Orkas is a desktop application for commanding a team of AI agents through one chat, with a commander model assigning work to specialist agents in parallel or in sequence. People use it to coordinate research, writing, presentations, and software tasks while keeping files on their computer. The catalogue includes skills for extending the agents available to Orkas.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Orkas-AI/Orkas --skill stage-assemblegit clone --depth 1 https://github.com/Orkas-AI/OrkasWrote 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/orkas-ai/orkas/stage-assemble)<a href="https://agentmods.dev/skills/orkas-ai/orkas/stage-assemble"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/stage-assemble/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/orkas-ai/orkas/stage-assemble"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/stage-assemble.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 27 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 93 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
- medium Excessive Agency · line 118 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00003 | $0.05235 |
| Opus 5 | $0.00002 | $0.02618 |
| Sonnet 5 | $0.00001 | $0.01047 |
| Haiku 4.5 | $0.00000 | $0.00524 |
Grade A, and why
stage-assemble scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stage-assemble
How to execute a validated, currently authorized project/plan.json into one finished file. Start/resume with production.status; if plan_approval_current is false, pass the state to gate-control and stop instead of producing or opening child gates. Walk the signed EDL; do not re-plan. Host-neutral: VideoStudio-specific edit/plan work runs through skill scripts (stage-edit edit_video, stage-plan video_plan via bin/run-skill.cjs), while authorization state, composition, and transcription run through the required built-in video_studio runtime; generic built-in capabilities remain generate_video / generate_image / generate_speech.
Script calls used here
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-edit edit_video -- --op concat --inputs project/parts/a.mp4,project/parts/b.mp4 --output project/render/primary.mp4
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-edit edit_video -- --op mix --input project/render/primary.mp4 --audio-segments @project/audio_segments.json --output project/render/mixed.mp4
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-edit edit_video -- --op normalize_loudness --input project/render/draft.mp4 --output project/render/video.mp4
When this document says stage-edit edit_video --op ..., call the matching bin/run-skill.cjs command above with the relevant paths. For compose/transcription work, call video_studio directly. Do not call deprecated direct tools.
Step 1 — Produce each segment (delegate by source)
Iterate segments in order. For each, produce its produced_path according to source, then write that path + status:"done" back into the segment so a resume never re-produces it:
- edit →
stage-edit: cut the segment'sinput_idto its[in_sec, out_sec]window →project/cuts/<id>.mp4. Those are the EDL's field names, not flags: the command isstage-edit edit_video --op trim --input <file> --start <in_sec> --duration <out_sec - in_sec> --output project/cuts/<id>.mp4. - compose →
stage-compose: the host derives this child'scomposition-manifest.jsonfrom the signed segment'sspec.composition_planwhen the plan is inherited — do not hand-author it, and author onlyindex.html. Keep it silent: no tracks and nonarration_intent(the voice is signed once on the parent EDL). Setaudio.owner:"assembler"whenever any scene carriesnarration_text— it renders silent exactly like"none"while naming the real owner, so the host stops asking this segment for its own narration audio. Use"none"only for a segment with no narration at all. Askgate-controlto resolve parent Gate B inheritance for the owning plan/segment, then follow the returned doctor/prepare path without creating a child user gate. Continue with visual authoring, native-required inspect/snapshot, and draft toproject/parts/<id>.mp4only while the binding stays current. Run the QA phases for compose segments as ONE batched call per phase across the production rather than one call per segment: the batched path defaults its scope to the segments with no current frames, returns full findings only for the failures, and returns the production state once instead of repeating it per segment. Use a single-composition QA call only to re-check one named segment. Onlycomposesegments need snapshot evidence, because their artifact is HTML; anedit/generate/providedsegment is captured by its ownproduced_pathfile the moment that file exists, so never run a QA phase on one and never treat it as unfinished work. When every segment is captured (production.statusreports nouncaptured_segment_ids), stop at the keyframe preview. The batched snapshot phase returnsproduction_contact_sheet: ONE image of the whole video, segments in playback order, media segments included as an extracted still. Lead the stop with it — it is the artifact the user judges the video from. Then list each segment's own locators in playback order, one row per segment including the media-backed ones (a cut or generated shot has no snapshot — carry itsproduced_pathso the user can play it), one line inviting changes, and<plan-interaction status="open" />— no form, no per-segment approvals. Never present a per-segment contact sheet as the production preview: four links to four children is not a look at the video. Assembly starts only from the user's reply; a reply that names no change is the go-ahead, while a named change is applied without asking the user to approve their own instruction. Re-capture the resulting complete production preview and return it in the next preview message — the message that ENDS the turn. A completed turn keeps only your final message, so frames posted mid-turn were never seen by the user. Put the updated contact sheet and locators in the turn's last message itself. The stop happens once per complete production visual identity: narration/audio-only work inherits it when every visible segment stays byte-identical; changing any composed frame or media-backed segment creates a new aggregate identity and the changed whole-video preview stops once before assembly. There are still no per-segment approvals. - generate →
stage-generate(+stage-consistencyfor recurring characters): proceed only whileproduction.statusreports the current paid-generation signature. Callgenerate_videoorgenerate_imagewithproduction_plan_path:"project/plan.json"andproduction_segment_id:<id>→project/assets/<id>.<ext>. When the segment hasoperation:"edit", treat it as the bounded semantic-edit executor for the EDIT/AUTO workflow: pass the exact original reference video and obey top-levelreferences+edit_strategy; never widen it into regeneration. Every auxiliary portrait/keyframe is already its own signed generate segment; do not create unplanned billable calls. The host transaction reuses a completed artifact and blocks an interrupted/failed duplicate. Pass pending/failed state togate-control; never automatically retry or invent a recovery API, and use a new output path for any later authorized retry. - provided → use
spec.asset_idas-is (probe it first; conform aspect/fps if needed).
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 First seen · 131 lines · 3 tokens per session scan A 8853f4c14c9c
stage-assemble is a skill published in the GitHub repository Orkas-AI/Orkas (1,848 stars, last pushed yesterday), licensed MIT. It adds 3 tokens to every session and 5,235 once invoked, about $0.0000 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.
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Suggests editing structure, scene cuts, transitions, and pacing for improved video content quality and engagement.
caption-subtitle-formatter
Formats captions and subtitles for readability, timing, and accessibility across videos.
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Use DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a…