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name: sk2decompile description: "Run the SK²Decompile two-phase skeleton/skin pipeline, RL helpers, and BringUpBench evaluation." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

SK²Decompile

Use this sub-skill when the user wants the repo's two-phase binary-decompilation workflow: structure recovery, identifier naming, RL reward helpers, two-stage inference, or BringUpBench evaluation.

Covers

• pseudo-code normalization and obfuscation helpers
• header/type inference for structure-recovery training data
• function-map generation across source, pseudo-code, and assembly
• two-stage skeleton/skin inference
• GRPO / RL helper scripts and reward modules
• BringUpBench evaluation and report generation

Excludes

• direct LLM4Decompile training and inference → use training or evaluation
• Ghidra-only refinement flow → use ghidra-refine
• plain benchmark scoring without the two-stage pipeline → use evaluation

Start Here

1. Read references/workflows.md for the stage map.
2. Read references/data-formats.md before changing JSONL/Parquet layouts.
3. Read references/reward-functions.md if the user asks about RL rewards.
4. Read references/bringupbench.md when the task touches the benchmark pipeline.
5. Read references/troubleshooting.md if normalization, reward services, or benchmark checkout paths fail.

Common routes

Preprocessing / normalization

Use this route when the user wants to normalize pseudo-code, build function maps, or infer headers for RL data.

Good entry points:

• scripts/normalize_pseudo.py
• scripts/infer_type.py
• scripts/build_func_maps.py
• scripts/disasm_all_objdump.sh

Two-stage inference

Use this route when the user wants to run the structure-recovery model and then the identifier-naming model.

Good entry points:

• scripts/sk2decompile_inf.py

RL reward helpers

Use this route when the user wants to inspect, adapt, or debug the reward modules used by VERL / GRPO.

Good entry points:

• scripts/reward_functions/exe_type.py
• scripts/reward_functions/sim_exe.py
• scripts/reward_functions/embedding_gte.py
• scripts/reward_functions/embedding_qwen3.py
• scripts/run_struct_rl.sh
• scripts/run_ident_rl.sh

BringUpBench evaluation

Use this route when the user wants to rebuild a benchmark workspace and score replacement / compile / execution rates.

Good entry points:

• scripts/eval_infer_out.py

Environment signals

• clang-format is required for normalization.
• vllm / torch CUDA is required for the stage models.
• Psychec is optional but needed for header inference.
• BringUpBench uses its own checkout; set BENCH_REPO_ROOT before evaluation.

When to read the bundled references

• Use the workflow reference for stage ordering and script ownership.
• Use the data-format reference to validate the JSONL/Parquet schemas.
• Use the reward reference to distinguish structure-recovery and identifier-naming signals.
• Use the BringUpBench reference to understand the benchmark-side file layout and environment variables.