shift-preflight-cli 0.1.0

What it does

SHIFT sits between your application and the model API. Every request passes through a pipeline that inspects, evaluates, and transforms media inputs so they conform to provider constraints.

Before SHIFT: oversized images, unsupported formats, and bloated payloads cause hard failures (400 errors, token waste, session crashes).

After SHIFT: every request is valid, optimized, and tuned to your cost/quality preference.

Token savings

SHIFT estimates per-provider token savings for every run. Both OpenAI and Anthropic charge tokens based on image dimensions — resizing images before they hit the API reduces cost.

Token estimates based on published provider formulas. OpenAI uses tile-based counting (GPT-4o/4.1 family, 512×512 tiles); Anthropic uses pixel-based (w×h/750, 1568px long-edge cap for standard models). Actual billing may vary by model — newer OpenAI models use patch-based counting, and Anthropic Opus 4.7 supports higher resolution (2576px, 4784 max tokens).

Sample report

$ cat request.json | shift -m economy -o report

=== SHIFT Report ===
Images found:      1
Images modified:   1
Images dropped:    0
Original size:     42262 bytes
Transformed size:  17018 bytes
Size reduction:    59.7%

Token Savings (estimated):
  OpenAI:    765 -> 765 tokens  (0.0% saved)
  Anthropic: 1,568 -> 1,082 tokens  (31.0% saved)

Per-image breakdown:
  [0] 4000x3000 -> 1024x768  (OpenAI: 765 -> 765, Anthropic: 1,568 -> 1,082)

Actions:
  [image 0] resize — 4000x3000 -> 1024x768

Cumulative tracking

SHIFT automatically records run statistics to ~/.shift/stats.jsonl. View cumulative savings with shift gain:

$ shift gain

=== SHIFT Cumulative Savings ===

Runs:     42
Images:   156 processed, 89 modified
Bytes:    247.3 MB saved

Token Savings (estimated):
  OpenAI:    52,400 -> 12,300 tokens  (76.5% saved)
  Anthropic: 84,200 -> 28,100 tokens  (66.6% saved)

$ shift gain --daily

=== SHIFT Daily Token Savings ===

Date          Runs  Images    OpenAI saved Anthropic saved
----------------------------------------------------------
2026-04-20       8      24           3,200           5,400
2026-04-21      12      42           4,800           8,200
2026-04-22      22      90          12,100          18,500

Use shift gain --format json for machine-readable output.

Install

From crates.io

cargo install shift-preflight-cli

From GitHub (latest)

cargo install --git https://github.com/alohaninja/shift shift-preflight-cli

From source

git clone https://github.com/alohaninja/shift.git
cd shift
cargo install --path shift-cli

Build without installing

git clone https://github.com/alohaninja/shift.git
cd shift
cargo build --release
# Binary at target/release/shift

Pre-built binaries

Download from GitHub Releases — macOS (x86/arm) and Linux (x86/arm).

Verify installation

shift --version
shift --help

# Quick validation — transform a sample payload
echo '{"model":"gpt-4o","messages":[{"role":"user","content":"hello"}]}' | shift

# Check stats tracking
shift gain

Quick start

# Transform an OpenAI request (stdin/stdout pipe)
cat request.json | shift -p openai -m balanced > safe_request.json

# Transform an Anthropic request from a file
shift request.json -p anthropic -m economy > safe_request.json

# See what would change without modifying anything
shift request.json --dry-run -o report

# Compose with curl
shift request.json -p openai | curl -s -X POST \
  https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @-

Options

shift [OPTIONS] [FILE]

Arguments:
  [FILE]  Input file (JSON request payload). Reads stdin if omitted.

Options:
  -p, --provider <PROVIDER>  Target provider [default: openai]
                              [openai, anthropic, claude]
  -m, --mode <MODE>          Drive mode [default: balanced]
                              [performance, balanced, economy]
      --svg-mode <MODE>      SVG handling [default: raster]
                              [raster, source, hybrid]
  -o, --output <FORMAT>      Output format [default: json]
                              [json, report, json-report, both]
      --dry-run              Show what would change without modifying
      --profile <FILE>       Custom provider profile JSON
      --model <MODEL>        Target model (overrides model in payload)
      --no-stats             Disable saving run statistics
  -v, --verbose              Verbose output

Commands:
  shift gain                 Show cumulative token savings
  shift gain --daily         Day-by-day breakdown
  shift gain --format json   Machine-readable output for dashboards

Drive modes

SVG handling

Most AI model APIs reject SVG. SHIFT detects SVG inputs and handles them based on --svg-mode:

Supported formats

Detected and processed:

BMP and TIFF are auto-converted to PNG. SVGs are rasterized. Everything else passes through if it meets provider constraints.

Provider profiles

Built-in constraints for the two major multimodal providers:

Profiles include per-model overrides (gpt-4o, gpt-4.1, claude-sonnet-4, etc.) and fall back to provider defaults for unknown models.

Custom profiles can be loaded with --profile custom.json.

Library usage

SHIFT is split into two crates: shift-preflight (library) and shift-preflight-cli (binary). The library can be used directly in Rust applications:

# Cargo.toml
[dependencies]
shift-preflight = "0.1"

use shift_preflight::{pipeline, ShiftConfig, DriveMode};
use serde_json::json;

let payload = json!({
    "model": "gpt-4o",
    "messages": [{
        "role": "user",
        "content": [{
            "type": "image_url",
            "image_url": {"url": "data:image/png;base64,..."}
        }]
    }]
});

let config = ShiftConfig {
    mode: DriveMode::Balanced,
    provider: "openai".to_string(),
    ..Default::default()
};

let (safe_payload, report) = pipeline::process(&payload, &config).unwrap();
eprintln!("{}", report); // what changed and why

How it works

1. Inspect -- Detect every image in the request payload. Extract format (via magic bytes), dimensions, file size, encoding type. Handles base64 data URIs, raw base64, and URL references (fetched automatically).

2. Evaluate -- Load the provider profile for the target API. Compare each image's metadata against the constraints. Apply mode-specific rules to determine what actions are needed (resize, recompress, convert, rasterize, drop).

3. Transform -- Execute the actions. Resize preserves aspect ratio using Lanczos3 filtering. SVGs are rasterized with resvg (supports gradients, text, viewBox). BMP/TIFF are converted to PNG. JPEG recompression uses mode-tuned quality levels.

4. Reconstruct -- Rebuild the original payload with transformed images slotted back in. Output is a valid JSON request ready to send to the API.

Project structure

shift/
├── shift-core/          Library crate: shift-preflight (all processing logic)
│   └── src/
│       ├── inspector/   Format detection, metadata extraction
│       ├── policy/      Provider profiles, constraint evaluation, rules
│       ├── transformer/ Image resize, recompress, SVG rasterize, convert
│       ├── payload/     OpenAI + Anthropic message format parse/reconstruct
│       ├── pipeline.rs  Orchestrator: inspect -> policy -> transform
│       ├── cost.rs      Token estimation (OpenAI tile, Anthropic pixel)
│       ├── stats.rs     Persistent run statistics, gain summaries
│       ├── report.rs    Transformation report with token savings
│       └── mode.rs      DriveMode, SvgMode, ShiftConfig
├── shift-cli/           Binary crate: shift-preflight-cli (CLI + gain subcommand)
├── profiles/            Provider constraint JSON (embedded at compile time)
├── tests/
│   ├── fixtures/        Test images and sample payloads
│   └── docker/          Dockerfiles for cross-distro CI (Ubuntu, Arch)
└── .github/workflows/   CI + Linux distro tests

Roadmap (v2+)

• Video: frame sampling, keyframe extraction, resolution downscale
• Audio: compression, transcription to text
• Documents: chunking, summarization, text extraction
• Smart image selection: near-duplicate detection, keep most informative
• Caption fallback: replace low-value images with text descriptions
• Adaptive policies: dynamic adjustment based on request size and latency targets

License

Apache-2.0