agentleFS
aaronjmarsaaronjmars/miroshark-aeon/skills/video-script/SKILL.md

video-script

Skill20 starsChanged 22 days agoMITView on GitHub

Turn a repo, product page, or update into a recording-ready video script in a receipts-first format - verifies every claim against live sources, then writes timestamped VO + on-screen direction with a plain-language glossary, assets checklist, anti-tells, and a verify-before-recording list

23 other projects use a version of this, 2 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 5 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 15 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read memory/MEMORY.md and the last 7 days of memory/logs/ — know what has already been covered, and don't script the same story twice.

    line 26
  2. Very specific:

    If a soul/ directory exists, read soul/SOUL.md and soul/STYLE.md for voice calibration.

    line 27
  3. Distinctive:

    Every claim you could NOT verify either gets cut or becomes a checkbox in "Verify before recording".

    line 36
  4. Distinctive:

    After the chapters, ALWAYS:

    line 70
  5. Distinctive:

    Every technical term gets one plain-language line or gets cut.

    line 85
  6. Distinctive:

    Never reference private forks or internal accounts — point at the upstream/public repo.

    line 87
  7. Very specific:

    Write the script to output/video-scripts/${today}-<slug>.md (shell redirection.

    line 91
  8. Distinctive:

    If the source is private or unreachable, say so and stop — a script built on unverifiable claims defeats the format.

    line 104

“Common” is measured: kept by at least 6 in 10 of the 15 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

${var} — Selector: <source> [--minutes N].

  • repo:<owner/repo> → script about that repo's latest update wave (README + recent releases/commits decide what "the update" is).
  • <url> → script about that product page / doc / announcement.
  • <topic> (anything else non-empty) → script about that topic; research it first.
  • empty → pick the most script-worthy update from the last 7 days of memory/logs/ (a shipped feature, a release, a milestone with receipts). If nothing qualifies, log VIDEO_SCRIPT_NO_SOURCE, send no notification, and exit clean.
  • --minutes N anywhere → runtime target (default 4; ~150 spoken words per minute).

Examples: "repo:aeonfun/aeon --minutes 6", "https://example.com/launch", "our new payments integration --minutes 3".

Today is ${today}. Write a recording-ready video script. No placeholders — every number, address, and date in it must be verified this run.

Shared preamble

  1. Read memory/MEMORY.md and the last 7 days of memory/logs/ — know what has already been covered, and don't script the same story twice.
  2. If a soul/ directory exists, read soul/SOUL.md and soul/STYLE.md for voice calibration. The script is the operator talking to a smart friend — if soul/ is empty, default to terse, direct, first-person.
  3. Parse ${var} per the selector above into source + minutes.

Step 1 — Research and verify (never skip)

The format is called receipts-first because the receipts are real:

  • Fetch the live source now (WebFetch / gh api for repos — README and recent releases/commits). Do not write numbers, counts, addresses, or dates from memory.
  • Collect concrete verifiable artifacts: contract addresses, live dashboards, version numbers, dollar figures, dates. These become the cold open and the Receipts section.
  • Every claim you could NOT verify either gets cut or becomes a checkbox in "Verify before recording".

Step 2 — Find the angle

The cold open is never the pitch. It is the single most concrete, verifiable, surprising fact — shown, not claimed (a live dashboard, a list of on-chain addresses, a terminal). Test: could a skeptic check it in 30 seconds? The flex is usually the credibility mechanism (immutability, safety gates, audit trail, reproducible tests), not the feature list.

Step 3 — Write the script

Header block:

# <project> — "<Working Title>" — Video Script

**Runtime target:** ~M:SS
**Framing:** receipts-first (open on <the artifact>)
**Byline:** <the project's public handle>
**One-line pitch:** <one sentence, no hype>
**Rev:** v1, ${today} — plain-language pass baked in; every technical term explained in one line or cut.

Chapters, each with a time range:

## <Chapter name> — 0:00 – 0:25

**VO:**
> Spoken text as blockquote. Short sentences. Operator voice.

**On screen:**
- Concrete shot list: screencaps, terminals, motion graphics, captions in `backticks`.

Canonical arc (adapt, don't force): cold open (the receipt, no greeting) → context/problem (one plain-language line for the underlying tech) → how it works (the mechanism, credibility story first) → what's new or the demo → straight talk (limits, flat delivery — every script has this section; it is a feature, not a concession) → close + CTA (one action).

After the chapters, ALWAYS:

  • Plain-language glossary — jargon → the phrasing used on screen, plus terms deliberately cut.
  • Assets checklist — everything to capture or build; mark what must be captured day-of.
  • Anti-tells checklist — the voice rules below plus per-video specifics (what not to oversell, whose failures not to dunk on).
  • Verify before recording — dated checkboxes for every volatile fact, each with where to re-check it.
  • Receipts — the verified facts with sources and the date verified (mandatory when the video makes on-chain or factual claims).

Voice rules (the anti-tells)

  • Banned: "In this video", "Let's dive in", "game-changer", "insane", "mind-blowing", "revolutionary", "trustless" (name what enforces it instead).
  • Short declaratives; read VO lines aloud mentally before keeping them.
  • Numbers stay conservative — round down; "tens of millions" beats a precise figure that might drift.
  • Incidents and competitors are stakes, not villains — flat delivery, no dunking.
  • The safety/verification story outranks the feature list.
  • Every technical term gets one plain-language line or gets cut.
  • On-screen text must NOT mirror the VO verbatim — reword it so the viewer isn't reading what they're hearing.
  • Never reference private forks or internal accounts — point at the upstream/public repo.

Step 4 — Deliver

  1. Write the script to output/video-scripts/${today}-<slug>.md (shell redirection; mkdir -p first).
  2. Notify with ./notify -f /tmp/video-script-notify.md (write the body under /tmp/, never memory/ or output/): the working title, runtime, the chosen angle in one line, one thing deliberately left out, and a clickable link to the script built from the run's environment:

    SCRIPT_URL="${GITHUB_SERVER_URL:-https://github.com}/${GITHUB_REPOSITORY}/blob/main/output/video-scripts/<file>.md"
  3. Log to memory/logs/${today}.md under a ### video-script heading: source, angle, runtime, output path, and any claims deferred to day-of verification. Status codes: VIDEO_SCRIPT_OK on success, VIDEO_SCRIPT_NO_SOURCE when ${var} was empty and the logs offered nothing script-worthy.

Limits

  • This writes the script; it does not record, edit, or generate footage. The assets checklist is a to-do list for a human (or other skills).
  • Verification is only as fresh as this run — volatile facts must still be re-checked day-of; that is what the "Verify before recording" section is for.
  • If the source is private or unreachable, say so and stop — a script built on unverifiable claims defeats the format.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.