Palmier Pro Shorts Editor — 9:16 Vertical

Palmier Pro is a local desktop NLE controlled via MCP — fundamentally different from Descript, which is a cloud service. These differences change the workflow significantly:

• No upload step. Palmier runs on the user's machine, so local file paths (e.g. /Users/yash/Videos/foo.mp4) can be passed straight to import_media via source.path — no signed-URL workaround needed. path and bytes imports finalize synchronously; only url imports are async and need wait_for_job-style polling (there's no such tool here — just re-check get_media).
• No "clean up my footage" AI agent. Unlike Descript's prompt_project_agent, Palmier has no built-in filler-word/silence removal. Only attempt manual filler-word removal (caption the clip, read the transcript, split_clip + remove_clips around junk) if the user explicitly asks — it's labor-intensive. Otherwise just trim obvious dead air at the start/end by eye.
• Canvas size is changeable, but not via MCP. The project resolution (width/height) is a real, user-changeable setting in Palmier Pro — there is just no tool exposed to Claude for setting it. The user has to change it themselves inside the app. Always check get_timeline first — if width/height aren't 1080×1920, stop and ask the user to switch the project to vertical 1080×1920 (9:16) in-app, then re-check get_timeline (look for settingsConfigured: true and the new dimensions) before doing any crop/transform math.
• No export/render/publish tool. Nothing equivalent to Descript's publish_project. Once the timeline edit is done, tell the user to export from inside Palmier Pro themselves (File > Export or equivalent).
• Crop + transform gotcha (confirmed by testing, not guessed): the transform box (centerX/centerY/width/height) describes where the full, uncropped source would map onto the canvas. crop then reveals a window inside that box — it does NOT resize or recenter the box to fit the cropped content. Naively setting width:1, height:0.5, centerX:0.5 plus a crop produces a pillarboxed result (content stuck to one edge, black space on the other), not a full-bleed fill. See Step 5 for the actual formula to compensate — this is the single easiest thing to get wrong in this tool.

Step 1 — Always start with get_timeline

Call get_timeline with no args. It returns the current canvas width/height/fps, settingsConfigured, and any existing tracks/clips. Use the returned resolution for all later math — never assume 1080×1920 until verified.

The canvas size is a real, changeable setting — it's just changed in the Palmier Pro app, not through any tool available here. If it isn't already 1080×1920, tell the user to switch the project/sequence to vertical (1080×1920, 9:16) inside the app. You can keep importing/inspecting the source while waiting. Once they confirm, call get_timeline again and check settingsConfigured: true with width: 1080, height: 1920 before doing any crop/transform math.

Step 2 — Import the source file

import_media({ source: { path: "<absolute local path>" }, name: "<descriptive name>" })

Synchronous for local paths. Then call get_media to confirm and read duration, sourceWidth, sourceHeight, hasAudio.

Step 3 — Add to timeline & inspect layout

Convert source duration to project frames (project fps, not source fps): durationFrames = round(duration_seconds * project_fps).

add_clips({ entries: [{ mediaRef, startFrame: 0, durationFrames }] })

This auto-creates a video track + linked audio track.

Call inspect_timeline with endFrame = clip duration and maxFrames: 8-12 to sample frames across the whole clip. Use this to determine:

• Side-by-side (source width roughly 2–3.5× the height, e.g. 3840×1080): dual-monitor recording — identify which half is facecam vs screen by eye.
• Standard talking-head (source ~16:9, e.g. 1920×1080): single speaker, crop to face. Also use this pass to spot dead air at the very start/end — trim later with set_clip_properties (trimStartFrame/trimEndFrame).

Step 4 — Pick the segment

If the source is already ≤60–75s and stays on-topic throughout (common for short raw recordings), just use the whole thing. If longer, ask the user which part is the hook/highlight, or — after running captions (Step 6) — skim the transcript via get_timeline's captionGroups to pick a self-contained ~60s window, then adjust durationFrames/trims.

Step 5 — Crop & position for vertical

Pre-requisite: canvas must already be 1080×1920 (confirmed in Step 1).

The crop + transform formula (read this before either layout)

Because transform maps the box for the full uncropped source onto the canvas, and crop only reveals a window inside that box, you must inflate the box and shift its center to compensate, per axis, independently:

Given crop insets [top, right, bottom, left] (fractions 0–1 of the full source) and a target rectangle on canvas [x0,x1] × [y0,y1] (fractions of canvas) that you want the cropped content to exactly fill:

• Horizontal: keptW = 1 - left - right. width = (x1-x0) / keptW. boxLeft = x0 - left*width. centerX = boxLeft + width/2.
• Vertical: keptH = 1 - top - bottom. height = (y1-y0) / keptH. boxTop = y0 - top*height. centerY = boxTop + height/2. If an axis has no crop on it (inset0=inset1=0), the formula collapses to the obvious size = target range, center = target center — i.e. only the cropped axis needs the inflate/shift treatment.

Don't be alarmed if width comes out > 1 or centerX comes out negative or > 1 — that's expected; it describes an oversized box that's mostly off-canvas, with crop revealing just the on-canvas slice.

Layout A — Side-by-side source (facecam + screen)

Add a second clip referencing the same mediaRef, via another add_clips call omitting trackIndex — that auto-creates a fresh video track (+ linked audio track) rather than overwriting the first clip.

Worked example, confirmed working: 3840×1080 source → 1080×1920 canvas, each half targeting a 1080×960 box.

• Left half (facecam), target x:[0,1], y:[0.5,1] (bottom half). Crop insets top0 right0.592 bottom0 left0.092 (centers the middle ≈31.6% of the full source width — i.e. the centered portion of just that half). Per the formula: width=1/(1-0.092-0.592)=3.165, centerX=-0.092*3.165+3.165/2=1.291. Vertical has no crop so height=0.5, centerY=0.75 directly. Final transform: { width: 3.165, height: 0.5, centerX: 1.291, centerY: 0.75 }.
• Right half (screen), target x:[0,1], y:[0,0.5] (top half). Crop insets top0 right0.092 bottom0 left0.592. Same width=3.165 (symmetric crop amount), centerX=-0.592*3.165+3.165/2=-0.291. height=0.5, centerY=0.25. Final transform: { width: 3.165, height: 0.5, centerX: -0.291, centerY: 0.25 }.
• Apply crop via set_keyframes({ clipId, property: "crop", keyframes: [[0, top, right, bottom, left]] }) — one row at frame 0 = constant value.
• Apply transform via set_clip_properties({ clipIds: [id], transform: {...} }).
• Default convention: screen on top, facecam on bottom — flip if the user prefers otherwise.
• Mute whichever duplicate clip's linked audio you don't want doubled: set_clip_properties({ clipIds: [linkedAudioId], volume: 0 }). Always recompute from the formula above for the actual source resolution and crop amounts — don't reuse the numbers verbatim if the dimensions or desired crop differ.

Layout B — Standard talking-head (no screen share)

To crop the centre ~56% of the width and fill the full 1080×1920 canvas (target x:[0,1], y:[0,1], no vertical crop):

• Crop insets top0 right0.22 bottom0 left0.22 (tune based on framing).
• Per the formula: keptW = 1-0.22-0.22 = 0.56, width = 1/0.56 = 1.786, centerX = -0.22*1.786 + 1.786/2 = 0.5 (symmetric crop keeps centerX at 0.5 — only asymmetric crops shift it). height=1, centerY=0.5 (no vertical crop).
• Final transform: { width: 1.786, height: 1, centerX: 0.5, centerY: 0.5 }.
• This is untested against a live talking-head source so far — verify with inspect_timeline and nudge insets if the speaker's face isn't framed well, recomputing width/centerX from the formula each time you change the insets. After either layout, inspect_timeline again at a few frames to visually confirm full-bleed framing (no black pillarboxing) before moving on.

Step 6 — Captions

add_captions({
  centerY: 0.92,          // lower third, close to bottom edge — confirmed readable and clear of the dividing line in a side-by-side layout
  fontSize: 60,            // bigger than the 48 default for vertical mobile viewing
  color: "#FFFFFF",
  fontName: "Helvetica-Bold"
})

Transcribes on-device and places styled caption clips automatically — no manual transcript work needed. Omit clipIds to auto-detect the primary spoken track. Set language (BCP-47) if the footage isn't English.

Step 7 — Final check

inspect_timeline across start/middle/end frames to eyeball the finished composite: layout correct, captions readable, no clipped faces/text, audio not doubled.

Step 8 — Export (manual)

No export/publish tool exists. Tell the user the edit is ready in Palmier Pro and they need to export/render it themselves from the app — mention the target resolution (1080×1920) so they pick the right export preset.

Extracting multiple shorts from one long source

Palmier has no multi-sequence/composition concept — get_timeline always refers to the single project timeline. If asked for several shorts from one long recording, you can't make 3 separate "projects"; instead you lay the 3 edited segments back-to-back on the one timeline and tell the user the timeline ranges to export separately.

1. Transcribe before picking segments. Import the long source, add it as one full-length scratch clip (add_clips, startFrame: 0, durationFrames: full length), then add_captions on it with no clipIds (auto-detects the one spoken track). This gives you a complete transcript via get_timeline's captionGroups — page through with startFrame/endFrame windows (~150s of footage per window is a safe chunk size to stay under the 200-row-per-group cap). Read the full transcript and pick 2-4 self-contained, distinct moments (a clean definition, a specific surprising technique, a narrative/news beat — look for natural sentence boundaries to cut on, e.g. "So this is what X is" or a topic-shift "Now,"/"So,").
2. Clear the scratch work. remove_tracks for every track index from that first get_timeline call (caption track + video track + audio track) once you've picked your segments and noted their source frame ranges. Don't try to reuse the scratch clip's crop/captions — rebuild clean per segment.
3. Lay segments out sequentially. Pick an output position for each segment back-to-back (segment 2 starts where segment 1 ends, etc). For each segment: add_clips for the facecam/screen pair at the output startFrame with durationFrames = segment length (this defaults to showing SOURCE frames [0, length) — wrong, fix next).
4. Pull the right source window with trimStartFrame. Call set_clip_properties({ clipIds: [id], trimStartFrame: <sourceSegmentStart> }) on each of the pair's clips. This shifts the source in-point so the displayed range becomes [sourceSegmentStart, sourceSegmentStart + durationFrames), while the clip's timeline position/duration (set in step 3) stays put. (No trimStartFrame needed if a segment happens to start at source frame 0.) Then apply the usual crop + transform formula from Step 5, and mute the duplicate audio, same as a single short.
5. Caption each segment with explicit clipIds. Once there's more than one spoken-audio clip on the timeline, add_captions auto-detect ("omit clipIds") is ambiguous — always pass the specific unmuted audio clip ID for that segment so it transcribes only that range.
6. Report exact ranges to the user, both as output-timeline timecodes (so they can scrub/export) and original-source timecodes (so they know what was used). There's still no export tool — they export each range manually, same limitation as a single short.

Tightening duration (and why "remove silences" doesn't mean what it does in Descript)

Palmier has no waveform/VAD tool, and in practice the auto-captions from add_captions tile back-to-back with no exposed gaps — even across a 9+ minute recording, consecutive caption clips' startFrame always equals the previous one's startFrame + durationFrames. So there is no reliable signal here for literal silence detection; don't promise silence removal in the Descript sense. Say so directly rather than silently skipping the request or pretending a cut was silence-based when it wasn't.

What IS available and serves the same practical goal (tightening pacing, hitting a duration target like "under 60s"): cut filler, tangential, or redundant spoken content using the same multi-piece splicing mechanism as the multi-short workflow above, but within a single short:

1. Read the transcript and identify droppable spans — preamble/throat- clearing before the real hook starts, tangential detail not essential to this specific cut's narrative, redundant restatements, hedging asides. Favor cutting whole clauses at clear topic-shift boundaries (e.g. "But during all of this," "And essentially what had ended up happening is") over mid-clause trims — safer both for not garbling meaning and for not relying on fine-grained word-order precision in the caption data (caption clips are frame-accurate for audio cuts, but their displayed text order can be slightly jittery at the word-to-word level, so don't trust it for surgical precision below the clause level).
2. Prefer fewer, larger cuts over many tiny ones. A single well-chosen contiguous sub-range (just pick a later start / earlier end) is far simpler and more reliable than stitching 4+ fragments. Only resort to a multi-piece splice when you genuinely need to preserve a strong opening hook AND a satisfying closing line that aren't adjacent in the source — and even then, 2-3 pieces (1-2 cuts) is usually enough.
3. Each kept piece becomes its own facecam+screen clip pair with its own trimStartFrame pointing at that piece's source start, placed back-to-back in OUTPUT position (piece 2 starts where piece 1 ends, etc) — same mechanics as the multi-short section above, just within one short instead of across several.
4. Caption the whole short in one add_captions call listing every piece's unmuted audio clip ID in clipIds — no need for one call per piece.
5. Compute the final duration (sum of all piece durationFrames / fps) and sanity-check it against the target before calling it done — don't eyeball it.

A note on tool reliability

A Palmier tool call can occasionally time out with no response (observed: get_timeline and set_clip_properties both hung for several minutes then errored). When that happens, don't assume the call partially applied — re-check with a fresh get_timeline once the connection recovers and re-apply anything that's missing (e.g. a trimStartFrame or transform that didn't show up in the refreshed state) before continuing. Treat the timeline state as the source of truth over what you think you already sent.

Speeding up playback slightly (e.g. "make it a bit faster")

set_clip_properties has a speed field (1.0 = normal, >1.0 = faster). Two things that aren't obvious from the schema alone, confirmed by testing:

• speed and durationFrames are independent fields you must reconcile yourself. Setting speed alone does not shrink the clip's timeline footprint. To actually get a shorter, faster-playing clip that still plays the same source content, set both in the same call: durationFrames = round(oldDurationFrames / speedMultiplier), with trimStartFrame left untouched. The math works out so the same amount of source content is consumed either way — only the timeline footprint shrinks.
• Speed (like other timing fields) propagates to a clip's own linked audio automatically, but a facecam/screen pair in this side-by-side layout are in separate link groups — apply speed+duration to both the facecam clip and the screen clip of a piece, not just one.
• Downstream pieces need move_clips after this. Shrinking piece 1's duration leaves a gap where piece 2 used to start — recompute every later piece's new startFrame (previous piece's new start + new duration) and move both its facecam and screen clips there.
• Captions must be fully regenerated, not shifted. The old caption clips' timing assumed the original tempo; after a speed change the same words land at different frames. remove_tracks the caption track(s) and rerun add_captions with the same clipIds — confirmed this correctly retranscribes against the new (faster) audio with no manual offset math needed.
• A genuinely subtle bump is speed: 1.05 (5% faster) — noticeable enough to tighten pacing, not so much that pitch/voice quality suffers.

Real silence removal: use ffmpeg outside Palmier, not a workaround inside it

Given the "no VAD tool, captions tile with no gaps" limitation documented earlier, the actually-correct fix — if the user wants real silence removal rather than manual filler-cutting — is a pre-processing pass with ffmpeg on the raw source file, before it ever gets imported into Palmier. This works because ffmpeg runs locally on the user's machine (same place Palmier reads local paths from) and has a real audio-level silencedetect filter Palmier has no equivalent of.

Recipe (verified working, including edge cases, in a sandboxed test before delivering it to a user):

1. ffmpeg -i in.mp4 -af silencedetect=noise=-30dB:d=0.6 -f null - logs silence_start/silence_end pairs to stderr.
2. Parse those into "keep" segments (the complement, with ~0.1-0.15s padding left on each side so word onsets/offsets don't get clipped).
3. Build a trim/atrim + concat filter_complex from the keep list and re-encode once.
4. Handle edge cases explicitly: zero silences found (just copy the file through, don't error), and silence that runs to end-of-file (no matching silence_end gets logged — treat it as ending at the file's total duration). Critical portability constraint discovered by testing: the user's machine is macOS, where the stock /bin/bash is 3.2 (no mapfile, no array features newer than that) and the stock grep is BSD grep (no -P/PCRE support). A script written with GNU-isms will work when you test it in this Linux sandbox and then silently fail on the user's machine. Concretely: read line-by-line into arrays with a while IFS= read -r loop instead of mapfile; parse the ffmpeg log with sed -n 's/.../\1/p' instead of grep -oP; do float arithmetic with awk 'BEGIN{...}' instead of bc (recent macOS has dropped bc). Test the script in this sandbox against a synthetic file built with ffmpeg -f lavfi (tone + anullsrc segments concatenated) to validate the actual cut points and resulting duration before handing it over — don't ship an untested script just because the syntax looks right.

This is a standalone utility, independent of Palmier — hand it to the user as a script to run once against their raw recording, producing a cleaned file they then import (Step 2) instead of the original.

Export only ever renders the whole timeline

Confirmed from the actual Export dialog (screenshot, not guessed): Format / Codec / Resolution / Frame Rate fields only, no in/out-range field, and the displayed duration always equals the full timeline length. There is no way to export just one short from inside Palmier when multiple shorts live back-to-back on the same timeline (see "Extracting multiple shorts" above for why they have to).

Workaround: export once, split outside Palmier with ffmpeg. Same toolchain as the silence-removal script — the user already has ffmpeg, so hand them a tiny script that cuts the one combined export into N files at known timecodes (seconds = outputStartFrame / fps), re-encoding (not -c copy) so each cut lands exactly on the boundary rather than the nearest keyframe. Test it against a synthetic dummy file of the same total duration before delivering, the same way as the silence script — confirm each piece's ffprobe-reported duration matches the expected (endFrame-startFrame)/fps for that short.

When add_captions produces text that doesn't match what you expected

This bit twice in one session and both times the bug was mine, not Palmier's — worth checking method, not the tool, first:

Always verify with a disposable scratch clip before assuming a tool bug. If transcribed text doesn't match what you expect at a given trimStartFrame, don't conclude add_captions is broken and don't just retry the same call (a literal retry reproduces the identical "wrong" result if your trim value is actually wrong — that's not evidence of a flaky tool, it's evidence the input was wrong). Instead: add_clips a small (~300-1800 frame) clip at some far-out, clearly-unused output position (e.g. startFrame: 20000+), set trimStartFrame to the value in question, add_captions on just that clip, and read what's actually there. This is cheap (one small clip, a few seconds of transcription) and gives ground truth before touching the real timeline.

The actual root cause both times: confusing output-relative frame numbers with source-relative ones. get_timeline's caption dump reports startFrame in output/timeline coordinates. If a clip has trimStartFrame=X and sits at output startFrame=Y, a caption reported at output frame F corresponds to source frame F - Y + X, not F itself. It's easy to skim a caption dump from a clip that's already positioned somewhere non-zero on the timeline, spot the line you want, and copy its startFrame directly into a new trimStartFrame — that silently drops the -Y+X conversion and points at the wrong few seconds of source. The failure mode is insidious because the wrong content is real, coherent transcript text (often from a topic the source covers elsewhere), so it doesn't look obviously broken on a quick visual check — it takes noticing the actual words don't match the intended narrative. Safest practice: only ever read frame numbers for new trimStartFrame values from a dump where the clip sits at output startFrame: 0 with no trim of its own (so output coordinates equal source coordinates 1:1) —