Agent skill

Stage Decide

by Orkas-AI in Orkas-AI/Orkas-VideoStudio

The decision layer for real footage — understand → select → produce an EVIDENCE-bearing rough cut.

MITAuto-check passedMedia & Creative

Install Stage Decide

skills CLI
$ npx skills add Orkas-AI/Orkas-VideoStudio --skill stage-decide -a claude-code

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

GitHub CLI
$ gh skill install Orkas-AI/Orkas-VideoStudio stage-decide --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/Orkas-AI/Orkas-VideoStudio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/stage-decide .claude/skills/stage-decide && 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
stage-decide
GitHub stars
499
Token cost
~1.4k tokens
SKILL.md length
730 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

The decision layer for real footage — understand → select → produce an EVIDENCE-bearing rough cut.

  • Works in 5 steps: Understand the material first (never… → Decide — deterministic first, judgment… → Record strategy and references. Write… → …
  • The EDIT task is find / select / reduce / clean (remove dead air
  • SKILL.md covers Use this when, Method and Honest ceiling — present a…
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Stage Decide is an agent skill from Orkas-AI/Orkas-VideoStudio. The decision layer for real footage — understand → select → produce an EVIDENCE-bearing rough cut. Trigger when the EDIT task is "find / select / reduce / clean" (remove dead air, drop fillers, pick highlights, cut 1 hour to 3 minutes), NOT executing a known timecode edit (that is stage-edit). Deterministic auto-cuts (silence/filler/quality) are reliable; narrative/emotional selection is a low-confidence DRAFT for the user to review.

Its SKILL.md is about 1.4k 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 Media & Creative, covering AI video generation. The repository describes itself as: Turn your coding agent into a video studio: describe a video in plain language, and your agent writes the timeline and produces the file. The licence is MIT.

When your agent uses it

  • The EDIT task is find / select / reduce / clean (remove dead air
  • Pick highlights
  • Cut 1 hour to 3 minutes)
  • NOT executing a known timecode edit (that is stage-edit)

Example prompts

  • “find / select / reduce / clean”
  • “/stage-decide”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Understand the material first (never decide against footage you have not measured)
  2. Decide — deterministic first, judgment second
  3. Record strategy and references. Write plan.json#edit_strategy with deterministic/mixed mode, concrete objectives, and only evidence…
  4. Record evidence — make every cut auditable. For each kept/cut segment in plan.json, set
  5. Produce the tightened clip (the auto-cut tools output it directly; for selection, trim the kept

What it can do on your machine

Read from SKILL.md and the folder at commit c0c3c3e. 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

    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.

  • Network

    No URLs in SKILL.md.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Stage Decide loads about 1.4k tokens when it runs. Until then it costs about 113 tokens; SKILL.md has 730 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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 Orkas-AI/Orkas-VideoStudio at commit c0c3c3e, republished under its MIT licence (© Orkas-AI). 730 words, ~1,416 tokens.

Download SKILL.mdSave it as .claude/skills/stage-decide/SKILL.md (or your agent's skills folder).
name
stage-decide
description
The decision layer for real footage — understand → select → produce an EVIDENCE-bearing rough cut. Trigger when the EDIT task is "find / select / reduce / clean" (remove dead air, drop fillers, pick highlights, cut 1 hour to 3 minutes), NOT executing a known timecode edit (that is stage-edit). Deterministic auto-cuts (silence/filler/quality) are reliable; narrative/emotional selection is a low-confidence DRAFT for the user to review.

stage-decide

The hard, valuable part of editing real footage is not executing a cut you already chose — it is figuring out WHAT to cut: understanding opaque raw material, removing its intrinsic defects (dead air, fillers, weak takes), and reducing it without losing the point. This skill is the "understand → decide" layer; stage-edit executes the cuts you land on.

Describe what to produce; the operations run through the CLI (or the equivalent MCP tool): ovs edit trim-silence / ovs edit remove-fillers (deterministic auto-cuts that return evidence), ovs scenes (cut candidates), ovs quality (blur/exposure/black/freeze flags), ovs transcribe --out (word timings saved as JSON), ovs silence.

Use this when

The user supplies real footage AND the work is to select or clean, not to run a known edit: "cut this 40-min recording to a 2-min highlight", "remove the ums and dead air", "make 3 clips from this podcast", "tighten this talking-head". If they already gave you timecodes ("trim 0:10–0:35"), skip this — that is plain stage-edit.

Method

  1. Understand the material first (never decide against footage you have not measured):
    • ovs edit probe for duration/resolution.
    • Spoken footage → ovs transcribe raw/clip.mp4 --out project/transcripts/clip.json (word-level timings) so you cut on sentence/word boundaries, never mid-word.
    • Visual reduction → ovs scenes for shot boundaries; bound the moments you keep on these candidates.
    • Dead air → ovs silence to see the gaps.
  2. Decide — deterministic first, judgment second:
    • Cleaning is mechanical — use the auto-cuts: ovs edit trim-silence (drop dead air), ovs edit remove-fillers (transcribe → drop um/uh). They are reliable and return the spans they removed.
    • Build a candidate pool first — turn the signals into a structured list of selectable pieces: each transcript sentence (spoken footage) or scene segment (visual footage), annotated with its timecode, duration, and quality flags/score. Select FROM this list — do not eyeball raw footage.
    • Selection is judgment — when picking highlights / reducing length, ground EACH kept span on a measured signal (a scene boundary, a transcript sentence, a scored moment). Keep whole sentences; pad cuts so they are not jarring; for a talking-head the jump-cut keeps audio and video in sync — do not desync the lips.
    • Best take among repeats — when the same line was recorded several times, do NOT guess: write a takes.json ([{id, text=the take's transcript, quality_score from ovs quality, duration_sec}]) and run ovs plan rank-takes takes.json. It groups the repeats and tells you which to KEEP (best quality) and which to drop. Choosing what to keep across DIFFERENT moments is still your judgment; this only resolves "which of these identical takes".
    • Quality triage — ovs quality flags bad shots (blurry / too dark / over-exposed / black / frozen). Drop or avoid flagged spans; blur is content-relative (compare, do not threshold blindly), dark / black / freeze are absolute defects.
    • Visual / silent footage (no speech) — the content is in the PICTURE, so transcript is empty. Sample frames at candidate moments with ovs edit extract-frame and JUDGE THEM YOURSELF if you can see images (you are the vision — no separate vision model). If you CANNOT see images, ground on ovs scenes + ovs quality only and mark every visual judgment UNVERIFIED, or ask the user which moments matter — NEVER invent what is on screen, and never escalate to a separate vision model.
  3. Record strategy and references. Write plan.json#edit_strategy with deterministic/mixed mode, concrete objectives, and only evidence signals actually used. New work uses transcript/scene/silence/quality/vision signals; ocr remains readable only in historical plans. Keep preserve/may-change boundaries non-overlapping. Record every source or guiding image/video in top-level references; video timing/motion guidance needs temporal anchors.
  4. Record evidence — make every cut auditable. For each kept/cut segment in plan.json, set reason (why this moment), confidence, and evidence (the auto-cut tools return removed/kept spans; for your own selections, cite the signal). This is the whole point — not a black box.
  5. Produce the tightened clip (the auto-cut tools output it directly; for selection, trim the kept spans and concat per stage-edit).
Show full SKILL.md (91 more words)Show less

Honest ceiling — present a DRAFT, let the user decide

  • High confidence (ship it): silence/filler removal, transcript-driven sentence selection, quality filtering. These are deterministic and proven.
  • Low confidence (mark it, never claim it is "right"): narrative arc, emotional beats, comedic timing, "does this cut FEEL right". These are subjective with no ground truth. Offer the rough cut as a first pass, flag the low-confidence calls, and invite the user to adjust at the draft gate.

Never over-claim. An evidence-backed rough cut the user can audit and tweak beats a confident black box.

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

Files

Just SKILL.md in packages/skills/stage-decide of Orkas-AI/Orkas-VideoStudio.

Open the folder on GitHubat commit c0c3c3e

Compare with similar skills

Stage Decide 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.

Stage Decide compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Stage Decide this skillOrkas-AI/Orkas-VideoStudio499—~1.4kAutomated safety check: PassMIT
Video Generationbytedance/deer-flow84k3 repos~1.4kAutomated safety check: PassMIT
Video Cover Imageitwanger/toBeBetterJavaer18k—~3.3kAutomated safety check: PassNone
Seedancesongguoxs/seedance-prompt-skill2.9k1 repos~2.5kAutomated safety check: PassNone
HyperFrames Video Entry Pointheygen-com/hyperframes60k3 repos~5.2kAutomated safety check: PassApache-2.0
Lanshu Create AI Presenter Videocclank/lanshu-create-ai-presenter-video2.6k—~3.6kAutomated safety check: PassMIT

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Questions about Stage Decide

What does Stage Decide do?

The decision layer for real footage — understand → select → produce an EVIDENCE-bearing rough cut. Stage Decide is an agent skill from Orkas-AI/Orkas-VideoStudio. The decision layer for real footage — understand → select → produce an EVIDENCE-bearing rough cut.

When should I use Stage Decide?

Stage Decide fits situations like: the EDIT task is find / select / reduce / clean (remove dead air; pick highlights; cut 1 hour to 3 minutes); NOT executing a known timecode edit (that is stage-edit).

How do I install Stage Decide in Claude Code?

Run `npx skills add Orkas-AI/Orkas-VideoStudio --skill stage-decide -a claude-code`. Or copy the skill folder (packages/skills/stage-decide in Orkas-AI/Orkas-VideoStudio) into .claude/skills/stage-decide in your project. Claude Code loads it when a task matches its description.

How do I install Stage Decide in Codex?

Run `npx skills add Orkas-AI/Orkas-VideoStudio --skill stage-decide -a codex`. Or copy the skill folder (packages/skills/stage-decide in Orkas-AI/Orkas-VideoStudio) into .agents/skills/stage-decide in your project. Codex loads it when a task matches its description.

Can I use Stage Decide 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 Orkas-AI/Orkas-VideoStudio --skill stage-decide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stage-decide, .gemini/skills/stage-decide, .github/skills/stage-decide and .opencode/skills/stage-decide in your project.

What does Stage Decide need to run?

SKILL.md names no scripts, command-line tools or credentials: Stage Decide is instructions for the agent only.

Does Stage Decide access the network?

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.

Is Stage Decide 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 Stage Decide use?

Stage Decide 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 Stage Decide use?

About 1.4k tokens (SKILL.md is roughly 5.7k 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 Stage Decide?

Skills that share tags, products or a category with Stage Decide: Video Generation (bytedance/deer-flow, 84k stars), Video Cover Image (itwanger/toBeBetterJavaer, 18k stars), Seedance (songguoxs/seedance-prompt-skill, 2.9k stars) and HyperFrames Video Entry Point (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 Stage Decide?

Orkas-AI (a GitHub user) maintains it in Orkas-AI/Orkas-VideoStudio, which has 499 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on September 22, 2026.

Source: Orkas-AI/Orkas-VideoStudio on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.