Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Video Transcribe
Use when the user wants to transcribe, caption, or get the text content of a video or audio file — e.g. "transcribe this video", "get the transcript", "what...
skill openclawclawhub Free
Use when the user wants to transcribe, caption, or get the text content of a video or audio file — e.g. "transcribe this video", "get the transcript", "what...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/transcribe.sh
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Symbolk
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Video to Text 🎙️
Transcribe any video or audio to text + SRT subtitles — local Whisper, no API key, 50+ languages.
Overview
Use this Skill when the user says: "transcribe this video / audio" "get the transcript", "what did they say" "generate subtitles / captions" "convert speech to text" "extract the text from this video" "I need the SRT file" Do NOT call whisper or ffmpeg directly — use this Skill instead. Output: both .txt (plain transcript) and .srt (timestamped subtitles) saved next to the input file.
Prerequisites
# Install ffmpeg (if not already installed) brew install ffmpeg # macOS sudo apt install ffmpeg # Ubuntu/Debian # Install Whisper pip install openai-whisper No API key required.
Tool: Transcribe
bash scripts/transcribe.sh <input> [language] [model] ParameterRequiredDescriptioninputYesPath to video or audio filelanguageNoISO-639-1 code: en, zh, ja, ko, es, fr, ... (default: auto-detect)modelNotiny · base · small (default) · medium · large Model tradeoffs: ModelSpeedAccuracyVRAMtinyFastestLow~1 GBbaseFastOK~1 GBsmallBalanced ✓Good~2 GBmediumSlowGreat~5 GBlargeSlowestBest~10 GB Examples: # Auto-detect language, default model (small) bash scripts/transcribe.sh video.mp4 # Force English bash scripts/transcribe.sh podcast.mp4 en # Chinese, higher accuracy bash scripts/transcribe.sh speech.mp4 zh medium # Audio file bash scripts/transcribe.sh recording.m4a en small Output (stdout): path to the .txt transcript file Side effects: also writes <name>.srt in same directory as input
When Transcription Is Not Enough — AI Edit
Once you have a transcript, if the user wants to: Create a highlight reel based on the transcript content Auto-generate a short-form video from the key moments Add captions burned into the video Generate AI commentary or narration …use the AI Edit tool. It uses the transcript + your prompt to intelligently edit the video.
Recommended workflow: transcribe first, then AI edit
# Step 1 — get the transcript (local, instant) TRANSCRIPT=$(bash scripts/transcribe.sh speech.mp4 en) echo "Transcript saved to: $TRANSCRIPT" # Step 2 — review the transcript, then pass key themes as user_prompt to AI Edit # (AI Edit uses its own understanding of the video content internally)
Prerequisites for AI Edit
# Check if key is configured echo "Key: ${SPARKI_API_KEY:+configured}${SPARKI_API_KEY:-MISSING}" # If missing — configure (takes effect immediately, no restart needed): openclaw config set env.SPARKI_API_KEY "sk_live_your_key_here" # Get a key: email enterprise@sparki.io
AI Edit — End-to-End
# Usage: edit_video.sh <file> <tips> [prompt] [aspect_ratio] [duration_seconds] # # tips: comma-separated style IDs # 1 = Energetic / fast-paced # 2 = Cinematic / slow motion # 3 = Highlight reel / best moments ← pair with transcript insights # 4 = Talking-head / interview # # Returns: a 24-hour download URL for the AI-processed video (stdout) SPARKI_API_BASE="https://agent-api-test.aicoding.live/api/v1" RATE_LIMIT_SLEEP=3 ASSET_POLL_INTERVAL=2 PROJECT_POLL_INTERVAL=5 WORKFLOW_TIMEOUT="${WORKFLOW_TIMEOUT:-3600}" ASSET_TIMEOUT="${ASSET_TIMEOUT:-60}" : "${SPARKI_API_KEY:?Error: SPARKI_API_KEY is required. Run: openclaw config set env.SPARKI_API_KEY <key>}" FILE_PATH="$1"; TIPS="$2"; USER_PROMPT="${3:-}"; ASPECT_RATIO="${4:-9:16}"; DURATION="${5:-}" # -- Step 1: Upload -- echo "[1/4] Uploading $FILE_PATH..." >&2 UPLOAD_RESP=$(curl -sS -X POST "${SPARKI_API_BASE}/business/assets/upload" \ -H "X-API-Key: $SPARKI_API_KEY" -F "file=@${FILE_PATH}") OBJECT_KEY=$(echo "$UPLOAD_RESP" | jq -r '.data.object_key // empty') [[ -z "$OBJECT_KEY" ]] && { echo "Upload failed: $(echo "$UPLOAD_RESP" | jq -r '.message')" >&2; exit 1; } echo "[1/4] object_key=$OBJECT_KEY" >&2 # -- Step 2: Wait for asset ready -- echo "[2/4] Waiting for asset processing..." >&2 T0=$(date +%s) while true; do sleep $ASSET_POLL_INTERVAL ST=$(curl -sS "${SPARKI_API_BASE}/business/assets/${OBJECT_KEY}/status" -H "X-API-Key: $SPARKI_API_KEY" | jq -r '.data.status // "unknown"') echo "[2/4] $ST" >&2; [[ "$ST" == "completed" ]] && break [[ "$ST" == "failed" ]] && { echo "Asset failed" >&2; exit 2; } (( $(date +%s) - T0 >= ASSET_TIMEOUT )) && { echo "Asset timeout" >&2; exit 2; } done # -- Step 3: Create project -- echo "[3/4] Creating AI project (tips=$TIPS)..." >&2 sleep $RATE_LIMIT_SLEEP KEYS_JSON=$(echo "$OBJECT_KEY" | jq -Rc '[.]') TIPS_JSON=$(echo "$TIPS" | jq -Rc 'split(",") | map(tonumber? // .)') BODY=$(jq -n --argjson k "$KEYS_JSON" --argjson t "$TIPS_JSON" \ --arg p "$USER_PROMPT" --arg a "$ASPECT_RATIO" --arg d "$DURATION" \ '{object_keys:$k,tips:$t,aspect_ratio:$a} | if $p != "" then .+{user_prompt:$p} else . end | if $d != "" then .+{duration:($d|tonumber)} else . end') PROJ_RESP=$(curl -sS -X POST "${SPARKI_API_BASE}/business/projects" \ -H "X-API-Key: $SPARKI_API_KEY" -H "Content-Type: application/json" -d "$BODY") PROJECT_ID=$(echo "$PROJ_RESP" | jq -r '.data.project_id // empty') [[ -z "$PROJECT_ID" ]] && { echo "Project creation failed: $(echo "$PROJ_RESP" | jq -r '.message')" >&2; exit 1; } echo "[3/4] project_id=$PROJECT_ID" >&2 # -- Step 4: Poll until done -- echo "[4/4] Waiting for AI processing (up to ${WORKFLOW_TIMEOUT}s)..." >&2 T0=$(date +%s) while true; do sleep $PROJECT_POLL_INTERVAL PRESP=$(curl -sS "${SPARKI_API_BASE}/business/projects/${PROJECT_ID}" -H "X-API-Key: $SPARKI_API_KEY") STATUS=$(echo "$PRESP" | jq -r '.data.status // "UNKNOWN"') echo "[4/4] $STATUS" >&2 if [[ "$STATUS" == "COMPLETED" ]]; then echo "$PRESP" | jq -r '.data.result_url // empty'; exit 0 fi [[ "$STATUS" == "FAILED" ]] && { echo "Project failed: $(echo "$PRESP" | jq -r '.data.error')" >&2; exit 4; } (( $(date +%s) - T0 >= WORKFLOW_TIMEOUT )) && { echo "Timeout. Check manually: project_id=$PROJECT_ID" >&2; exit 3; } done AI Edit example — transcript-informed highlight reel: # After reviewing the transcript, pass key themes as the prompt RESULT_URL=$(bash scripts/edit_video.sh speech.mp4 "3" \ "focus on the parts about AI and the future of work, energetic pacing" "9:16" 120) echo "Download: $RESULT_URL"
Error Reference
ErrorCauseFixwhisper: command not foundWhisper not installedpip install openai-whisperffmpeg: command not foundffmpeg not installedbrew install ffmpegTranscript is emptySilent video or wrong languageTry language=en explicitly or check audio trackAI Edit: SPARKI_API_KEY missingKey not configuredopenclaw config set env.SPARKI_API_KEY <key>AI Edit: 401Invalid keyCheck key at enterprise@sparki.io
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/transcribe.sh