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tooling improvements

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John Alanbrook
2026-02-17 13:37:17 -06:00
parent 2e78e7e0b8
commit c02fbbd9e0
10 changed files with 371 additions and 186 deletions
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docs/compiler-tools.md
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@@ -81,9 +81,37 @@ Shows the optimized IR with type annotations. Each instruction is followed by th
Runs the full pipeline (tokenize, parse, fold, mcode, streamline) and outputs the optimized IR as JSON. Useful for piping to `jq` or saving for comparison.
```bash
./cell --core . streamline.ce <file.ce|file.cm>
./cell --core . streamline.ce <file.ce|file.cm> # full JSON (default)
./cell --core . streamline.ce --stats <file.ce|file.cm> # summary stats per function
./cell --core . streamline.ce --ir <file.ce|file.cm> # human-readable IR
./cell --core . streamline.ce --check <file.ce|file.cm> # warnings only
```
| Flag | Description |
|------|-------------|
| (none) | Full optimized IR as JSON (backward compatible) |
| `--stats` | Per-function summary: args, slots, instruction counts by category, nops eliminated |
| `--ir` | Human-readable canonical IR (same format as `ir_report.ce`) |
| `--check` | Warnings only (e.g. `nr_slots > 200` approaching 255 limit) |
Flags can be combined.
## seed.ce
Regenerates the boot seed files in `boot/`. These are pre-compiled mcode IR (JSON) files that bootstrap the compilation pipeline on cold start.
```bash
./cell --core . seed.ce # regenerate all boot seeds
./cell --core . seed.ce --clean # also clear the build cache after
```
The script compiles each pipeline module (tokenize, parse, fold, mcode, streamline) and `internal/bootstrap.cm` through the current pipeline, encodes the output as JSON, and writes it to `boot/<name>.cm.mcode`.
**When to regenerate seeds:**
- Before a release or distribution
- When the pipeline source changes in a way the existing seeds can't compile the new source (e.g. language-level changes)
- Seeds do NOT need regenerating for normal development — the engine recompiles pipeline modules from source automatically via the content-addressed cache
## ir_report.ce
The optimizer flight recorder. Runs the full pipeline with structured logging and outputs machine-readable, diff-friendly JSON. This is the most detailed tool for understanding what the optimizer did and why.