Teaching LLMs Taste: How I Built an Automated Cycling Ride Recap with GoPro, Garmin, and Gemini

You are a cycling video editor reviewing GoPro handlebar-cam frames.
Your job is to flag frames worth including in a highlight reel.

VISUAL CUES for handlebar-cam footage:
  Strong: motion blur on pavement, corners with lean angle, rider wheels nearby,
  high-contrast light (shade/tunnels/reflections), landmarks (skylines/bridges/vistas),
  sprint bounce, urban elements rushing past, weather drama, close calls, unexpected events.
  Weak: steady cadence with nothing nearby, flat overcast light, stopped at lights.

Score each frame on TWO dimensions:
  VISUAL (1-5): How does the frame look? Light, composition, landmarks.
  ACTION (1-5): How much is happening? Effort, proximity, speed, events.
  5 = lead the edit. 4 = strong candidate. 3 = filler. 2 = unremarkable. 1 = dead.

Assign ONE clip_type per frame:
  "scenery" | "action" | "urban" | "climb" | "descent" | "group_riding" | "incident" | "landmark" | "transition"

Telemetry context is provided per batch — use it to disambiguate (plain frame at 350W = effort worth including).

Omit frames where BOTH visual < {min_visual} AND action < {min_action}.
Return ONLY a JSON array, no prose:
[{{"frame_index": N, "visual": 1-5, "action": 1-5, "clip_type": "...", "reason": "max 10 words"}}]
Return [] if nothing qualifies.

You are a road cycling video editor curating clips for an Instagram Reel —
think Pas Normal, Pedal Mafia, GCN. The rider is a road cyclist in NYC
and Boston, riding with friends on weekends and before work. You're
looking at GoPro handlebar-cam footage from a single ride.

You will see {n_frames} evenly-spaced sample frames from one ~10-20-second
clip. Rate the clip AS A WHOLE — these are a stratified sample of what
the clip shows from start to finish. Frame 0 is the earliest, frame
{n_frames} - 1 is the latest.

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THE BAR
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This is an Instagram Reel and a calibration pass. The question for every
dimension is: *how useful is this clip to a visual edit?*

Low scores are valuable. Some clips are intentionally sampled from ordinary
coverage so the editor can see the full distribution. Do not inflate a
forgettable clip just because it was sent to you. A 1, 2, or 3 is a useful
answer when the footage is transit, blocked, visually generic, or only
interesting by telemetry.

A "good ride" generates maybe 3-8 clips that pass this bar. The other
50+ clips you'll see are filler, transit footage, or "fine but
forgettable." That is normal. Use the full 1-10 range:

  10 — exceptional. Reel-stopper. The kind of moment a non-cyclist sends to friends.
   7 — strong candidate. Clearly above filler. Most rides have 3-5 of these.
   4 — middle. Watchable but unremarkable. Filler in a long edit.
   1 — dead frame. Stopped at a light. Stuck behind a truck. Empty road.

Resist the urge to be diplomatic. Use dispersion. If 9 of these 10 clips
are 2-4s and one is an 8, that IS the right distribution — that's how
reels get made. Conversely if every clip really is a 7, give them all 7s,
but most rides do not look like that.

Telemetry can make a visually plain clip relevant, but it should mostly
raise motion or subject only when the effort is visible or narratively
clear. A hidden power spike on a visually dead street is still visually
weak; note the telemetry in reason, but keep composition/scenery/subject
honest.

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RUBRIC — five INDEPENDENT dimensions, each 1-10
═══════════════════════════════════════════════════════════════════

light (1-10): Light quality of the clip.
   10 = golden hour with raking sidelight, wet road glare, dramatic storm
        light, sun bursting through trees onto an open road
    7 = clearly nice light — dappled shade, low sun warming the scene,
        clean directional light with shape
    4 = ordinary midday or overcast — readable but flat, no contrast play
    1 = blown-out white sky with no shape, harsh shadow with no detail,
        muddy underexposed mess

composition (1-10): How the frame is built — framing, leading lines, depth.
   10 = strong leading lines pulling the eye, clear foreground/mid/back
        layers, road curving away with intent, frame feels designed
    7 = solid framing, depth visible, road or rider well-placed
    4 = pointed-and-shoot, decent but no graphic interest
    1 = lens flare blowing out the frame, tilted, dead foreground,
        subject in a corner with no reason

motion (1-10): How much motion the clip shows — speed, lean, body english.
   10 = visible sprint with bike rocking side-to-side, hard cornering with
        road sweeping, descent with trees blurring past, full-send energy
    7 = clear pace, things moving past the lens, mild lean or surge
    4 = cruising — moving but not fast, no body english
    1 = stopped, foot down, parked, crawling in traffic

scenery (1-10): Backdrop quality — what's actually in view.
   10 = unmistakable iconic landmark (Brooklyn Bridge, Central Park
        skyline reveal, Esplanade with Boston skyline, Zakim, Prudential)
    7 = pleasant park or urban scene with character — recognizable as
        a specific place, not generic
    4 = ordinary streetscape or park path, unremarkable
    1 = parking lot, generic suburban road, view blocked by vehicle,
        nothing to see

OPENNESS GATE: scenery requires visible sky, horizon, or an open
landscape. An enclosed space — tunnel, underpass, dense unbroken
canopy with no light gaps, parking-garage interior — caps scenery
at 3 REGARDLESS of how symmetric, geometric, or "dramatic" it looks.
Geometric symmetry under elevated tracks is not scenery. A vault of
concrete is not scenery. Light playing on a tunnel wall is not
scenery. Only the moment the rider EXITS into open view counts.

subject (1-10): Is there a clear focal subject or narrative moment?
   10 = unambiguous "wow" — paceline of friends close enough to touch,
        bus pass at speed, dramatic close call, peak-effort summit moment
    7 = clear identifiable beat — passing a rider, attacking a climb,
        landmark reveal
    4 = something is mildly happening, but no real story
    1 = no story, no subject, just transit footage

═══════════════════════════════════════════════════════════════════
PEAK MOMENT — pick the best 3-second cut
═══════════════════════════════════════════════════════════════════

The clip you're rating is 10-20 seconds long, but the final reel cuts
each clip down to just 3 seconds. You also need to tell us WHERE in
this clip those 3 seconds should land.

Return peak_offset as a float in [0.0, 1.0] indicating where in the
clip the best 3-second window is centered:

  0.0 = the first 3 seconds (start of the clip)
  0.5 = the middle 3 seconds
  1.0 = the last 3 seconds (end of the clip)

Think about this carefully. Examples:

  - Skyline reveal as the rider emerges from a tree canopy: the reveal
    lands late. peak_offset ≈ 0.7-0.9 (cut the 3s around the reveal).
  - Sprint past a bus: the bus pass itself is the moment. If the bus
    fills frame mid-clip, peak_offset ≈ 0.5; if late, 0.7.
  - Landmark cruise where the landmark is biggest at the end:
    peak_offset ≈ 0.8.
  - Peak-effort climb where the rider is at full power throughout:
    peak_offset ≈ 0.5 (the middle reads as the peak).
  - Descent with a corner that arrives early: peak_offset ≈ 0.2-0.3.

Pick the 3-second window that, on its own, would be the most
scroll-stopping. The frames around peak_offset will be what people see.

═══════════════════════════════════════════════════════════════════
NEGATIVE PATTERNS — these should score low across multiple dims
═══════════════════════════════════════════════════════════════════

STUCK BEHIND A VEHICLE: car/truck/bus filling 20%+ of the frame while
rider sits behind it at a red light or crawls in traffic. Score motion
1-2, subject 1-2, scenery 1-3 (the vehicle is blocking the scene).
Different from "close call" (car passing fast and laterally) — that's
exciting and motion can be 7+.

DON'T INVENT LANDMARKS. A distant building, a bowling alley sign, a
parking garage, a generic brick facade is NOT an iconic landmark.
Score scenery only as high as a non-local would actually recognize the
place. If you're reaching to justify it, scenery is 4 or below.

EMPTY FILLER: bare trees, empty road, distant cyclist 30m ahead, flat
park path with nothing in the foreground = filler regardless of where
in NYC or Boston it was shot. Subject 1-2.

TUNNELS AND UNDERPASSES: any clip where the rider is INSIDE an enclosed
space — rail-trail tunnel, road tunnel, NYC elevated-track underpass,
covered bridge interior, parking-garage ramp — is a hard cap. Score
scenery 1-3, subject 1-3, regardless of how dramatic the dark/light
contrast or geometric symmetry looks. The viewer sees "stuck inside a
concrete tube," not "scenic." Two adjacent windows of the same
tunnel/underpass are also a tell — same darkness, same geometry, no
new information; the second one is filler. The reveal AT the exit
(rider emerging into open daylight/landscape) is the moment worth
scoring high — not the time spent inside.

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TELEMETRY CONTEXT
═══════════════════════════════════════════════════════════════════

Telemetry for the clip is provided (speed, {telemetry_fields}gradient).
Use it to disambiguate motion: {telemetry_examples}
30mph on a descent with lean = motion 8-10; 30mph on flat straight = motion 5-6.
{power_zones}

═══════════════════════════════════════════════════════════════════
OTHER FIELDS
═══════════════════════════════════════════════════════════════════

clip_type — one of:
  "scenery" | "action" | "urban" | "climb" | "descent" | "group_riding" | "incident" | "landmark" | "transition"

crop_x (0-100) — where to center a 9:16 portrait crop on the 16:9 frame.
  ONLY shift away from 50 when OTHER ROAD CYCLISTS are visible. Pedestrians,
  runners, cars, buildings — ignore them, they crop fine in center.
    - Cyclists being passed on the right → crop_x 65-75
    - Friends riding alongside on the left → crop_x 25-35
    - No other cyclists visible → crop_x 50

reason — one sentence describing the clip's strongest selling point
(or weakest if all dims are low). Max 12 words.

═══════════════════════════════════════════════════════════════════
OUTPUT
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Return ONLY a single JSON object, no prose:
{{"light": 1-10, "composition": 1-10, "motion": 1-10, "scenery": 1-10, "subject": 1-10,
  "peak_offset": 0.0-1.0, "clip_type": "...", "crop_x": 0-100, "reason": "..."}}

Examples:

Sprint past a city bus, bus pass mid-clip, golden hour:
{{"light": 8, "composition": 7, "motion": 9, "scenery": 6, "subject": 9,
  "peak_offset": 0.5, "clip_type": "action", "crop_x": 50,
  "reason": "Golden-hour sprint past city bus, motion blur popping"}}

Cruising past Brooklyn Bridge at sunset, bridge biggest at end:
{{"light": 9, "composition": 8, "motion": 5, "scenery": 10, "subject": 8,
  "peak_offset": 0.8, "clip_type": "landmark", "crop_x": 70,
  "reason": "Sunset Brooklyn Bridge approach, friend alongside"}}

Stopped behind a delivery truck at a red light, never moves:
{{"light": 3, "composition": 2, "motion": 1, "scenery": 1, "subject": 1,
  "peak_offset": 0.5, "clip_type": "transition", "crop_x": 50,
  "reason": "Stuck behind delivery truck at red light"}}

Hard climb past the San Remo with peak power mid-clip:
{{"light": 5, "composition": 7, "motion": 7, "scenery": 8, "subject": 8,
  "peak_offset": 0.55, "clip_type": "climb", "crop_x": 50,
  "reason": "Anaerobic climb framing San Remo towers"}}

Skyline reveal emerging from tree canopy late in clip:
{{"light": 4, "composition": 8, "motion": 5, "scenery": 9, "subject": 8,
  "peak_offset": 0.75, "clip_type": "scenery", "crop_x": 50,
  "reason": "Canopy emerges revealing Manhattan skyline"}}

Rail-trail tunnel, symmetric dark interior with bright exit far away:
{{"light": 6, "composition": 7, "motion": 5, "scenery": 2, "subject": 2,
  "peak_offset": 0.9, "clip_type": "transition", "crop_x": 50,
  "reason": "Inside a dark tunnel — enclosed space, no sky"}}

Under NYC elevated tracks, symmetric girder geometry, no visible sky:
{{"light": 5, "composition": 7, "motion": 4, "scenery": 2, "subject": 3,
  "peak_offset": 0.5, "clip_type": "urban", "crop_x": 50,
  "reason": "Underpass geometry only — not scenery"}}

+++
name = "gemini_scan"
version = "v10"
model = "gemini-3.5-flash"
date = "2026-06-12"
commit = "uncommitted"
supersedes = "v9"
placeholders = ["n_frames", "power_zones", "telemetry_fields", "telemetry_examples"]
rationale = """
v10 changes the prompt contract from "find clips above a bar" to
"calibrate a distribution." The pipeline now sends periodic coverage
samples and keeps low-scored rubric results, so Gemini should score
ordinary/transit clips honestly instead of treating low scores as
failed outputs. Carries forward v9's openness gate, peak_offset,
rubric, and output schema.
"""
+++

You are editing a cycling highlight video. Select exactly {n_clips} clips
from the candidates below for a {budget_secs:.0f}-second highlight reel.

COUNT — READ THIS FIRST:
- Your answer MUST contain EXACTLY {n_clips} indices. Not fewer.
- The reel is {budget_secs:.0f} seconds at ~3 seconds per clip, so it needs
  all {n_clips} clips to fill the runtime. A short list leaves dead air that
  gets padded with worse clips — so returning fewer than {n_clips} makes the
  video WORSE, not tighter.
- If you think fewer clips tell a better story, you are wrong for THIS format:
  pick the {n_clips} best and order them well.

SELECTION RULES:
- Clips play in chronological ride order — pick clips that tell the ride's story as it unfolded.
- Ensure VARIETY in clip_type — no more than 3 of the same type.
- Spread selections across the ride timeline — don't cluster in one section.
- USER LABELS ARE EDITORIAL GROUND TRUTH. Any candidate whose sources include "label"
  with visual>=4 or action>=4 represents a moment the rider personally flagged as
  important. Strongly prefer these — include them unless doing so would badly break
  the variety/spread rules above. If you must drop one, prefer dropping gemini-only
  candidates instead.
- Prefer clips confirmed by multiple sources (telemetry + gemini + label).
- {layout_guidance}

CANDIDATES (sorted by ride time):
{candidate_text}

Return a JSON array of EXACTLY {n_clips} candidate indices, in the order they
should appear. Example shape (yours must have {n_clips} entries):
[2, 5, 9, 14, 17, 21, 26, 30, 33, 38, 41, 47, 52, 55, 58]
Return ONLY the JSON array, nothing else.

_PROXIMITY_LAMBDA = 6.0        # weight on the crowding penalty
_PROXIMITY_CROWDING_CAP = 2.0  # cap the sum so one big cluster can't lock out
                             # the whole neighborhood. Past two near-by picks
                             # the penalty stops growing.


def _proximity_crowding(t: float, picked_ts: list[float], tau: float) -> float:
  """Sum of exponential proximity weights against already-picked clips.

  crowding = min(Σ exp(-|t - t_i| / tau), _PROXIMITY_CROWDING_CAP).
  Adjacent picks contribute ~1.0, far-apart picks contribute ~0.
  Capped so a dense cluster of must_includes can't lock the greedy
  out of an entire region of the ride.
  """
  if not picked_ts:
      return 0.0
  raw = sum(math.exp(-abs(t - tp) / tau) for tp in picked_ts)
  return min(raw, _PROXIMITY_CROWDING_CAP)


def _diversity_tau(ride_times: list[float], n_clips: int) -> float:
  """Characteristic decay distance for the proximity penalty.

  Roughly the average gap between picks at full budget. A pick exactly
  at this distance contributes 1/e ≈ 0.37 of the crowding it would at
  zero distance.
  """
  if not ride_times or n_clips <= 0:
      return 60.0
  span = max(ride_times) - min(ride_times)
  return max(60.0, span / max(n_clips, 1))


# Applied when ranking each remaining candidate:
eff = seg.score + _label_boost(seg) - _PROXIMITY_LAMBDA * crowding