# Showcase Film

> Produce a catalog-quality showcase film from any source — an agent, an app, a portal, a process. The full pipeline: derive grammar from reference films, write the beatmap, capture, narrate, mix, gate. Trigger on any request for a showcase, demo film, sizzle reel, walkthrough or customer-facing video.

- Skill: `kody-w/showcase-film` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add kody-w/showcase-film`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kody-w/showcase-film/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: kody-w (https://skillmd.com/u/kody-w)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kody-w/showcase-film

---


# Making a showcase film that holds up

Reference bar: your best prior showcase film (the author's is a 164.8s,
1920x1080, 30fps, stereo cut at peak -3.0dB / mean -20.2dB).
Reference build system: a `films/<slug>/` folder in your work repo holding
GRAMMAR.md, BEATMAP.md, SCRIPT.md, build.py, cards.py, bedcal.py, broll/, with a
`director/` folder beside it (harvest_assets.py, assemble_film.py,
cards_html.py, device_frame.py). If none exists yet, this file is the spec.

## 0. Verify claims about the reference, including mine

Everything in this file that is a measurement carries the measurement. Where it
does not, it is flagged UNVERIFIED. Honour that distinction and extend it:
when a subagent reports a finding about the corpus, **re-measure it yourself
before acting on it**. A wrong belief about the reference propagates into every
film you build from it.

Verified 2026-08-01, reproducible:
- corpus audio: stereo 48kHz, mean -18.6 to -21.2dB, peaks -1.1 to -3.2dB
- bed `Slow Drift Ambient Synth.caf`: 19.009909s long, trough -37.5dB at 4s
  and -36.4dB at 5s against -17 to -25dB elsewhere
- `ffmpeg -h filter=alimiter` -> `level <boolean> auto level (default true)`

## 1. Derive the grammar — never assume it

If a catalog of reference films exists (e.g. an internal showcase catalog of
~48 films), **measure it**: sample 5+ films at 1Hz across their whole timelines,
classify every frame by border signature, and check the segment order agrees
across all of them. Measure cross-dissolves off the luma ramp.

A grammar derived this way on one internal catalog was reported as:
`logo 3 | b-roll 23 | motion 23 | demo 81 | motion 22 | logo 3`.

**UNVERIFIED — treat as a starting hypothesis, not a spec.** Those numbers come
from a subagent that said it classified frames at 1Hz across five films. The
method is sound but I have not independently reproduced the timings, and a
different claim from the same report ("no voiceover") turned out to be false.
Re-measure before you build to these numbers.

**The reference catalog films ARE narrated.** Measured 2026-08-01 across three of
them: stereo 48kHz, mean -18.6 to -21.2dB, peaks -1.1 to -3.2dB, with level
swinging -58 -> -39 -> -18 -> -22 -> -16 across 3s windows — the dynamic
signature of speech with sentence pauses, not a flat music bed.

An earlier version of this skill claimed the corpus had "no voiceover at all".
That was a subagent's assertion I wrote down without measuring, and it was
WRONG. It nearly caused another session to tear out a working TTS pipeline.
**Measure the reference audio yourself before you believe anything about it** —
`volumedetect` over the whole file, then per-3s windows to see whether the level
moves. Flat means bed; swinging means voice.

## 2. Freeze the beatmap before capturing anything

One row per beat: what is on screen, what the VO says, the window length, and
**the visible proof**. A beat without visible proof is a claim, and claims get
cut. Parallel work before the beatmap is frozen produces pieces that don't cut
together.

## 3. Capture

**Tab-only, always.** Desktop screen capture leaked private windows three
separate ways on this machine — see `/tab-film`. Use the browser extension's
recorder and drive frames with `wait` actions, not `screenshot` (same frames,
without flooding your context).

Product capture rules learned the expensive way:
- Citations resolve only when a message **completes**, and never inside a
  markdown table cell — only in prose. Mid-stream frames carry raw
  `[doc:turnNdocN]` tokens.
- A follow-up in an existing thread answers from history without re-calling
  tools, so it has **no citations at all**. Fresh chat every time.
- Show the user's prompt in frame. Three "ask it for…" VO lines over answers
  with no visible question is a reviewer's first finding.

## 4. Narrate

Voice `en-US-AndrewMultilingualNeural`. If Azure Speech is **Entra-only** in
your tenant (local keys disabled on every Speech resource):

```
Authorization: aad#<resourceId>#<token>
resourceId  /subscriptions/<subscription-id>/resourceGroups/<resource-group>/
            providers/Microsoft.CognitiveServices/accounts/<speech-resource>
token       az account get-access-token --resource https://cognitiveservices.azure.com
```

**Local fallback: macOS `say -v Daniel`.** Same slot structure, same fit-gate. A
film with a lesser voice ships; a film with no voice does not.

**Fit-gate every slot.** Read must land inside its window with air at the tail.
If it does not fit, widen the window or shorten the copy — **never speed up the
read**. Over ~2.6 words/sec reads rushed; name the slot and fix it.

## 5. Mix — three traps that each cost a rebuild

Contract: VO +6dB · bed at a level that is actually audible · `sidechaincompress=
threshold=0.015:ratio=8:attack=25:release=450:makeup=1` · `alimiter` ·
**NEVER loudnorm**.

1. **`alimiter` has `level=true` by default**, which makes it a *normaliser* that
   lifts the mix until peaks hit the ceiling. Lowering the ceiling makes clipping
   WORSE. Use `alimiter=level=disabled:limit=0.891` (-1.0 dBFS).
2. **`amix` adopts the FIRST input's channel layout.** A mono VO bus silently
   collapses a stereo bed to mono. Check `side` channel energy in the output.
3. **The Apple Loops bed loops every 19.0s with a -37dB trough 4-6s in.** If a
   segment cut lands on it, it reads as dead audio. Mix the bed against a
   half-loop-offset copy of itself — same material, range drops from 12dB to
   under 3dB.

Gate: VO slots mean > -19dB; bed audible in every gap (~-27dB, not -50dB);
peak below 0.0dB with nothing pinned at full scale; genuinely stereo.

## 6. Never motion-interpolate text

`minterpolate` with motion compensation warps pixels along estimated motion
vectors. Between frames of *different text* it produces unreadable ghosted soup.
Hard cuts, or crossfades <=0.3s. This shipped once.

## 7. Customer-facing versus internal

Two cuts, two vocabularies. Internal may name the build system. **Customer-facing
must not** — no RAPP, Factory, RAPPlication, MVP, prototype, pipeline, brainstem,
egg. Put a vocabulary gate in the build script that hard-fails on those in
narration and card strings, and know it **cannot see the pixels of captured
shots** — check frames by eye.

Synthetic data: badge on every data frame, and the disclaimer card lands
**before** the first data frame, not after. Show roles, never invented person
names.

## 8. B-roll must argue the scenario

Harvested stock that would drop unchanged into any other film is filler. If the
corpus has nothing that fits the domain, **cut the block short rather than pad
it** — and say so, so someone can source real footage. Never let b-roll carry
another agent's product UI, customer name or scenario.

## 9. The gate

- Longest unchanged frame <= 5.0s.
- **Watch it.** Frames at ~1Hz across the whole timeline, READ them. A green
  build is not a watched film — this failed repeatedly, once shipping a smeared
  unwatchable cut and once a frame with raw citation tokens.
- Then a **separate blind adversarial reviewer** against the reference film, on
  grammar, pacing, legibility, audio and claim-vs-proof. Fix blockers, rebuild,
  re-review. Loop until PASS with zero blockers — **and if you stop short, name
  exactly what is still wrong.** A known flaw named is fine; one the customer
  finds is not.

## Related

`/tab-film` (capture detail) · `/cs-agent-live` (get the agent presentable
first) · `/msft-deck` (the deck that travels with it)

<!-- toaster:generated:begin -->

## Run this — do not improvise

This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as `showcase_film_agent.py` and embedded as the fenced Python below (sha256 5dc5d9ae83b50a75…; a byte-exact copy is also vaulted in the capsule comment at the end of this file). On a host with sandbox execution, run the linked file directly — if it is missing, write the fence contents verbatim to `showcase_film_agent.py` first:

```bash
python3 showcase_film_agent.py '{"key": "value"}'      # arguments as one JSON object
echo '{"key": "value"}' | python3 showcase_film_agent.py   # or on stdin
python3 showcase_film_agent.py --tool                      # emit the JSON tool contract
```

Treat stdout as a tool result. If it reports missing or unresolved inputs, stop and collect them. If it returns `steps`, execute those steps in order exactly as returned; if it returns `instructions`, follow them with the supplied inputs. Otherwise use the result verbatim. Do not invent behavior beyond that output. On a host without code execution, treat the Parameters schema and the code below as the exact specification and never paraphrase a step. Never edit inside the generated markers; a converter-equipped host can instead restore the original file checksum-verified with the installed `rapp-agent-converter/scripts/toast.py convert SKILL.md --to agent`.

````python  # rapp:deterministic
"""ShowcaseFilm -- Produce a catalog-quality showcase film from any source — an agent, an app, a portal, a process. The full pipeline: derive grammar from reference films, write the beatmap, capture, narrate, mix, gate. Trigger on any request for a showcase, demo film, sizzle reel, walkthrough or customer-facing video.

Generated by the rapp skill from showcase-film. The RCI capsule at the bottom of this file carries the full original; `toast.py convert` restores it byte-exact."""

import json
import re
import sys

try:
    from agents.basic_agent import BasicAgent
except ImportError:  # running OUTSIDE a brainstem -- stay executable anyway.
    class BasicAgent:  # noqa: D101 - minimal stand-in, same contract
        def __init__(self, name=None, metadata=None):
            if name:
                self.name = name
            if metadata:
                self.metadata = metadata

        def perform(self, **kwargs):
            return "Not implemented."

        def system_context(self):
            return None

        def to_tool(self):
            return {"type": "function", "function": {
                "name": self.name,
                "description": self.metadata.get("description", ""),
                "parameters": self.metadata.get("parameters", {})}}

# The procedural layer, verbatim from the source capability.
INSTRUCTIONS = '# Making a showcase film that holds up\n\nReference bar: your best prior showcase film (the author's is a 164.8s,\n1920x1080, 30fps, stereo cut at peak -3.0dB / mean -20.2dB).\nReference build system: a `films/<slug>/` folder in your work repo holding\nGRAMMAR.md, BEATMAP.md, SCRIPT.md, build.py, cards.py, bedcal.py, broll/, with a\n`director/` folder beside it (harvest_assets.py, assemble_film.py,\ncards_html.py, device_frame.py). If none exists yet, this file is the spec.\n\n## 0. Verify claims about the reference, including mine\n\nEverything in this file that is a measurement carries the measurement. Where it\ndoes not, it is flagged UNVERIFIED. Honour that distinction and extend it:\nwhen a subagent reports a finding about the corpus, **re-measure it yourself\nbefore acting on it**. A wrong belief about the reference propagates into every\nfilm you build from it.\n\nVerified 2026-08-01, reproducible:\n- corpus audio: stereo 48kHz, mean -18.6 to -21.2dB, peaks -1.1 to -3.2dB\n- bed `Slow Drift Ambient Synth.caf`: 19.009909s long, trough -37.5dB at 4s\n  and -36.4dB at 5s against -17 to -25dB elsewhere\n- `ffmpeg -h filter=alimiter` -> `level <boolean> auto level (default true)`\n\n## 1. Derive the grammar — never assume it\n\nIf a catalog of reference films exists (e.g. an internal showcase catalog of\n~48 films), **measure it**: sample 5+ films at 1Hz across their whole timelines,\nclassify every frame by border signature, and check the segment order agrees\nacross all of them. Measure cross-dissolves off the luma ramp.\n\nA grammar derived this way on one internal catalog was reported as:\n`logo 3 | b-roll 23 | motion 23 | demo 81 | motion 22 | logo 3`.\n\n**UNVERIFIED — treat as a starting hypothesis, not a spec.** Those numbers come\nfrom a subagent that said it classified frames at 1Hz across five films. The\nmethod is sound but I have not independently reproduced the timings, and a\ndifferent claim from the same report ("no voiceover") turned out to be false.\nRe-measure before you build to these numbers.\n\n**The reference catalog films ARE narrated.** Measured 2026-08-01 across three of\nthem: stereo 48kHz, mean -18.6 to -21.2dB, peaks -1.1 to -3.2dB, with level\nswinging -58 -> -39 -> -18 -> -22 -> -16 across 3s windows — the dynamic\nsignature of speech with sentence pauses, not a flat music bed.\n\nAn earlier version of this skill claimed the corpus had "no voiceover at all".\nThat was a subagent's assertion I wrote down without measuring, and it was\nWRONG. It nearly caused another session to tear out a working TTS pipeline.\n**Measure the reference audio yourself before you believe anything about it** —\n`volumedetect` over the whole file, then per-3s windows to see whether the level\nmoves. Flat means bed; swinging means voice.\n\n## 2. Freeze the beatmap before capturing anything\n\nOne row per beat: what is on screen, what the VO says, the window length, and\n**the visible proof**. A beat without visible proof is a claim, and claims get\ncut. Parallel work before the beatmap is frozen produces pieces that don't cut\ntogether.\n\n## 3. Capture\n\n**Tab-only, always.** Desktop screen capture leaked private windows three\nseparate ways on this machine — see `/tab-film`. Use the browser extension's\nrecorder and drive frames with `wait` actions, not `screenshot` (same frames,\nwithout flooding your context).\n\nProduct capture rules learned the expensive way:\n- Citations resolve only when a message **completes**, and never inside a\n  markdown table cell — only in prose. Mid-stream frames carry raw\n  `[doc:turnNdocN]` tokens.\n- A follow-up in an existing thread answers from history without re-calling\n  tools, so it has **no citations at all**. Fresh chat every time.\n- Show the user's prompt in frame. Three "ask it for…" VO lines over answers\n  with no visible question is a reviewer's first finding.\n\n## 4. Narrate\n\nVoice `en-US-AndrewMultilingualNeural`. If Azure Speech is **Entra-only** in\nyour tenant (local keys disabled on every Speech resource):\n\n```\nAuthorization: aad#<resourceId>#<token>\nresourceId  /subscriptions/<subscription-id>/resourceGroups/<resource-group>/\n            providers/Microsoft.CognitiveServices/accounts/<speech-resource>\ntoken       az account get-access-token --resource https://cognitiveservices.azure.com\n```\n\n**Local fallback: macOS `say -v Daniel`.** Same slot structure, same fit-gate. A\nfilm with a lesser voice ships; a film with no voice does not.\n\n**Fit-gate every slot.** Read must land inside its window with air at the tail.\nIf it does not fit, widen the window or shorten the copy — **never speed up the\nread**. Over ~2.6 words/sec reads rushed; name the slot and fix it.\n\n## 5. Mix — three traps that each cost a rebuild\n\nContract: VO +6dB · bed at a level that is actually audible · `sidechaincompress=\nthreshold=0.015:ratio=8:attack=25:release=450:makeup=1` · `alimiter` ·\n**NEVER loudnorm**.\n\n1. **`alimiter` has `level=true` by default**, which makes it a *normaliser* that\n   lifts the mix until peaks hit the ceiling. Lowering the ceiling makes clipping\n   WORSE. Use `alimiter=level=disabled:limit=0.891` (-1.0 dBFS).\n2. **`amix` adopts the FIRST input's channel layout.** A mono VO bus silently\n   collapses a stereo bed to mono. Check `side` channel energy in the output.\n3. **The Apple Loops bed loops every 19.0s with a -37dB trough 4-6s in.** If a\n   segment cut lands on it, it reads as dead audio. Mix the bed against a\n   half-loop-offset copy of itself — same material, range drops from 12dB to\n   under 3dB.\n\nGate: VO slots mean > -19dB; bed audible in every gap (~-27dB, not -50dB);\npeak below 0.0dB with nothing pinned at full scale; genuinely stereo.\n\n## 6. Never motion-interpolate text\n\n`minterpolate` with motion compensation warps pixels along estimated motion\nvectors. Between frames of *different text* it produces unreadable ghosted soup.\nHard cuts, or crossfades <=0.3s. This shipped once.\n\n## 7. Customer-facing versus internal\n\nTwo cuts, two vocabularies. Internal may name the build system. **Customer-facing\nmust not** — no RAPP, Factory, RAPPlication, MVP, prototype, pipeline, brainstem,\negg. Put a vocabulary gate in the build script that hard-fails on those in\nnarration and card strings, and know it **cannot see the pixels of captured\nshots** — check frames by eye.\n\nSynthetic data: badge on every data frame, and the disclaimer card lands\n**before** the first data frame, not after. Show roles, never invented person\nnames.\n\n## 8. B-roll must argue the scenario\n\nHarvested stock that would drop unchanged into any other film is filler. If the\ncorpus has nothing that fits the domain, **cut the block short rather than pad\nit** — and say so, so someone can source real footage. Never let b-roll carry\nanother agent's product UI, customer name or scenario.\n\n## 9. The gate\n\n- Longest unchanged frame <= 5.0s.\n- **Watch it.** Frames at ~1Hz across the whole timeline, READ them. A green\n  build is not a watched film — this failed repeatedly, once shipping a smeared\n  unwatchable cut and once a frame with raw citation tokens.\n- Then a **separate blind adversarial reviewer** against the reference film, on\n  grammar, pacing, legibility, audio and claim-vs-proof. Fix blockers, rebuild,\n  re-review. Loop until PASS with zero blockers — **and if you stop short, name\n  exactly what is still wrong.** A known flaw named is fine; one the customer\n  finds is not.\n\n## Related\n\n`/tab-film` (capture detail) · `/cs-agent-live` (get the agent presentable\nfirst) · `/msft-deck` (the deck that travels with it)'

# Ordered commands lifted verbatim from the capability's own documentation.
STEPS = []


class ShowcaseFilmAgent(BasicAgent):
    def __init__(self):
        self.name = 'ShowcaseFilm'
        self.metadata = {
          "name": "ShowcaseFilm",
          "description": "Produce a catalog-quality showcase film from any source \u2014 an agent, an app, a portal, a process. The full pipeline: derive grammar from reference films, write the beatmap, capture, narrate, mix, gate. Trigger on any request for a showcase, demo film, sizzle reel, walkthrough or customer-facing video.",
          "parameters": {
            "type": "object",
            "properties": {},
            "required": []
          }
        }
        super().__init__(name=self.name, metadata=self.metadata)

    def perform(self, **kwargs):  # toaster:generated-perform
        return json.dumps({"status": "ok", "instructions": INSTRUCTIONS,
                           "inputs": kwargs,
                           "note": "Prose-only capability: follow INSTRUCTIONS "
                                   "with the given inputs."}, indent=2)

if __name__ == "__main__":
    #     echo '{"arg": "value"}' | python3 showcase_film_agent.py
    #     python3 showcase_film_agent.py '{"arg": "value"}'
    #     python3 showcase_film_agent.py --tool          # emit the JSON tool contract
    _a = sys.argv[1:]
    if _a and _a[0] == "--tool":
        print(json.dumps(ShowcaseFilmAgent().to_tool(), indent=2))
    else:
        _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}")
        print(ShowcaseFilmAgent().perform(**json.loads(_raw)))

# rci-capsule:v1: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
````

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