEDIA_VERIFICATION_ENABLED + a key is present. | Only if you opt into vision. |
You said: "if this is controlled by you, the Hermes AI agent, I don't want to use
any other AI models — all the AI work you can do yourself."
That is exactly backend: 'agent'. No Google Gemini key, no Ollama model required.
The agent reasons over structured verification output (confidence scores + reasons +
signal metrics) and approves/rejects/replaces each image, video and music track itself.
Vision similarity is an optional bolt-on, never a dependency.
One-shot agent run (recommended)
agentic_run does the entire 6-stage loop in one call:
- Hermes writes the script from a
topic (heuristic; or plug your LLM via agent.writeScript).
- Hermes expands per-scene keywords (heuristic; or
agent.expandKeywords).
- Acquire real candidates (Openverse/Pexels/Pixabay + free-music).
- Verify (signal checks always; vision only in
vision backend).
- Hermes DECIDES each asset (approve / reject / replace-with-refetch).
- Final gate — blocks render unless everything is verified + approved.
{ "topic": "5 home workouts", "title": "Home Workout", "backend": "agent" }
Or drive the stages yourself (full control)
agentic_plan → agentic_acquire → agentic_verify_all →
list_pending_assets / get_asset_preview (SEE the asset) / approve_asset / reject_asset
→ agentic_gate → render.
Old workflow is UNTOUCHED
The classic path (input/scripts/input-scripts.json → npm run generate, the Electron app, the
existing MCP generate_video tool) is completely unaffected. This agentic pipeline is
additive — a parallel, agent-driven path. Turn it on only when you want the agent in control.
- Call
agentic_plan with the script.
- Call
agentic_acquire (candidatesPerAsset=2).
- Call
agentic_verify_all.
- Loop:
list_pending_assets → for each, get_asset_preview to SEE it →
approve_asset (good) or reject_asset (wrong → auto re-fetch) with a rationale.
- Call
agentic_gate. If it BLOCKS, read the failed checks (X2 missing scene, etc.)
and fix decisions, then re-gate.
- Once GATE PASS, hand the approved
render-manifest.json to the render stage.
What is verified
- Images: relevance (vision), no watermark/text, NSFW-safe, min resolution, aspect ratio, license.
- Videos: same vision checks + duration fit + codec + aspect ratio + license.
- Music: valid license, duration ≥ video, no silence/corrupt, min bitrate (96 kbps).
- Cross-checks (gate): TTS duration alignment, every scene covered, no unresolved
decisions, runtime limit, attribution completeness, caption sync.
Files (for reference)
src/agentic/plan.ts, acquire.ts, verify.ts, gateway.ts, gate.ts, types.ts, workspace.ts, agent.ts, orchestrate.tssrc/lib/music-verifier.ts (new), src/lib/media-verifier.ts (extended: watermark/safety)src/adapters/mcp/register-agentic-tools.ts (the 9 MCP tools)- Workspace per job:
agentic-pipeline/workspaces/<jobId>/
