pack-light

name

pack-light

description

Analyze an application's source code and host hardware to recommend the smallest possible packaging strategy. Collects hardware specs via system commands, performs full dependency tree and CVE analysis, and produces packaging recommendations with generated Dockerfiles, CI/CD pipelines, benchmarking commands, and secret hygiene checks. Activates when the user says "pack light", "optimize this app", "shrink this", "smallest footprint", "analyze the application", or provides an application for deployment optimization.

Pack Light

Analyze an application's codebase and the hardware it will run on, then recommend the leanest packaging strategy — Docker image, standalone binary, or both. Produces generated build configs and benchmarking commands so the user can validate results immediately.

When to Activate

Manual triggers:

• "Pack light"
• "Optimize this app for size"
• "Shrink this" / "make this smaller"
• "Smallest footprint"
• "Analyze the application" / "analyze this app"
• "What's the best way to package this?"
• "Help me containerize this efficiently"

Auto-detect triggers:

• User provides an application and asks about deployment or packaging
• User mentions Docker image size being too large
• User is deploying to resource-constrained hardware (Pi, small VPS, edge device)
• User asks "how do I make this run on less?"

Process

Three sequential phases. Do not skip phases or run them out of order.

---

Phase 1: Hardware Recon

Collect system specifications to understand the target deployment environment. This determines what packaging strategies are viable.

1.1 Detect the Current Environment

Run system commands automatically to gather specs. Do not ask permission — just collect the data.

macOS detection (default assumption if OS unclear):

# OS and architecture
sw_vers 2>/dev/null && uname -m

# CPU
sysctl -n machdep.cpu.brand_string 2>/dev/null
sysctl -n hw.ncpu 2>/dev/null

# Memory (bytes → GB)
sysctl -n hw.memsize 2>/dev/null

# Disk (available space)
df -h / 2>/dev/null | tail -1

# Docker availability
docker info --format '{{.OSType}}/{{.Architecture}}' 2>/dev/null || echo "Docker not available"
docker info --format 'Docker Memory: {{.MemTotal}}' 2>/dev/null

Linux detection:

# OS
cat /etc/os-release 2>/dev/null | head -5
uname -m

# CPU
lscpu 2>/dev/null | grep -E "Model name|CPU\(s\)|Architecture"

# Memory
free -h 2>/dev/null | head -2

# Disk
df -h / 2>/dev/null | tail -1

# Docker
docker info --format '{{.OSType}}/{{.Architecture}}' 2>/dev/null || echo "Docker not available"

1.2 Ask About Target Environment

After collecting specs, ask the user ONE question:

"I've collected your current system specs. Is this also the target deployment environment, or will this run somewhere else?"

If somewhere else, ask targeted follow-ups to fill in:

• Target OS and architecture (amd64, arm64, armv7)
• Available RAM on the target
• Available disk on the target
• Whether Docker is available on the target
• Any hard constraints (e.g., "must run in under 256MB RAM", "no Docker available")

1.3 Produce the Hardware Profile

Summarize findings in a structured block:

HARDWARE PROFILE
────────────────────────────────
Build machine:    [OS] / [arch] / [CPU cores] / [RAM] / [disk free]
Target machine:   [OS] / [arch] / [CPU cores] / [RAM] / [disk free]
Docker available: [yes/no] (build) / [yes/no] (target)
Constraints:      [any hard limits stated by user, or "none"]
────────────────────────────────

---

Phase 2: Code Analysis

Analyze the application source to understand its language, framework, dependency weight, and runtime characteristics.

2.1 Locate the Source

If the user has not provided source code or a path:

"I need to see the application source. Can you provide the path to the project root, a GitHub repo URL, or upload the source files?"

If the user provides a path or URL, read the project structure.

2.2 Identify Language and Framework

Read project manifest files to detect the stack:

Signal File	Language / Framework
package.json	Node.js (check for Express, Fastify, Hono, Next.js, etc.)
package.json + bun.lockb	Bun runtime
deno.json / deno.lock	Deno runtime
pyproject.toml / requirements.txt / setup.py	Python (check for Flask, FastAPI, Django)
go.mod	Go
Cargo.toml	Rust
pom.xml / build.gradle	Java / Kotlin
mix.exs	Elixir
Gemfile	Ruby

If the stack is ambiguous (polyglot repo, no clear entrypoint), ask targeted follow-ups:

"This looks like a polyglot repo. Which is the primary application you want packaged — the [X] service or the [Y] service?"

2.3 Dependency Tree Analysis

Scan the full dependency tree for weight and optimization opportunities.

Node.js:

# Total node_modules size
du -sh node_modules/ 2>/dev/null

# Count of direct vs transitive deps
jq '.dependencies | length' package.json 2>/dev/null
jq '.devDependencies | length' package.json 2>/dev/null

# Heaviest packages
du -sh node_modules/*/ 2>/dev/null | sort -rh | head -20

Python:

# Installed package sizes
pip list --format=columns 2>/dev/null
pip show <heavy-suspects> 2>/dev/null

# Total site-packages size
du -sh $(python -c "import site; print(site.getsitepackages()[0])") 2>/dev/null

Go:

# Module dependency count
go list -m all 2>/dev/null | wc -l

# Binary size (if already built)
ls -lh <binary> 2>/dev/null

# Build with size flags
go build -ldflags="-s -w" -o /tmp/test-build . 2>/dev/null && ls -lh /tmp/test-build

Rust:

# Dependency count
cargo tree 2>/dev/null | wc -l

# Release binary size
cargo build --release 2>/dev/null && ls -lh target/release/<binary>

2.4 Identify Heavy Dependencies

Flag dependencies that contribute disproportionate weight. For each one, note:

• Package name and size
• What it's used for in the codebase (grep for imports)
• Whether a lighter alternative exists

2.5 Analyze Runtime Characteristics

Determine what the application actually needs at runtime:

• Does it need a full OS filesystem, or just a binary + config?
• Does it use native extensions or system libraries (e.g., sharp, canvas, libpq)?
• Does it need a writable filesystem at runtime?
• Does it spawn child processes or shells?
• Does it need TLS certificates or CA bundles?
• Does it bind to network ports?

2.6 Security Scan

Before recommending a base image or dependency set, scan for known vulnerabilities.

Base image CVE check:

# If Docker is available, scan candidate base images
docker scout cves <candidate-base-image> 2>/dev/null || \
  echo "Docker Scout not available — note in report"

Dependency vulnerability check:

# Node.js
npm audit --production 2>/dev/null

# Python
pip-audit 2>/dev/null || pip install pip-audit --break-system-packages -q && pip-audit

# Go
govulncheck ./... 2>/dev/null

# Rust
cargo audit 2>/dev/null

Flag any critical or high severity CVEs. If a recommended base image has known CVEs, note it in the report and suggest a patched alternative. Do not recommend base images with unpatched critical vulnerabilities.

2.7 Detect Existing Packaging

Check if the project already has packaging configuration:

# Check for existing Dockerfile(s)
find . -name "Dockerfile*" -not -path '*/.git/*' 2>/dev/null

# Check for docker-compose
find . -name "docker-compose*" -not -path '*/.git/*' 2>/dev/null

# Check for .dockerignore
test -f .dockerignore && echo ".dockerignore exists" || echo "No .dockerignore"

If an existing Dockerfile is found, read it and store its contents for comparison in Phase 3. This enables the Comparison Mode output.

2.8 Produce the Code Profile

CODE PROFILE
────────────────────────────────
Language:           [language] [version]
Framework:          [framework] [version]
Entrypoint:         [path to main file]
Direct deps:        [count]
Transitive deps:    [count]
Dependency weight:  [total size of deps]
Native extensions:  [list or "none"]
Runtime needs:      [filesystem / network / child processes / etc.]
Top 5 heaviest:     [name: size, name: size, ...]
Known CVEs:         [critical: X, high: X, medium: X, or "clean"]
Existing packaging: [Dockerfile found / none]
────────────────────────────────

---

Phase 3: Recommendation + Deliverables

Combine the hardware profile and code profile to recommend the optimal packaging strategy.

3.1 Ask for Optimization Weighting and Size Budget

Before recommending, ask:

"I can optimize for three metrics. How would you weight them?"

1. Image/artifact size (smallest possible output)
2. Startup time (fastest cold start)
3. Runtime memory (lowest RAM at steady state)

Options: Balanced (default), or tell me which to prioritize.

"Do you have a target size budget? (e.g., 'under 100MB', 'under 50MB') If not, I'll optimize as aggressively as the stack allows."

If the user sets a size budget, track it throughout Phase 3. If the recommended strategy cannot meet the budget, flag it explicitly in the report with an explanation of what's preventing it and what tradeoffs would be required to hit it.

3.2 Select Packaging Strategy

Evaluate both Docker and standalone binary options, then recommend the best fit.

Docker Image Strategy Matrix:

Scenario	Base Image Recommendation
Node.js, no native deps	node:{version}-alpine or distroless
Node.js, native deps (sharp, canvas, etc.)	node:{version}-slim with explicit lib installs
Python, minimal deps	python:{version}-alpine or python:{version}-slim
Python, heavy ML/data deps	python:{version}-slim (alpine breaks many wheels)
Go	scratch or gcr.io/distroless/static (static binary)
Rust	scratch or gcr.io/distroless/cc (if libc needed)

Standalone Binary Strategy Matrix:

Scenario	Approach
Go	CGO_ENABLED=0 go build -ldflags="-s -w" → static binary
Rust	cargo build --release + strip → static binary
Node.js	pkg, bun build --compile, or deno compile
Python	PyInstaller --onefile or Nuitka

Decision criteria:

• If Docker is available on target → prefer Docker (better isolation, reproducibility)
• If target is constrained (<512MB RAM, ARM edge device) → prefer standalone binary
• If app has many native deps → prefer Docker (dependency management)
• If app is pure Go/Rust → prefer standalone binary (smaller, no runtime needed)
• If startup time is critical → prefer standalone binary (no container overhead)

3.3 Generate Build Configuration

Based on the selected strategy, generate production-ready files:

For Docker: Generate a multi-stage Dockerfile following these principles:

1. Build stage: Full SDK/toolchain image. Install deps, compile, test.
2. Production stage: Minimal base image. Copy only the built artifact and runtime dependencies.
3. Use .dockerignore to exclude node_modules/, .git/, dist/, test files.
4. Pin base image versions (never use :latest).
5. Order layers by change frequency (deps first, source last) for cache efficiency.
6. Run as non-root user.
7. Set HEALTHCHECK if the app serves HTTP.

Include a .dockerignore file.

For standalone binary: Generate the appropriate build script or Makefile with:

1. Optimized compiler flags (strip symbols, LTO, size optimization)
2. Cross-compilation commands if build and target architectures differ
3. Post-build stripping (UPX compression if appropriate)

3.4 Multi-Architecture Support

Check if the build and target architectures differ, or if the user needs to deploy to multiple architectures (e.g., amd64 + arm64).

If multi-arch is needed, generate docker buildx commands:

# Create a multi-arch builder (one-time setup)
docker buildx create --name pack-light-builder --use 2>/dev/null

# Build for multiple platforms
docker buildx build \
  --platform linux/amd64,linux/arm64 \
  -t <image-name>:<tag> \
  --push \
  .

If standalone binary, generate cross-compilation commands per-language:

• Go: GOOS=linux GOARCH=arm64 CGO_ENABLED=0 go build ...
• Rust: cross build --target aarch64-unknown-linux-musl --release
• Node.js (Bun): bun build --compile --target=bun-linux-arm64 ...

Note: Always specify target platforms explicitly. Never assume the build machine's architecture matches the deployment target.

3.5 Secret Hygiene Check

Before finalizing any generated Dockerfile or build config, audit it for credential exposure:

Check for leaked secrets:

• No ENV directives that set API keys, tokens, passwords, or connection strings
• No COPY of .env, .env.local, .env.production, or similar files
• No ARG values containing secrets that persist in layer metadata
• No hardcoded credentials in RUN commands (e.g., curl -H "Authorization: ...")
• .dockerignore explicitly excludes .env*, *.pem, *.key, .git/

Best practices to enforce in generated files:

• Secrets passed at runtime via --env-file or orchestrator (Compose, K8s)
• Build-time secrets use --mount=type=secret (BuildKit) instead of ARG
• Multi-stage builds ensure build-only credentials do not carry to production stage

If any secret hygiene issues are found in an existing Dockerfile (from Phase 2.7), flag them as Critical findings in the report.

3.6 Runtime Healthcheck Tuning

If the application serves HTTP or exposes a health endpoint, generate a tailored HEALTHCHECK instruction:

1. Scan the source for health/readiness endpoints (common patterns: /health, /healthz, /ready, /ping, /api/health)
2. If found, generate a HEALTHCHECK that hits the actual endpoint:

   HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \
     CMD wget --no-verbose --tries=1 --spider http://localhost:<port>/<endpoint> || exit 1
   

3. If no health endpoint exists, suggest adding one and generate a minimal implementation appropriate to the framework
4. Tune --start-period based on the application's expected startup time (informed by Phase 2 runtime analysis and the user's optimization weighting)

Prefer wget --spider over curl in the Dockerfile healthcheck to avoid adding curl to minimal images. If the base image has neither, use a language-native check script.

3.7 Comparison Mode

If an existing Dockerfile was found in Phase 2.7, produce a side-by-side comparison:

EXISTING vs OPTIMIZED
────────────────────────────────────────────────────
                    Existing          Optimized
Base image:         [image]           [image]
Stages:             [count]           [count]
Est. image size:    [size]            [size]
Non-root user:      [yes/no]          [yes/no]
.dockerignore:      [yes/no]          [yes/no]
Layer count:        [count]           [count]
Secret hygiene:     [pass/fail]       [pass]
Healthcheck:        [yes/no]          [yes/no]
Multi-arch ready:   [yes/no]          [yes/no]
────────────────────────────────────────────────────

Below the comparison table, provide a bulleted list of the specific changes made and the rationale for each. This lets the user understand exactly what was improved and why, rather than just receiving a new Dockerfile with no context on what changed.

3.8 Generate CI/CD Pipeline

Produce a GitHub Actions workflow that builds the optimized image or binary:

# .github/workflows/pack-light-build.yml
name: Pack Light Build

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      # Docker builds
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

      - name: Build image
        uses: docker/build-push-action@v6
        with:
          context: .
          push: false
          tags: <image-name>:${{ github.sha }}
          cache-from: type=gha
          cache-to: type=gha,mode=max

      # Size gate (if budget set)
      - name: Check image size
        run: 

Customize the workflow based on:

• Docker strategy: Include buildx, multi-platform if needed, cache layers
• Binary strategy: Include appropriate toolchain setup, cross-compilation
• Size budget: If set, add a size gate step that fails the build if exceeded
• Security: Include a docker scout or trivy scan step if CVEs were found in Phase 2.6

3.9 Generate Benchmarking Commands

Produce a set of commands the user can run immediately to validate the result:

# ── Image / Artifact Size ──
docker images <image-name> --format "{{.Size}}"
# or: ls -lh <binary>

# ── Startup Time ──
time docker run --rm <image-name> --health-check
# or: time ./<binary> --version

# ── Runtime Memory ──
docker stats --no-stream <container-name>
# or: /usr/bin/time -v ./<binary> (Linux)
# or: /usr/bin/time -l ./<binary> (macOS)

# ── Layer Analysis (Docker) ──
docker history <image-name>
# Recommend: dive <image-name> (if dive is installed)

# ── Before vs After Comparison ──
echo "Compare these numbers against your current build."

3.10 Produce the Final Report

Write the deliverable in this structure:

# Pack Light Report: [Application Name]

**Date:** [today's date]
**Analyst:** Claude (Pack Light)
**Application:** [name] ([language] / [framework])
**Optimization weighting:** [user's stated priorities]
**Size budget:** [target or "none set"]

---

## Hardware Profile

[from Phase 1]

## Code Profile

[from Phase 2, including CVE summary]

---

## Security Findings

[CVE summary from Phase 2.6. Flag any critical/high findings.]
[Secret hygiene issues from Phase 3.5, if any.]

---

## Recommendation: [Docker Multi-Stage / Standalone Binary / Both]

> One paragraph explaining why this strategy is optimal given the hardware
> profile, code profile, and optimization weighting.

### Estimated Footprint

| Metric | Current (est.) | Optimized (est.) | Reduction | Budget |
|---|---|---|---|---|
| Image / artifact size | [X] | [Y] | [Z%] | [pass/fail/n/a] |
| Startup time | [X] | [Y] | [Z%] | — |
| Runtime memory | [X] | [Y] | [Z%] | — |

---

## Comparison with Existing Packaging (if applicable)

[from Phase 3.7 — only include if existing Dockerfile was found]

---

## Generated Files

### Dockerfile (or build script)

[complete, copy-pasteable file]

### .dockerignore (if Docker)

[complete file]

### .github/workflows/pack-light-build.yml

[from Phase 3.8]

---

## Benchmarking Commands

[from Phase 3.9]

---

## Dependency Optimization Opportunities (Optional)

> This section identifies code-level changes that could further reduce
> the footprint. These are suggestions, not requirements.

| Current Package | Size | Lighter Alternative | Estimated Savings | Migration Effort |
|---|---|---|---|---|
| [package] | [size] | [alternative] | [savings] | [low/medium/high] |

[For each suggestion, provide a one-sentence rationale.]

---

## Next Steps

1. [First concrete action]
2. [Second concrete action]
3. [Third concrete action]

---

Rules

1. Always run hardware recon first. Never skip to code analysis. The hardware profile directly influences which packaging strategies are viable.

2. Never guess the language or framework. Read manifest files. If ambiguous, ask. Getting the stack wrong invalidates the entire recommendation.

3. Default to macOS assumptions when running in a Claude environment where system commands return Linux specs but the user hasn't specified their target. Ask to confirm.

4. Pin versions in all generated files. Never use :latest, *, or unpinned dependencies in Dockerfiles or build configs.

5. The Dependency Optimization section is always optional. Present it, but frame it as "further opportunities" — not as required changes. The user asked for packaging optimization, not a code rewrite.

6. Every estimate must be labeled as an estimate. Do not state projected image sizes or memory usage as facts. Use "est." or "~" consistently.

7. Generate complete, copy-pasteable files. The Dockerfile and build configs must work as-is. No [fill in your ...] placeholders. Use actual values from the code analysis.

8. Always include benchmarking commands. The user should be able to validate every claim in the report by running the provided commands.

9. If Docker is not available on the target, do not recommend Docker. This seems obvious, but it's a common failure mode. Check Phase 1 results before Phase 3 recommendations.

10. Respect the user's optimization weighting. If they said "prioritize startup time," don't recommend a strategy that optimizes for size at the expense of cold start performance.

11. Never generate files that bake secrets into image layers. No ENV with credentials, no COPY .env, no ARG with tokens. Secrets go in at runtime. If an existing Dockerfile violates this, flag it as Critical.

12. If a size budget is set, enforce it. The report must explicitly state whether the recommendation meets or exceeds the budget. If it exceeds it, explain what's preventing it and what tradeoffs would close the gap.

13. Always generate a CI/CD workflow. Every recommendation includes a GitHub Actions workflow. The workflow must include cache optimization and a size gate step if a budget was set.

---

Examples

Good Example

Input: User provides a Node.js Express API with 47 direct dependencies, running on a 2GB RAM VPS. Existing Dockerfile uses node:22 with no multi-stage build.

Output: Multi-stage Dockerfile using node:22-alpine build stage → gcr.io/distroless/nodejs22-debian12 production stage. .dockerignore excludes test files, docs, and .git. Report shows estimated image size reduction from ~950MB (default node:22) to ~130MB. Comparison table shows the existing Dockerfile runs as root, has no healthcheck, and copies .env into the image (flagged as Critical secret hygiene issue). Security scan finds 2 medium CVEs in dependencies, noted in findings. GitHub Actions workflow includes buildx with GHA cache and a 200MB size gate. Healthcheck hits /api/health detected in the source. Benchmarking commands include docker history, docker stats, and a startup time test. Dependency section notes that moment (4.2MB) could be replaced with dayjs (6KB) since only format() and diff() are used.

Bad Example (what to avoid)

Input: Same Node.js Express API.

Bad output: "Use Alpine and multi-stage builds. Here's a generic Dockerfile template with [YOUR_APP_NAME] placeholders. You should probably also look into reducing your dependencies."

This fails because:

• Dockerfile has placeholders instead of real values from the code analysis
• No hardware profile was collected
• No dependency tree was actually scanned
• No benchmarking commands
• No estimated footprint comparison
• No security scan or secret hygiene check
• No CI/CD workflow generated
• No comparison with the existing Dockerfile
• Vague advice instead of specific, actionable recommendations

---

Chaining

• codebase-review → pack-light: After a codebase review identifies performance or deployment issues, pack-light can optimize the packaging.
• pack-light → workflow-lock: If the user packages the same type of application repeatedly, lock the pack-light output into a reusable template.
• decompose → pack-light: For monorepos or multi-service architectures, decompose can break the system into individual services, then pack-light optimizes each one.