MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Delivery, rendering, and deliverable QC in the DaVinci Resolve MCP.
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-delivery --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/resolve-delivery .claude/skills/resolve-delivery && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "resolve-delivery" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-delivery into .claude/skills/resolve-delivery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-delivery", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-deliveryType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-delivery --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcp.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/resolve-delivery .agents/skills/resolve-delivery && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "resolve-delivery" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-delivery into .agents/skills/resolve-delivery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-delivery", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-delivery --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcp.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/resolve-delivery .cursor/skills/resolve-delivery && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "resolve-delivery" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-delivery into .cursor/skills/resolve-delivery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-delivery", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/samuelgursky/davinci-resolve-mcp.git --path .agents/skills/resolve-delivery--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-delivery --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcp.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/resolve-delivery .gemini/skills/resolve-delivery && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "resolve-delivery" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-delivery into .gemini/skills/resolve-delivery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-delivery", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-deliveryInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcp.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/resolve-delivery .github/skills/resolve-delivery && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "resolve-delivery" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-delivery into .github/skills/resolve-delivery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-delivery", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-delivery --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/samuelgursky/davinci-resolve-mcp.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/resolve-delivery .opencode/skills/resolve-delivery && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "resolve-delivery" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-delivery into .opencode/skills/resolve-delivery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-delivery", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
resolve-deliveryDelivery, rendering, and deliverable QC in the DaVinci Resolve MCP.
Resolve Delivery is an agent skill from samuelgursky/davinci-resolve-mcp. Delivery, rendering, and deliverable QC in the DaVinci Resolve MCP. Apply when preparing render jobs, validating render settings, QCing a finished render against a spec (video/loudness/blanking/completeness), building or reconciling a render manifest, expanding texted/textless/stems/slate deliverables, verifying media ingest, or producing a provenance/episode report — live in a running Resolve OR offline against rendered files and the project DB. Routes to the live render tools, the offline…
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: MCP server integration for DaVinci Resolve Studio. The licence is MIT.
Read from SKILL.md and the folder at commit 94c7a88. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Resolve Delivery loads about 2.1k tokens when it runs. Until then it costs about 146 tokens; SKILL.md has 937 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from samuelgursky/davinci-resolve-mcp at commit 94c7a88, republished under its MIT licence (© samuelgursky). 937 words, ~2,076 tokens.
.claude/skills/resolve-delivery/SKILL.md (or your agent's skills folder).Bridges delivery craft to this repo's tools.
deliverables-knowledge, post-supervisor, and
quality-control / qc-domain skills (distributor specs, mastering, QC
discipline). Use for what the spec should be, not tool mechanics.docs/kernels/render-deliver-kernel.md (the render
planning/validation boundary + Quick Export).resolve-advanced/README.md → deliverable,
media, provenance.| Job | Server | Tools |
|---|---|---|
| Plan / validate / run renders in a running Resolve | davinci-resolve (Python, live) | render, render_presets |
| QC a finished render vs spec, verify ingest, build manifests/provenance with no Resolve open | davinci-resolve-advanced (Node) | deliverable, media, provenance |
Named render intents. list_delivery_targets → prepare_delivery_job(target, target_dir). Ask for prores422hq_master, dnxhr_hqx_master, h264_1080p_web,
or an alias (youtube, tiktok, avid, stems). One definition emits BOTH the
Resolve render settings and the deliverable_qc spec, so the returned qc_spec
is what you QC the finished file against — do not hand-write a second spec.
Format/codec resolve against the live matrix. A target this machine or
license cannot render fails with the available lists; it never silently
substitutes. Use check_availability: true to see what this install supports.
Image-sequence targets return qc_spec: null — deliverable_qc probes one
file, a sequence is many. That is expected, not a gap.
Bitrate is deliberately unset (Resolve has no bitrate key). Pin quality
yourself via settings if a spec demands it.
Programme loudness is a separate projection. A target names a standard via
overrides: {loudness_standard: "ebu_r128"}; resolve_delivery_target then
returns a loudness_target alongside qc_spec. Hand loudness_target.target
to advanced loudness_qc. render(action='list_loudness_standards') lists the
five named contracts (web, podcast, ebu_r128, atsc_a85,
ott_dialogue_gated) — cite one, never invent the numbers.
No shipped target names a loudness standard by default: a ProRes master has no
inherent programme loudness and a broadcast handoff depends on territory.
A loudness_note tells you when none is pinned.
Dialogue-gated standards emit no gradeable integrated. loudness_qc
measures full-programme; grading a dialogue-gated figure against that means
nothing. The number rides in meta for a properly gated meter and only true
peak is asserted. This is deliberate, not a missing field.
Use the lower-level path below when you need something no target covers.
probe_render_matrix (formats/codecs/res) →
validate_render_settings → safe_set_render_settings (dry-run capable) →
prepare_render_job (adds a job, does not start it).GetRenderSettings readback is version/page dependent — the kernel validates
and applies through SetRenderSettings regardless.safe_quick_export forces EnableUpload=False and needs allow_render=True
before it actually renders.prepare_render_job(from_preset=...).
SetRenderSettings applies your keys on top of whatever the Deliver page is
holding rather than replacing it, and a loaded preset carries more state than
the keys you pass. An Audio Only preset plus an explicit ExportVideo: true
has been measured to queue a job that reads back IsExportVideo: true and
renders an mp4 with no video stream (issue #123). There is no way to detect
this: the API documents neither GetRenderSettings nor
GetCurrentRenderPresetName, so the inherited state cannot be read — only
pinned. Verify the OUTPUT, not the job: ffprobe for a codec_type=video
stream. A long timeline that "renders" in seconds is the tell.UseFullExtents, AddFrameHandles,
DataBurnIn (issue #131). SetRenderSettings ignores unknown keys silently,
so on an older build these are dropped with no signal rather than refused —
which is exactly the failure mode that produces a deliverable missing handles
nobody notices until the conform. Check resolve_control check_version_support
before offering them, and note AddFrameHandles is also ignored when full
extents is enabled, so it can do nothing for two different reasons.deliverable actions)Report-only, gate: review — never auto-pass-clear. Run these on the finished
file, not the timeline:
deliverable_qc — ffprobe a render vs its spec → pass/fail per field.loudness_qc — ebur128 LUFS / true-peak / LRA.reframe_blanking_check — pillar/letterbox/blanking vs expected framing.conform_completeness — every intended shot present in the delivered cut.re_delivery_diff — what changed between two delivery versions.render_manifest — build / reconcile the manifest of what was delivered.expand_deliverable — derive texted / textless / stems / slate / leader
entities from a master.spec_from_authored — turn the authored deliverable vocabulary (codec display
names, "1920x1080", "-16 LUFS", <SHOW>_<EP>_<YYYYMMDD>.mov naming) into a
deliverable_qc spec plus a loudness_qc target. Anything it cannot map is
listed in unmapped[] rather than dropped, so an unrecognized codec surfaces
instead of quietly producing a spec with no codec check in it.Two things that bite when hand-writing specs, both handled by the projections:
container is "mov" for both .mov and .mp4 — ffprobe reports
format_name=mov,mp4,m4a,... for each and only the first token is kept. Use
video.codec to tell them apart; a spec asserting container: "mp4" always fails.deliverable_qc field. It comes back as a separate
loudnessTarget for loudness_qc.media (front-end / AE): ingest_verify (hash seal / verify / dupes),
media_inventory (fps/codec/colorspace/TC + card gaps), sync (picture↔sound
TC + drift/MOS), relink_manifest, rename_plan (refuses camera
originals) / reel_normalize, turnover_package, project_hygiene.provenance (audit): grade_provenance ("why is this graded this way"),
gallery_lineage, cdl_export / cdl_diff (round-trip asserted),
revision_tracking, episode_report.deliverable/media QC needs ffmpeg + ffprobe on PATH (GPL, not
bundled) — call the advanced capabilities tool for live status + install hints.Render probes may render derivatives of synthetic fixtures, never user source
media. media.rename_plan refuses camera originals by design — do not override
without explicit approval. Preserve the camera-original-to-delivery chain.
© samuelgursky, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/resolve-delivery of samuelgursky/davinci-resolve-mcp.
Open the folder on GitHubat commit 94c7a88
Resolve Delivery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Resolve Delivery this skillsamuelgursky/davinci-resolve-mcp | 3.4k | — | ~2.1k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Crush Configurationcharmbracelet/crush | 29k | — | ~3.7k | Automated safety check: Pass | Custom licence | |
| Context Mode Output Sandboxmksglu/context-mode | 26k | — | ~4.1k | Automated safety check: Pass | Custom licence |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
anthropics/claude-plugins-official
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
charmbracelet/crush
Explains how to configure the Crush coding agent with crushrc or crush.json, covering providers, models, LSPs, MCP servers, hooks, permissions and config precedence.
mksglu/context-mode
Routes large command, file, API and browser output through context-mode tools so only the needed result enters the agent's context, instead of dumping it via Bash.
warpdotdev/warp
Migrates the compatible subset of settings and global file-based MCP servers from the Warp desktop app into Warp Agent CLI without exposing credentials or state.
samuelgursky/davinci-resolve-mcp
Orientation and index for DaVinci Resolve MCP work — grading, editing, conforming, delivery, media analysis, and .drp/.drt/.drx file work, live in a running Resolve or offline with none open.
samuelgursky/davinci-resolve-mcp
Assembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP.
samuelgursky/davinci-resolve-mcp
The editorial and finishing preferences this project's work is judged against — cut rhythm, shot selection, delivery conventions, and the corrections that have already been given.
samuelgursky/davinci-resolve-mcp
Audio and Fairlight work in the DaVinci Resolve MCP. An agent skill from samuelgursky/davinci-resolve-mcp.
samuelgursky/davinci-resolve-mcp
Color grading and look work in the DaVinci Resolve MCP. An agent skill from samuelgursky/davinci-resolve-mcp.
samuelgursky/davinci-resolve-mcp
Conforming, relinking, and finishing prep in the DaVinci Resolve MCP.
Works with
Categories
Delivery, rendering, and deliverable QC in the DaVinci Resolve MCP. Resolve Delivery is an agent skill from samuelgursky/davinci-resolve-mcp. Delivery, rendering, and deliverable QC in the DaVinci Resolve MCP.
Resolve Delivery fits situations like: tasks that involve MCP servers.
Run `npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a claude-code`. Or copy the skill folder (.agents/skills/resolve-delivery in samuelgursky/davinci-resolve-mcp) into .claude/skills/resolve-delivery in your project. Claude Code loads it when a task matches its description.
Run `npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a codex`. Or copy the skill folder (.agents/skills/resolve-delivery in samuelgursky/davinci-resolve-mcp) into .agents/skills/resolve-delivery in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-delivery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/resolve-delivery, .gemini/skills/resolve-delivery, .github/skills/resolve-delivery and .opencode/skills/resolve-delivery in your project.
SKILL.md names no scripts, command-line tools or credentials: Resolve Delivery is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Resolve Delivery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Resolve Delivery: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Crush Configuration (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
samuelgursky (a GitHub user) maintains it in samuelgursky/davinci-resolve-mcp, which has 3,448 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 9, 2026.
Source: samuelgursky/davinci-resolve-mcp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.