Agent skill

Darlink AI

by LeoYeAI in LeoYeAI/openclaw-master-skills

Darlink-ai brings a new dimension to video editing by letting you direct changes through natural conversation rather than complex timelines.

MITAuto-check passedMedia & Creative

Install Darlink AI

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill darlink-ai -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills darlink-ai --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/darlink-ai .claude/skills/darlink-ai && rm -rf skills-src

Use ~/.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/

Facts

Skill name
darlink-ai
GitHub stars
2.2k
Token cost
~4.4k tokens
SKILL.md length
1,900 words
Files
2
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Darlink-ai brings a new dimension to video editing by letting you direct changes through natural conversation rather than complex timelines.

  • Works in 8 steps: First Contact → Routing Incoming Requests to the Correct… → Primary Workflow Sequences → …
  • Tasks that involve Video production
  • SKILL.md covers 0. First Contact, 2. Routing Incoming Requests…, 3. Primary Workflow Sequences and 4. Translating Backend…, plus 4 more sections
  • Calls curl; reaches mega-api-prod.nemovideo.ai and nemovideo.com; needs NEMO_TOKEN

What it does

Darlink AI is an agent skill from LeoYeAI/openclaw-master-skills. Darlink-ai brings a new dimension to video editing by letting you direct changes through natural conversation rather than complex timelines. Trim dead air, reorder scenes, adjust pacing, apply color grading, and stitch segments together — all by describing what you want. Built for content creators, indie filmmakers, and social media teams who need fast turnaround without steep learning curves. Darlink-ai understands context across your edits, so follow-up instructions refine rather than restart. Supports mp4…

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

It sits in Media & Creative, covering Video production and Blog and article writing. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve Video production
  • Tasks that involve Blog and article writing

Example prompts

  • “/darlink-ai”

Requirements

  • A credential in NEMO_TOKEN

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. First Contact
  2. Routing Incoming Requests to the Correct Endpoint
  3. Primary Workflow Sequences
  4. Translating Backend Behavior into GUI Concepts
  5. Recommended Conversational Interaction Patterns
  6. Known Constraints and Limitations
  7. Error Response Handling
  8. API Version and Required Token Scopes

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • mega-api-prod.nemovideo.ai
    • nemovideo.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NEMO_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Darlink AI loads about 4.4k tokens when it runs. Until then it costs about 140 tokens; SKILL.md has 1,900 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~140
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,900 words, ~4,422 tokens.

Download SKILL.mdSave it as .claude/skills/darlink-ai/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
darlink-ai
description
Darlink-ai brings a new dimension to video editing by letting you direct changes through natural conversation rather than complex timelines. Trim dead air, reorder scenes, adjust pacing, apply color grading, and stitch segments together — all by describing what you want. Built for content creators, indie filmmakers, and social media teams who need fast turnaround without steep learning curves. Darlink-ai understands context across your edits, so follow-up instructions refine rather than restart. Supports mp4, mov, avi, webm, and mkv formats.
version
1.0.4
displayName
Darlink AI — Smart Video Editing Powered by Conversational AI
homepage
https://nemovideo.com
apiDomain
https://mega-api-prod.nemovideo.ai
repository
https://github.com/nemovideo/nemovideo_skills

0. First Contact

When the user opens this skill or sends their first message, greet them immediately:

⚡ Darlink Ai at your service! Upload a video or tell me what you're looking for.

Try saying:

  • "add effects to this clip"
  • "edit my video"
  • "help me create a short video"

IMPORTANT: Always greet the user proactively on first contact. Let them know you're setting up while connecting. Always greet the user proactively on first contact.

Auto-Setup

When the user first interacts, set up the connection:

  1. Check token: If NEMO_TOKEN env var is set, use it. Otherwise:
  2. Read or generate Client-ID:
    • Read ~/.config/nemovideo/client_id if it exists
    • Otherwise generate a UUID, save it to ~/.config/nemovideo/client_id
  3. Acquire anonymous token:
    bash
    curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token" -H "X-Client-Id: $CLIENT_ID"
    Store the returned token as NEMO_TOKEN for this session. You get 100 free credits.
  4. Create a session (§3.0) so you're ready to work immediately.

Let the user know briefly: "Setting things up… ready!" then proceed with their request.

Darlink-ai reimagines the editing workflow by replacing manual timeline scrubbing with a back-and-forth dialogue. Instead of hunting for the right cut point or wrestling with keyframe curves, you describe the outcome you want — 'tighten the opening to under 10 seconds' or 'make the color warmer in the second half' — and the skill interprets that intent into precise, frame-accurate operations on your footage.

Under the hood, the OpenClaw agent acts as the reasoning layer between your instructions and the video processing pipeline. It parses conversational input, resolves ambiguities by asking clarifying questions when needed, and dispatches the appropriate editing operations in sequence. The agent maintains memory of prior instructions within a session, so each new request builds on what came before rather than treating the project as a blank slate.

This makes darlink-ai particularly well-suited for iterative creative work — the kind where you need to see a rough cut, react to it, and refine in real time. Whether you are assembling a short-form reel, cutting a product demo, or trimming a long interview down to its sharpest moments, the conversational model keeps you in a creative flow state rather than a technical one.

Environment Variables
VariableRequiredDefault
NEMO_TOKENNoAuto-generated (100 free credits, expires in 7 days, revocable via Settings → API Tokens)
NEMO_API_URLNohttps://mega-api-prod.nemovideo.ai
NEMO_WEB_URLNohttps://nemovideo.com
NEMO_CLIENT_IDNoAuto-generated UUID, persisted to ~/.config/nemovideo/client_id (UUID only, no secrets)
SKILL_SOURCENoAuto-detected from install path, fallback unknown

If NEMO_TOKEN is not set, get one (requires X-Client-Id header):

bash
# Generate or read persisted Client-Id
CLIENT_ID="${NEMO_CLIENT_ID:-$(cat ~/.config/nemovideo/client_id 2>/dev/null)}"
if [ -z "$CLIENT_ID" ]; then
  CLIENT_ID=$(uuidgen 2>/dev/null || echo "client-$(date +%s)-$RANDOM")
  mkdir -p ~/.config/nemovideo & echo "$CLIENT_ID" > ~/.config/nemovideo/client_id
fi
curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token" -H "X-Client-Id: $CLIENT_ID"
# → {"code":0,"data":{"token":"nmv_usr_xxx","credits":100,...}}

Save token as NEMO_TOKEN, CLIENT_ID as NEMO_CLIENT_ID. Anonymous: 1 token per client per 7 days; token expires in 7 days and can be revoked at any time via Settings → API Tokens on nemovideo.com. If your token expires, request a new one with the same X-Client-Id.

Local persistence: This skill writes ~/.config/nemovideo/client_id to persist the Client-Id across sessions. This avoids generating a new ID on every request, which would hit the per-IP rate limit quickly (default 10 tokens per 7 days per IP). The file contains only a UUID — no credentials are stored locally.

2. Routing Incoming Requests to the Correct Endpoint

Use the table below to determine which API endpoint should handle each type of user request.

User says...ActionSkip SSE
"export" / "导出" / "download" / "send me the video"→ §3.5 Export✅
"credits" / "积分" / "balance" / "余额"→ §3.3 Credits✅
"status" / "状态" / "show tracks"→ §3.4 State✅
"upload" / "上传" / user sends file→ §3.2 Upload✅
Everything else (generate, edit, add BGM…)→ §3.1 SSE❌

3. Primary Workflow Sequences

$API = ${NEMO_API_URL:-https://mega-api-prod.nemovideo.ai}, $TOKEN = ${NEMO_TOKEN}, $WEB = ${NEMO_WEB_URL:-https://nemovideo.com}.

Skill attribution variables — do NOT hardcode:

  • $SKILL_NAME = this file's YAML frontmatter name
  • $SKILL_VERSION = this file's YAML frontmatter version
  • $SKILL_SOURCE = detect at runtime using the first match:
    1. Env SKILL_SOURCE if set
    2. If this file's path contains a known platform directory (e.g. ~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor)
    3. Fallback: unknown

CRITICAL: ALL API requests (including render/export/upload/state/credits) MUST include these headers. Missing them will cause export to fail with 402.

X-Skill-Source: $SKILL_NAME
X-Skill-Version: $SKILL_VERSION
X-Skill-Platform: $SKILL_SOURCE
3.0 Initializing a New Session
bash
curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE" \
  -d '{"task_name":"project","language":"<lang>"}'
# → {"code":0,"data":{"task_id":"...","session_id":"..."}}

Before any editing can begin, a session must be established to maintain conversation context. All subsequent requests within the same editing task should reference this session identifier.

Open in browser: After creating a session, give the user a link to view/edit the task in NemoVideo: $WEB/workspace/claim?task={task_id}&session={session_id}&skill_name=$SKILL_NAME&skill_version=$SKILL_VERSION&skill_source=$SKILL_SOURCE

3.1 Transmitting Messages Over SSE
bash
curl -s -X POST "https://mega-api-prod.nemovideo.ai/run_sse" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE" --max-time 900 \
  -d '{"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}}'

All conversational messages to the editing engine are delivered through a Server-Sent Events stream that returns real-time responses.

SSE Handling
EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultWait silently, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess final response

Typical durations: text 5-15s, video generation 100-300s, editing 10-30s.

Timeout: 10 min heartbeats-only → assume timeout. Never re-send during generation (duplicates + double-charge).

Ignore trailing "I encountered a temporary issue" if prior responses were normal.

Silent Response Fallback (CRITICAL)

Approximately 30% of editing operations complete without returning any text in the SSE stream. When no text payload is received within the expected window, do not treat this as an error — instead, immediately poll the task state endpoint to check for a completed result, surface a neutral status message to the user such as 'Your edit is being processed,' and proceed once the state endpoint confirms completion.

Two-stage generation: After the raw edited video is produced, the backend automatically initiates a second processing stage that layers in background music and generates a title overlay — no additional prompt is required. The first stage delivers the cut video, and the second stage enriches it; both must complete before presenting the final result to the user.

3.2 Handling File Uploads

File upload: curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/upload-video/nemo_agent/me/<sid>" -H "Authorization: Bearer $TOKEN" -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE" -F "files=@/path/to/file"

URL upload: curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/upload-video/nemo_agent/me/<sid>" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE" -d '{"urls":["<url>"],"source_type":"url"}'

Use me in the path; backend resolves user from token.

Supported: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Both video and audio file uploads are supported through the designated upload endpoint prior to referencing them in any editing request.

3.3 Checking Available Credits
bash
curl -s "https://mega-api-prod.nemovideo.ai/api/credits/balance/simple" -H "Authorization: Bearer $TOKEN" \
  -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE"
# → {"code":0,"data":{"available":XXX,"frozen":XX,"total":XXX}}

Query the credits endpoint before submitting any edit operation to confirm the user has a sufficient balance to proceed.

3.4 Retrieving Current Task State
bash
curl -s "https://mega-api-prod.nemovideo.ai/api/state/nemo_agent/me/<sid>/latest" -H "Authorization: Bearer $TOKEN" \
  -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE"

Use me for user in path; backend resolves from token. Key fields: data.state.draft, data.state.video_infos, data.state.canvas_config, data.state.generated_media.

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Draft ready for export when draft.t exists with at least one track with non-empty sg.

Track summary format:

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)
Show full SKILL.md (816 more words)Show less
3.5 Triggering Export and Delivering the Result

Export does NOT cost credits. Only generation/editing consumes credits.

Exporting a finished project does not deduct any credits from the user's balance. To deliver the final video: (a) confirm the task state shows completion, (b) call the export endpoint with the session and task identifiers, (c) poll until the export status is ready, (d) retrieve the download URL from the response, and (e) present the URL to the user as the deliverable.

b) Submit: curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/render/proxy/lambda" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE" -d '{"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}'

Note: sessionId is camelCase (exception). On failure → new id, retry once.

c) Poll (every 30s, max 10 polls): curl -s "https://mega-api-prod.nemovideo.ai/api/render/proxy/lambda/<id>" -H "Authorization: Bearer $TOKEN" -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKILL_VERSION" -H "X-Skill-Platform: $SKILL_SOURCE"

Status at top-level status: pending → processing → completed / failed. Download URL at output.url.

d) Download from output.url → send to user. Fallback: https://mega-api-prod.nemovideo.ai/api/render/proxy/<id>/download.

e) When delivering the video, always also give the task detail link: $WEB/workspace/claim?task={task_id}&session={session_id}&skill_name=$SKILL_NAME&skill_version=$SKILL_VERSION&skill_source=$SKILL_SOURCE

Progress messages: start "⏳ Rendering ~30s" → "⏳ 50%" → "✅ Video ready!" + file + task detail link.

3.6 Recovering from an SSE Disconnection

When an SSE stream drops unexpectedly, follow these five steps to recover gracefully: (1) detect the disconnection event and log the last received event ID; (2) wait a brief back-off interval before attempting to reconnect; (3) re-establish the SSE connection using the same session identifier and passing the last event ID in the reconnect header; (4) if the stream does not resume within the retry window, fall back to polling the task state endpoint directly; (5) once task completion is confirmed through either method, continue the normal delivery flow as if no interruption occurred.

4. Translating Backend Behavior into GUI Concepts

The backend is designed with the assumption that a graphical interface is present, so under no circumstances should raw GUI-layer instructions or interface directives be forwarded directly to the user.

Backend saysYou do
"click [button]" / "点击"Execute via API
"open [panel]" / "打开"Show state via §3.4
"drag/drop" / "拖拽"Send edit via SSE
"preview in timeline"Show track summary
"Export button" / "导出"Execute §3.5
"check account/billing"Check §3.3

Keep content descriptions. Strip GUI actions.

• Confirm the user's intent before initiating any operation that will consume credits, giving them a clear opportunity to cancel. • After submitting an edit request, proactively communicate processing status at regular intervals so the user is never left wondering if something went wrong. • When an edit completes silently with no text response, bridge the gap with a brief neutral status update rather than leaving the conversation idle. • If the user's request is ambiguous about timing, style, or scope, ask one focused clarifying question before proceeding rather than making assumptions. • Always present the export download URL as the final step, framed as the deliverable, so the user knows the task is fully complete.

6. Known Constraints and Limitations

• The conversational AI cannot preview frames or inspect raw video content directly — it relies entirely on metadata and task state responses. • Session identifiers are not permanent; do not assume a session remains valid across separate user conversations or after extended inactivity. • Credit balances are read-only from the API perspective — the skill cannot add, refund, or adjust credits under any circumstances. • Only the file formats and MIME types explicitly accepted by the upload endpoint are supported; attempting to upload unsupported formats will result in a rejection error. • The two-stage post-processing pipeline for BGM and title overlays runs automatically and cannot be skipped or reordered by the skill.

7. Error Response Handling

The table below maps each HTTP error code returned by the API to its likely cause and the recommended recovery action.

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up at nemovideo.ai"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register at nemovideo.ai to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Common: no video → generate first; render fail → retry new id; SSE timeout → §3.6; silent edit → §3.1 fallback.

8. API Version and Required Token Scopes

Always verify the API version header in every response to ensure compatibility with the expected contract; if the version does not match the supported range, halt and surface a compatibility warning rather than proceeding. The access token must include all required scopes for session management, file upload, task polling, and export operations — requests made with a token missing any of these scopes will be rejected with a 403 response and must not be retried until a properly scoped token is obtained.

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/darlink-ai of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills8.5k6 repos~3.2kAutomated safety check: NotesApache-2.0
Faceless Explainer Videoheygen-com/hyperframes60k3 repos~7.7kAutomated safety check: NotesApache-2.0
Video Understandcalesthio/OpenMontage66k—~841Automated safety check: PassAGPL-3.0

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Questions about Darlink AI

What does Darlink AI do?

Darlink-ai brings a new dimension to video editing by letting you direct changes through natural conversation rather than complex timelines. Darlink AI is an agent skill from LeoYeAI/openclaw-master-skills. Darlink-ai brings a new dimension to video editing by letting you direct changes through natural conversation rather than complex timelines.

When should I use Darlink AI?

Darlink AI fits situations like: tasks that involve Video production; tasks that involve Blog and article writing.

How do I install Darlink AI in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill darlink-ai -a claude-code`. Or copy the skill folder (skills/darlink-ai in LeoYeAI/openclaw-master-skills) into .claude/skills/darlink-ai in your project. Claude Code loads it when a task matches its description.

How do I install Darlink AI in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill darlink-ai -a codex`. Or copy the skill folder (skills/darlink-ai in LeoYeAI/openclaw-master-skills) into .agents/skills/darlink-ai in your project. Codex loads it when a task matches its description.

Can I use Darlink AI in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill darlink-ai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/darlink-ai, .gemini/skills/darlink-ai, .github/skills/darlink-ai and .opencode/skills/darlink-ai in your project.

What does Darlink AI need to run?

Going by SKILL.md and its folder, Darlink AI needs the command-line tools its instructions call (curl) and credentials named NEMO_TOKEN. Our summary lists: A credential in NEMO_TOKEN.

Does Darlink AI access the network?

SKILL.md names 2 domains. In commands or code: mega-api-prod.nemovideo.ai and nemovideo.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Darlink AI safe to install?

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.

What licence does Darlink AI use?

Darlink AI is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Darlink AI use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Darlink AI?

Skills that share tags, products or a category with Darlink AI: Talking Head Video (gooseworks-ai/goose-skills, 1.2k stars), HyperFrames Animation (heygen-com/hyperframes, 60k stars), Stitch to Remotion Walkthrough Videos (google-labs-code/stitch-skills, 8.5k stars) and Faceless Explainer Video (heygen-com/hyperframes, 60k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Darlink AI?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.