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 CaYatur/PremiereProMCP --skill skillgit clone --depth 1 https://github.com/CaYatur/PremiereProMCPWrote 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/cayatur/premierepromcp/skill)<a href="https://agentmods.dev/skills/cayatur/premierepromcp/skill"><img src="https://agentmods.dev/badge/skills/cayatur/premierepromcp/skill.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.02796 |
| Opus 5 | $0.00012 | $0.01398 |
| Sonnet 5 | $0.00005 | $0.00559 |
| Haiku 4.5 | $0.00002 | $0.00280 |
Grade B, and why
premiere-pro-mcp-editing scanned grade B with 1 finding 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 2d 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
**Never tell the user a capability is unsupported before running How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PPMCP agent skill — automatic systems over tool count
Quality bar
A long chain of atomic calls is not a better cut than one playbook that already knows the professional defaults. Optimize for cut quality, not call count:
| Capability | Use |
|---|---|
| Automatic edit | edit_auto + playbooks — one call, complete structure |
| Editable titles | text engine: UXP → hybrid → CEP → PNG fallback |
| Delivery polish | quality_pass / edit_delivery — grade + transitions + fades |
| QA gate | edit_verify before export |
| Failures | recovery hint, then continue the plan |
| Token cost | playbooks + compact: true + the standard tool profile |
Never sacrifice quality for “one more atomic tool.” Prefer automatic playbooks that already include grade + transitions + fades (+ normalize).
Session flow
edit_bootstrap
→ edit_auto (intent) OR edit_playbook_run (named pack)
→ edit_verify
→ (strong model) atomic polish
→ export / save
Prompt templates (copy-paste)
Full system + user prompts: docs/PROMPTS.md
| Track | Tools | Use when |
|---|---|---|
| Weak | only edit_bootstrap + edit_auto + edit_help |
small models, low token |
| Strong / premium | playbooks first → atomics + vision QA | client review, trim, captions |
Weak model (cheap, still good)
Only:
edit_bootstrapedit_autowith intent + media argsedit_helpif stuck
{
"intent": "youtube episode",
"args": {
"paths": ["C:\\\\media\\\\clip.mp4"],
"title": "Episode 1",
"lowerThird": "Alex Host",
"musicPath": "C:\\\\music\\\\bed.mp3",
"look": "warm"
}
}
{
"intent": "tiktok",
"args": {
"paths": ["C:\\\\media\\\\clip.mp4"],
"title": "WAIT FOR IT",
"sfxHits": [{ "path": "C:\\\\sfx\\\\whoosh.wav", "atSeconds": 1.2 }],
"look": "warm"
}
}
Strong model (premium)
- Same automatic pack for structure + polish (
edit_auto/edit_playbook_run) - Then atomics:
clip_trim,workflow_ken_burns,color_set_param,audio_set_gain, silence/captions - Vision:
sequence_screenshot/edit_verify - Export with explicit path/preset when user asks
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.
- 2d ago Changed · +43 lines scan A → B 3935afda1bfc
- 7d ago First seen · 223 lines · 23 tokens per session scan A 037ad13e51dd
premiere-pro-mcp-editing is a skill published in the GitHub repository CaYatur/PremiereProMCP (3 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 2,796 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
premiere-pro-mcp
Install, verify, troubleshoot, and operate the Adobe Premiere Pro MCP server. Use when a user wants an agent to set up Premiere MCP, connect Claude Code/Codex/Claude Desktop, control Premiere, import media, build sequences, edit timelines, apply effects, or diagnose bridge issues.
edit-premiere-project
Inspect, edit, verify, and export an open Adobe Premiere Pro project through the local Premiere MCP Bridge.
starreel-drama-production
Operating skill for any AI agent driving the StarReel short-drama production pipeline (script → rewrite → extract → portraits + sheets → storyboards → frames → video → voiceover → final cut) over MCP or REST. Covers the ordered workflow, the entry-point decision table (which channel each kind of customer material…
post-to-xhs
A publishing guide for 小红书, a Chinese social-media platform. It covers posting image-and-text content or a longer formatted article, using supplied content or a webpage.
screenshot-game-view
Capture a screenshot of the Unity Editor's Game View by reading its internal render texture directly. Image size matches the current Game View resolution; the tool corrects Y-flip on DirectX / Metal so the output is always upright. Requires an open Game View window.
muapi-workflow
Build, run, and visualize multi-step AI generation workflows. The AI architect translates natural language descriptions into connected node graphs — chain image generation, video creation, enhancement, and editing into automated pipelines.