davinci-resolve-mcp is an MCP server that lets AI assistants control DaVinci Resolve Studio through its scripting API, including editing, media organization, rendering, grading, and other project tasks. It is for users who want agents to operate Resolve and inspect media through a local control panel. The catalogue entries support agent workflows for this Resolve integration.
Borrowing it
Nothing to install: this file belongs to samuelgursky/davinci-resolve-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/samuelgursky/davinci-resolve-mcp/main/.agents/skills/resolve-tighten-recording/SKILL.mdgit clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcpWrote 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/samuelgursky/davinci-resolve-mcp/resolve-tighten-recording)<a href="https://agentmods.dev/skills/samuelgursky/davinci-resolve-mcp/resolve-tighten-recording"><img src="https://agentmods.dev/badge/skills/samuelgursky/davinci-resolve-mcp/resolve-tighten-recording/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/samuelgursky/davinci-resolve-mcp/resolve-tighten-recording"><img src="https://agentmods.dev/badge/skills/samuelgursky/davinci-resolve-mcp/resolve-tighten-recording.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00102 | $0.02093 |
| Opus 5 | $0.00051 | $0.01046 |
| Sonnet 5 | $0.00020 | $0.00419 |
| Haiku 4.5 | $0.00010 | $0.00209 |
Grade A, and why
resolve-tighten-recording 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resolve Tighten Recording
Turns one long raw take into a tightened variant timeline. The variant is the deliverable; the original timeline is never touched.
Everything below was measured live against Resolve Studio 21.0.1.11 / MCP v2.80.1 on 2026-08-06, on a real 28.5-minute 30fps screen recording, unless a different date is given.
The deliverable contract
Deliver a tightened variant timeline, plan → review → confirm. Do not grade, caption, or add anything; a tighten is pure subtraction. The variant runs generous on purpose — recovering over-cut material is slow and invisible, trimming further is fast and visible.
Two things always go back to the editor with the variant:
- The largest lifts. A lift is only silence to the microphone. Measured on real material (2026-07-31): a 29s lift was 70% silent on-screen demonstration — real content, cut because nobody spoke over it. Anything over ~15s deserves a human look before the variant is trusted.
- What silence-driven tightening cannot hear — see the last section.
Workflow
-
Lift the analysis caps for long media —
media_analysis set_caps_preset {preset: "unlimited"}. The standard preset carries a 90-second wall clock; multi-hour material dies on it. Restorestandardwhen done (step 7). -
Transcribe —
media_analysis start_batch_jobwith{clip_id, vision: false, transcription: {enabled: true, ...}}, thenrun_batch_job_slice {job_id}. Two traps in one call pair:- Batch jobs do not advance themselves.
start_batch_jobreturns a durable job inqueuedand nothing runs until a slice call drives it. run_batch_job_sliceblocks until the slice completes — budget roughly 4-7x realtime for local whisper (measured: 28.5min of Chinese speech in 5m14s via mlx_whisper) and run it from a background process, not the main conversation.
Copy
clip_idfrommedia_pool probe_media_pooloutput. Never type it from memory: one transposed hex pair (measured 2026-06:b9ab→9bab) fails every downstream call in ways that look like engine bugs. - Batch jobs do not advance themselves.
-
Plan —
edit_engine plan_tighten {timeline_name}. Dry run; nothing moves.min_pause_secondsraises the bar when the default cuts too fine-grained. The plan persists on disk — planning and executing in different sessions is fine. -
Review with the editor: lift count, estimated removed seconds, and the largest lifts (sorted by duration). This is the approval gate the whole tool sequence exists for — do not skip it because the numbers look reasonable.
-
Execute —
edit_engine execute_tighten {plan_id}returns aconfirm_token(TTL 300s); re-call with the token. Both calls in the same MCP session is verified; a token across a server restart is not — don't bet on it. -
Verify, in this order:
timeline detect_gaps_overlapson the variant → must be 0 / 0.readback.after.clip_countin the execute response ≈ 2× the video keep-range count when audio is mirrored (113 video + 113 audio = 226 measured). If it equals the video count alone, the variant is silent — stop and say so.- Spot-check placement with
timeline clip_where {track_type, track_index}against the plan's keep ranges (coordinate rules below).clip_wherereads the current timeline only —set_currentto the variant first; it takes notimeline_nameargument. - Total duration ≈ original − estimated_removed_seconds.
-
Clean up — restore
set_caps_preset {preset: "standard"},project_manager save, and list timelines:execute_tightenarchives the source timeline once (_versioning.archived: true, by design), so a*_archived_vNNappears. Surface it; deleting is the editor's call.
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.
- 10d ago Changed · -1 lines e30d00903b70
- 13d ago First seen · 158 lines · 102 tokens per session scan A cfdd4e054847
resolve-tighten-recording is a skill published in the GitHub repository samuelgursky/davinci-resolve-mcp (2,701 stars, last pushed yesterday), licensed MIT. It adds 102 tokens to every session and 2,093 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
cyxj-blog-pub
A publishing workflow for adding a finished article to an Astro blog, a website built with the Astro framework.
cyxj-transcript
A workflow that turns a video or audio transcript into a structured article draft in Obsidian Markdown.
cyxj-video-cover
A generator for making 16:9 video-cover background scenes without text, letters, numbers, logos, or watermarks.
skill
Skill "skill" from Mun1to/vidorq, covering vidorq skill — edición por ia (v1), flujo, uso, 1) transcribir (word-level, local) and 3) renderizar.
davinci-resolve
Automate DaVinci Resolve media and timelines with its scripting API, build Fusion workflows, and configure render jobs.
cyxj-jingxuan
A writing assistant for applying to have a finished video selected for 抖音精选, a curated feature on the Douyin video platform. It reads the video's actual subtitles or transcript and writes a 150–250 character Chinese application in four sections.