Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add GGbond-bo/MemOmics-Agent --skill kanban-video-orchestratorgit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ggbond-bo/memomics-agent/kanban-video-orchestrator)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/kanban-video-orchestrator"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/kanban-video-orchestrator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/kanban-video-orchestrator"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/kanban-video-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00236 | $0.02545 |
| Opus 5 | $0.00118 | $0.01273 |
| Sonnet 5 | $0.00047 | $0.00509 |
| Haiku 4.5 | $0.00024 | $0.00254 |
Grade A, and why
kanban-video-orchestrator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to kanban-video-orchestrator — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kanban Video Orchestrator
Wrap any video request — from a 15-second product teaser to a 5-minute narrative short to a music video to an ASCII loop — in a Hermes Kanban pipeline that decomposes the work to specialized agent profiles.
This skill does not render anything itself. It is a meta-pipeline that:
- Scopes the request through targeted discovery
- Designs an appropriate team (which roles, which tools per role) based on the style
- Generates a setup script that creates Hermes profiles, project workspace, and the initial kanban task
- Hands off to the director profile, which decomposes via the kanban
- Monitors execution, helps intervene when tasks stall or fail
The actual rendering happens inside the kanban once it's running, via whichever
existing skills + tools fit the scenes — ascii-video, manim-video, p5js,
comfyui, touchdesigner-mcp, blender-mcp, songwriting-and-ai-music,
heartmula, external APIs, or plain Python with PIL + ffmpeg.
When NOT to use this skill
- The video is one continuous procedural project that needs no specialists. Just write the code directly.
- The user wants a quick one-shot conversion (e.g. "convert this mp4 to a GIF") — use ffmpeg directly.
- The output is a static image, GIF, or audio-only artifact — use the matching specific skill (
ascii-art,gifs,meme-generation,songwriting-and-ai-music). - The work fits a single existing skill cleanly (e.g. a pure ASCII video — just use
ascii-video).
Workflow
DISCOVER → BRIEF → TEAM DESIGN → SETUP → EXECUTE → MONITOR
Step 1 — Discover (ask the right questions)
The discovery process is adaptive: ask only what is actually needed. Always start with three questions to identify the broad shape:
- What is the video? (one-sentence brief)
- How long? (5-30s teaser / 30-90s short / 90s-3min explainer / 3-10min film / longer)
- What aspect ratio + target platform? (1:1 / 9:16 / 16:9; X, IG, YouTube, internal, etc.)
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/brief.md.tmpl 2.1 KB
- assets/setup.sh.tmpl 8.1 KB
- assets/soul.md.tmpl 631 B
- references/examples.md 10 KB
- references/intake.md 7.8 KB
- references/kanban-setup.md 11 KB
- references/monitoring.md 7.3 KB
- references/role-archetypes.md 12 KB
- references/tool-matrix.md 14 KB
- scripts/bootstrap_pipeline.py 18 KB runs code
- scripts/monitor.py 6.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 209 lines · 236 tokens per session scan A 2bd6a08de5f3
kanban-video-orchestrator is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 236 tokens to every session and 2,545 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to kanban-video-orchestrator, differing in 8 lines, and is treated as a copy.
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