Linkdigest Social Link Reader
sickn33/agentic-awesome-skills
Read one public Xiaohongshu, Douyin, TikTok, YouTube, X or WeChat article link into text an agent can use (transcript, image text, key points) via the LinkDigest API or MCP server.
Assembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP.
$ npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-rough-cut -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-rough-cut --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-rough-cut .claude/skills/resolve-rough-cut && 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-rough-cut" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-rough-cut into .claude/skills/resolve-rough-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-rough-cut", 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-rough-cutType 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-rough-cut -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-rough-cut --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-rough-cut .agents/skills/resolve-rough-cut && 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-rough-cut" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-rough-cut into .agents/skills/resolve-rough-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-rough-cut", 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-rough-cut -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-rough-cut --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-rough-cut .cursor/skills/resolve-rough-cut && 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-rough-cut" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-rough-cut into .cursor/skills/resolve-rough-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-rough-cut", 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-rough-cut--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-rough-cut -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install samuelgursky/davinci-resolve-mcp resolve-rough-cut --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-rough-cut .gemini/skills/resolve-rough-cut && 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-rough-cut" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-rough-cut into .gemini/skills/resolve-rough-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-rough-cut", 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-rough-cutInstalls 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-rough-cut -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-rough-cut .github/skills/resolve-rough-cut && 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-rough-cut" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-rough-cut into .github/skills/resolve-rough-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-rough-cut", 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-rough-cut -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-rough-cut --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-rough-cut .opencode/skills/resolve-rough-cut && 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-rough-cut" agent skill from https://github.com/samuelgursky/davinci-resolve-mcp/tree/main/.agents/skills/resolve-rough-cut into .opencode/skills/resolve-rough-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "resolve-rough-cut", 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-rough-cutAssembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP.
Resolve Rough Cut is an agent skill from samuelgursky/davinci-resolve-mcp. Assembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP. Apply when asked for a rough cut, first cut, assembly, or "make me a timeline" from a folder of footage — day-in-the-life, BTS, process, or product-shoot material destined for Reels, TikTok, or Shorts. Covers ingest, shot finding at scale, vertical timeline setup, and gapless assembly.
Its SKILL.md is about 2.9k 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 and TikTok. The repository describes itself as: MCP server integration for DaVinci Resolve Studio. The licence is MIT.
8 steps, taken from the first numbered list in SKILL.md.
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.
Shell commands in SKILL.md call:
python3pipgitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip and git, which can reach the network depending on how they are called.
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 Rough Cut loads about 2.9k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 1,582 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). 1,582 words, ~2,916 tokens.
.claude/skills/resolve-rough-cut/SKILL.md (or your agent's skills folder).Turns a folder of raw footage into an assembled, gapless timeline. The timeline is the deliverable, not a finished video.
For editorial craft (why a cut works) see docs/guides/editorial-decision-guide.md.
For the analysis layer see the resolve-media-analysis skill. This skill is the
assembly recipe and the traps between them.
Deliver an assembled timeline. Do not add titles, captions, text cards, effects, transitions, music, or grading unless explicitly asked. Editors who ask for a rough cut want shot selection and pacing; styling is theirs. Adding graphics is work that gets thrown away — and see the Fusion trap below for why it also silently fails.
Render an mp4 only to preview the cut, and say that is what it is.
Probe before importing. ffprobe every clip for r_frame_rate,
avg_frame_rate, and rotation. Two things routinely differ from what the
filenames and Resolve suggest — see Verified traps.
For a series, add a timeline to the existing project — don't create a new
one. Check project_manager list first. One project per series keeps the
bins, analysed clips and settings in one place; one per episode scatters
them. Only create a project for a genuinely new series.
On a NEW project, set BOTH frame rates before any timeline exists.
timelineFrameRate is locked once a timeline is created — set it via
project_manager safe_set_project_settings. timelinePlaybackFrameRate
cannot be set through the API at all, so ask the user to set it in the UI
now, as a setup step:
Project Settings (gear, bottom-right) → Master Settings → Playback frame rate
Quote that path as written and do not extend it from memory — if the user
cannot find it on their build, say you cannot see their screen and point at
Blackmagic's manual rather than guessing a second location. Improvised UI
directions are what issue #132 cost a user; see the retime entry in
resolve-edit for the rule.
Do not defer this to the end or write it off as cosmetic; a mismatch has been reported to affect playback and output, not just the display. On an existing project, read both back and only raise it if they are wrong.
Create a bin named for the episode, media_pool set_current_folder to it,
THEN import. That order is mandatory, not stylistic: ImportMedia has no
destination parameter and always lands in the current folder, so importing
first puts the clips wherever the current folder happens to be — see the
MediaPool.ImportMedia (current-folder destination only) entry in the
api_truth ledger. Restore the previous current folder afterwards if it
matters. One bin per episode, not one shared dump — it keeps analyze_bin
scoped and the Media Pool readable across a long series.
Analyse — media_analysis analyze_bin, sampling_mode="adaptive_capped".
Complete the commit_vision loop for every clip; leaving one in
pending_host_vision_analysis is a failure, not a partial success (AGENTS.md).
Find shots via contact sheets, not one frame at a time:
python3 scripts/contact_sheet.py <clip_dir> <out_dir> — see Shot finding.
Assemble in one call — media_pool create_timeline_from_clips with
positioned clip_infos. Name the timeline for the episode.
Verify — timeline detect_gaps_overlaps must return zero of both, and
check the total duration against the target.
media_analysis extracts frames at full source resolution. A 35-minute 4K
clip yields 80 frames at 2160x3840. Reading those individually to satisfy
commit_vision costs ~1,100 tokens each — a four-clip shoot exceeds 250 frames.
Tile them instead. scripts/contact_sheet.py burns frame index, timestamp and
selection_reason onto each tile, so per-frame findings stay reportable by
index. Roughly a 15x saving with no loss of coverage. It needs Pillow
(pip install Pillow) — the repo treats Pillow as optional elsewhere, but this
script hard-fails without it rather than silently producing nothing.
Ask for short clips per subject rather than 20-35 minute takes when the
shooter can choose. adaptive_capped tops out at 80 frames per clip, so a
35-minute take gets sampled only to about its first 16 minutes — the tail is
never seen.
When footage arrives pre-culled (the shooter has already thrown away the unusable takes), scale the effort down: fewer frames per clip, fewer sheets, and trust the selection rather than hunting for the good moments. The expensive part of shot finding is separating usable from unusable, and that work is already done. Don't re-litigate it.
Each one silently produces a wrong result rather than an error.
Every row states the build it was confirmed on. A date alone is not reproducible — the scripting API changes per patch release, so a trap confirmed on one build is only a prior on another. Re-confirm before relying on a row whose build is older than yours, and update the stamp when you do.
| Trap | Confirmed on | Symptom | Fix |
|---|---|---|---|
clip_infos end_frame is exclusive | Studio 21.0 | 1-frame gap between every clip, and matching audio gaps | end_frame = start_frame + duration |
Mixed-fps duration floor — start_frame/end_frame are SOURCE frames | Studio 21.0 (MCP v2.71.1) | Source fps ≠ timeline fps (24.0 or 29.97 source in a 23.976 timeline) → Resolve floors the source→timeline conversion, so a range planned to fill an exact record slot lands one frame short | Plan durations in timeline frames: floor(src_frames * timeline_fps / source_fps). If the floored duration misses the slot, extend end_frame by one source frame and re-check |
| An audio item's source frames are in the media's rate, and a WAV freezes the project's rate at import | Studio 21.0.3.7 (2026-08-09), corrected on 19.1.3.7 (2026-08-10) | probe_timeline_structure returned source_start: 56871 for a WAV on a 29.97 fps timeline; read at 29.97 that is 1897.6 s, but the clip counts at 24, making it 2369.6 s — out by 7m52s. 24 is not a WAV constant: a WAV takes the project's timelineFrameRate at import and keeps it, so it reads 24 only if the project was at 24 when you imported, and a WAV imported at 29.97 has no mismatch at all | Read the source_fps the probe reports beside every item and divide by that — never the timeline rate, and never a hard-coded 24. Video items are fine; they carry their own rate. Feed the media-rate frames back to create_variant_from_ranges unchanged; it converts on placement and reports it in duration_delta. Related: on an audio item GetLeftOffset counts in timeline frames while GetSourceStartFrame counts in source frames, so the probe reports source_fps: null when it had to fall back |
create_timeline_from_clips needs the current folder | Studio 21.0 | Bare Failed to create timeline from clip_infos, valid clip_ids | media_pool set_current_folder to the clips' bin first |
ImportMedia has no destination parameter | Studio 21.0 (MCP v2.71.1) | Clips land wherever the current folder happens to be; an unrecognized destination arg is silently ignored | set_current_folder before importing (step 4) |
Phone footage carries a rotation flag | Studio 21.0 | Stored 3840x2160, displays 2160x3840 | Check rotation in ffprobe; Resolve honours it. Do not reframe — it is already vertical |
| Phone footage is VFR | Studio 21.0 | avg_frame_rate differs per clip and from r_frame_rate | Match the timeline to what Resolve conforms to (its reported clip FPS), not to r_frame_rate |
timelinePlaybackFrameRate is read-only | Studio 21.0.2 | set_setting returns False for every value form, before and after a timeline exists | No API path. Ask the user to set it in Master Settings during setup (step 3), not at handover |
| An API-built Fusion comp on a media clip renders only when MediaOut has a path from MediaIn | Studio 19.1.3.7, corrected 2026-08-02 | A comp wired MediaIn → Blur → MediaOut does render. But a MediaOut fed by a tool with no source does not merely get bypassed — the render job comes back Failed. The earlier blanket claim that such comps "never render" was too broad | Wire the graph so MediaOut descends from MediaIn, and never leave a tool unrooted. For text over picture, insert a Fusion title as its own timeline clip and set its Text+ via fusion_comp set_text_plus |
The mixed-fps, audio-source-rate, ImportMedia, playback-frame-rate and Fusion
rows are recorded in the api_truth ledger and
docs/reference/api-limitations.md; query them at runtime with
resolve_control api_truth "fusion" (or "timeline", "media-pool",
"frame-rate"). The ledger is the canonical copy — this table is the
narrative version, so when the two disagree the ledger wins and this table is
the thing to correct.
⚠️ A running MCP process keeps executing the version it started with, so a
git pull does not update the ledger until it restarts. Check
resolve_control get_version → mcp.version before trusting an api_truth
miss: on MCP v2.70.4, api_truth "DeleteClips" returns zero facts that
v2.71.1 does carry.
On burning text over picture specifically: the title route above renders, but
the API cannot choose a destination track (issue #74 — Insert*IntoTimeline
takes no trackIndex and always lands on V1), so overlaying text onto an
existing clip's track is not reachable end-to-end. Treat text as a request for
the user's UI pass, not something to attempt and half-deliver.
Destructive ops auto-archive the timeline, so several edits leave
*_archived_vNN timelines behind. Clean them up before handing over.
Build clip_infos as a flat list — source start_frame/end_frame plus a
cumulative record_frame:
shots = [(clip_id, start_sec, duration_sec), ...]
infos, record = [], 0
for clip_id, start, dur in shots:
start_f, dur_f = round(start * fps), round(dur * fps)
infos.append({
"clip_id": clip_id,
"start_frame": start_f,
"end_frame": start_f + dur_f, # exclusive
"record_frame": record,
})
record += dur_fWorking in seconds and converting once keeps the shot list readable and makes
the exclusive-end_frame rule impossible to get wrong twice.
detect_gaps_overlaps alone — also confirm total duration and
inspect representative frames.Assembly references existing Media Pool items and never transcodes, proxies, or relinks source media. Analysis frames, contact sheets, and preview renders are scratch artifacts — write them to the analysis root or session scratch, never beside the source, and never into git.
© 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-rough-cut of samuelgursky/davinci-resolve-mcp.
Open the folder on GitHubat commit 94c7a88
Resolve Rough Cut 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 Rough Cut this skillsamuelgursky/davinci-resolve-mcp | 3.4k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Linkdigest Social Link Readersickn33/agentic-awesome-skills | 47k | — | ~3.4k | Automated safety check: Pass | MIT | |
| Taisly Social Media Postingtaisly/agent | 217 | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Tiktok Shop Operatoraronhy/tiktok-agent-skills | 168 | — | ~1.2k | 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 |
sickn33/agentic-awesome-skills
Read one public Xiaohongshu, Douyin, TikTok, YouTube, X or WeChat article link into text an agent can use (transcript, image text, key points) via the LinkDigest API or MCP server.
taisly/agent
Free AI-first short-form video publishing to TikTok, Instagram Reels, YouTube Shorts, X, and Facebook from AI agents through Taisly.
aronhy/tiktok-agent-skills
Operate TikTok Shop research and planning with KSS MCP across product discovery, shop analysis, viral commerce videos, creator matching, caption extraction, pagination, sorting, and evidence-based…
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.
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
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.
samuelgursky/davinci-resolve-mcp
Delivery, rendering, and deliverable QC in the DaVinci Resolve MCP.
Works with
Categories
Assembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP. Resolve Rough Cut is an agent skill from samuelgursky/davinci-resolve-mcp. Assembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP.
Resolve Rough Cut fits situations like: tasks that involve MCP servers.
Run `npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-rough-cut -a claude-code`. Or copy the skill folder (.agents/skills/resolve-rough-cut in samuelgursky/davinci-resolve-mcp) into .claude/skills/resolve-rough-cut in your project. Claude Code loads it when a task matches its description.
Run `npx skills add samuelgursky/davinci-resolve-mcp --skill resolve-rough-cut -a codex`. Or copy the skill folder (.agents/skills/resolve-rough-cut in samuelgursky/davinci-resolve-mcp) into .agents/skills/resolve-rough-cut 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-rough-cut -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-rough-cut, .gemini/skills/resolve-rough-cut, .github/skills/resolve-rough-cut and .opencode/skills/resolve-rough-cut in your project.
Going by SKILL.md and its folder, Resolve Rough Cut needs the command-line tools its instructions call (python3, pip and git). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use pip and git, which can reach the network depending on how they are called. 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 Rough Cut is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.9k tokens (SKILL.md is roughly 12k 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 Rough Cut: Linkdigest Social Link Reader (sickn33/agentic-awesome-skills, 47k stars), Taisly Social Media Posting (taisly/agent, 217 stars), Tiktok Shop Operator (aronhy/tiktok-agent-skills, 168 stars) and MCP Server Builder (anthropics/skills, 180k 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.