Migrate Legacy Codebases to Safe Rust

Act as a Systems Architect, Performance Engineer, and Rust Specialist. Your goal is to migrate legacy codebases into 100% Safe, PGO-tuned Rust while recording measured telemetry and a first-person engineering narrative via scripts/telemetry.py.

Execution Contract & Portability

When migrating a repository or local directory ({github_url} or {codebase_path}), follow these execution rules across agentic harnesses:

1. Stage 0 External Code Confirmation Gate: After cloning an external repository and inspecting its build files, present the resolved legacy build command, run command, and target directory to the user and obtain confirmation before compiling or executing cloned code. (For local code that the user explicitly instructed you to run, or once Stage 0 confirmation is granted, proceed directly.)
2. Continuous Stage 1 -> Stage 2 Progression: Once Stage 0 baseline capture is complete, proceed directly from writing the Architectural Blueprint (Stage 1) into implementing and verifying the Rust workspace (Stage 2) without pausing between milestones, where the host environment permits autonomous tool execution.
3. Bounded Automated Self-Healing (Max 5 Iterations): When tests, clippy lints, or A/B parity checks fail, log the failure via scripts/telemetry.py, diagnose and fix the root cause, and re-test automatically up to a budget of 5 self-healing iterations (or 15 minutes per phase). If any test or seed still diverges after the budget is exhausted, record the partial state in migration_telemetry.json and document the remaining gap in the final report rather than looping indefinitely.
4. Verification Targets & Definition of Done:
   • #![forbid(unsafe_code)] Rust crate compiles with a target of 0 cargo clippy warnings (any unavoidable exception must be documented).
   • Unit, integration, doc, and property-based (proptest) tests pass with a target of >= 95% branch coverage (any unavoidable exception must be documented), with every run logged in migration_telemetry.json.
   • A/B differential output parity is verified across N deterministic seeds within a documented numerical epsilon (--max-abs-error), with any floating-point/FMA/PRNG divergence root-caused and logged.
   • All 4 benchmark stages (legacy_baseline, safe_rust, soa_simd, pgo_lto) are measured and logged (or omitted cleanly if a tool such as llvm-profdata is unavailable).
   • The interactive HTML infographic (migration_infographic.html) is rendered via scripts/telemetry.py render-infographic.

End-to-End Workflow Checklist

Copy this checklist to track progress across the three stages (detailed command recipes and architectural specifications are in references/pgo_and_telemetry_guide.md):

- [ ] Stage 0: Acquire repo, initialize `migration_telemetry.json` (`discovery` bucket), scan legacy codebase, confirm resolved build/run commands, and benchmark baseline
- [ ] Stage 1: Audit legacy code (`blueprint` bucket), log opportunities & latent legacy bugs, and generate 6-section Architectural Blueprint
- [ ] Stage 2.1: Scaffold `#![forbid(unsafe_code)]` Rust workspace & deterministic PRNG parity layer (`rewrite` bucket)
- [ ] Stage 2.2: Implement core domain types & SoA/SIMD kernels (<= 400 LOC/file target, full rustdoc) & benchmark `safe_rust` + `soa_simd`
- [ ] Stage 2.3: Run Verification Crucible (`test_heal` bucket, max 5 iterations) — log initial test failure % and each self-healing fix
- [ ] Stage 2.4: Run A/B differential parity harness (`parity` bucket, max 5 iterations) across N seeds within documented epsilon tolerance
- [ ] Stage 2.5: Run LLVM Profile-Guided Optimization (`pgo_bench` bucket), scan final Rust codebase, and render `migration_infographic.html`

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Stage 0: Repository Acquisition, Telemetry Init & Legacy Baseline

Resolve context parameters from the repository or directory:

Parameter	Placeholder	Resolution Strategy
Source Input	{codebase_path} or {github_url}	Accept a GitHub URL (https://github.com/...) or a local filesystem path
Original Language / Framework	{legacy_language}	Auto-detect from file extensions and build files (CMakeLists.txt, Makefile, meson.build, pyproject.toml, pom.xml, *.f90, *.cpp)
Baseline Run Command	{baseline_run_command}	Identify from README.md, CI workflows (.github/workflows/*.yml), Makefile, or test scripts
Telemetry Ledger	{workspace_dir}/migration_telemetry.json	Initialized via scripts/telemetry.py init at the start of Stage 0

Stage 0 Execution Steps

1. Clone Repository (if GitHub URL): Choose a clean local directory (default to ~/rust_migrations/{repo_name}, or inside the current workspace if requested) and run git clone {github_url} ~/rust_migrations/{repo_name}.
2. Initialize Telemetry & Scan Legacy Codebase: Run scripts/telemetry.py init --telemetry "{workspace_dir}/migration_telemetry.json" --project-name "{project_name}" --legacy-lang "{legacy_language}". Start the discovery bucket and run scripts/telemetry.py scan-codebase --phase before on the legacy source directory to record baseline file count, code LOC, comment density %, max file LOC, nesting depth, average function length, and raw pointer/memory hazards.
3. Confirm & Benchmark Legacy Baseline: Before executing build or run commands from a cloned external repository, surface the resolved build command, {baseline_run_command}, and working directory for user confirmation. Once confirmed, compile the legacy project in release mode (-O3 / Release), capture golden reference outputs across deterministic seeds, measure baseline wall-clock runtime (ms) and peak RSS (MB), and log Stage 1 via scripts/telemetry.py log-benchmark --stage legacy_baseline.

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Stage 1: Deep Audit, Opportunity Discovery & Architectural Blueprint

Start the blueprint timer bucket (scripts/telemetry.py start-bucket --bucket blueprint), inspect the repository, log every discovered opportunity and latent legacy bug (scripts/telemetry.py log-opportunity), and generate the 6-section Markdown blueprint defined in Section 2 of references/pgo_and_telemetry_guide.md.

Stage 1 -> Stage 2 Transition: Save the blueprint and proceed directly to Stage 2 (unless the user explicitly requested a blueprint-only plan).

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Stage 2: Phased Safe Rust Implementation, Self-Healing, PGO & Infographic

Follow the command recipes and core engineering standards in references/pgo_and_telemetry_guide.md for each step:

Step 1: Scaffold Safe Rust Workspace & Port Modules (rewrite Bucket)

1. Start the rewrite timer bucket (scripts/telemetry.py start-bucket --bucket rewrite).
2. Initialize the Rust workspace with #![forbid(unsafe_code)] in src/lib.rs and src/main.rs.
3. Implement the deterministic PRNG wrapper, domain types, and modules in dependency order (<= 400 LOC per file target, complete //! and /// rustdoc with runnable doc-tests).
4. Benchmark and log Stage 2 (safe_rust) and Stage 3 (soa_simd) via scripts/telemetry.py log-benchmark.

Step 2: The Verification Crucible — Test, Fail, Diagnose & Fix (test_heal Bucket)

1. Start the test_heal timer bucket (scripts/telemetry.py start-bucket --bucket test_heal).
2. Write unit tests, runnable doc-tests, and proptest boundary tests.
3. Run cargo test --all-targets, cargo test --doc, and cargo clippy --all-targets.
4. Log Every Iteration (Up to 5 Iterations): After Run 1 and each subsequent run, invoke scripts/telemetry.py log-test-run with total tests, passed, failed, coverage %, clippy warnings, diagnostic summary, and fix applied. Iterate until 0 failures and 0 warnings (or until the 5-iteration budget is reached, documenting any unavoidable exception).

Step 3: A/B Differential Output Parity (parity Bucket)

1. Start the parity timer bucket (scripts/telemetry.py start-bucket --bucket parity).
2. Run the automated A/B differential harness across N deterministic seeds comparing {baseline_run_command} and the Rust binary within the documented epsilon tolerance.
3. Log each parity iteration via scripts/telemetry.py log-parity-run. If any seed diverges beyond epsilon, bisect the first diverging step, repair the discrepancy, and re-run (up to 5 iterations).

Step 4: Profile-Guided Optimization (PGO) & Final Benchmarks (pgo_bench Bucket)

1. Start the pgo_bench timer bucket (scripts/telemetry.py start-bucket --bucket pgo_bench).
2. Execute the LLVM PGO workflow in references/pgo_and_telemetry_guide.md: build with RUSTFLAGS="-Cprofile-generate=./pgo-data", run representative workloads, merge profiles via llvm-profdata, and rebuild with RUSTFLAGS="-Cprofile-use=./pgo-data/merged.profdata" and lto = "fat" (or use the documented -Ctarget-cpu=native fallback if llvm-profdata is unavailable).
3. Verify that tests and A/B parity still pass on the PGO binary, then measure wall-clock runtime and peak RSS and log Stage 4 (pgo_lto) via scripts/telemetry.py log-benchmark --stage pgo_lto.

Step 5: Final Census, First-Person AI Story & Visual Infographic

1. Run scripts/telemetry.py scan-codebase --phase after on src/ to capture final LOC, <= 400 LOC compliance, comment/rustdoc density, runnable doc-tests, nesting depth, and unsafe block count.

2. Log a Before vs. After Code Spotlight (scripts/telemetry.py log-spotlight) and populate the First-Person AI Story Chapters (scripts/telemetry.py set-story).

3. End the pgo_bench timer bucket and render the interactive HTML infographic (where {skill_dir} is the directory containing this SKILL.md):

   python3 "{skill_dir}/scripts/telemetry.py" render-infographic \
     --telemetry "{workspace_dir}/migration_telemetry.json" \
     --out "{output_dir}/migration_infographic.html" \
     --scope-summary "{short_summary_of_ported_modules}"
   

4. Output a concise executive summary referencing the generated migration_infographic.html report and key measured before/after metrics.

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Bundled Resources

• Telemetry & Infographic CLI: scripts/telemetry.py
• Engineering Standards, Blueprint Spec, PGO & Telemetry Recipes: references/pgo_and_telemetry_guide.md
• Interactive HTML Infographic Template: references/infographic_template.html