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Headless Ghidra Skill Family

This repository is the source for a standalone skill family for planning and auditing headless Ghidra reverse-engineering work. It is built for workflows that need explicit phase boundaries, reproducible handoffs, and reviewable Markdown outputs instead of ad hoc analyst notes.

Use this family when you want to:

  • route a reverse-engineering task to the right planning phase
  • hand a phase contract into speckit without downstream-only hooks
  • audit generated spec.md, plan.md, and tasks.md against the same rules
  • keep dynamic evidence, script changes, and late-stage decompilation work inside clear review boundaries

What This Skill Family Provides

  • One umbrella entrypoint in headless-ghidra/ that explains routing across the full workflow.
  • Phase-specific skills for intake, evidence, Frida runtime capture, Frida evidence import, progressive decompilation, and script review.
  • A canonical planning-brief.md in each planning phase directory. That file is both the portable handoff surface and the audit checklist for generated planning artifacts.
  • Worked examples and templates in each skill directory so the contract stays concrete instead of implicit.
  • A reusable Frida common script library in headless-ghidra-frida-runtime-injection/frida-scripts/ covering five supported runtime evidence scenarios.
  • An explicit auto-evolution skill for turning completed real-task learnings into tracked reusable assets when the evidence is strong enough.

Core Guarantees

Every skill in this family keeps the same baseline:

  • Headless-only workflows. GUI-only Ghidra guidance stays out of scope.
  • Evidence-backed claims. Planning and review must point back to observable inputs, artifacts, or recorded findings.
  • Reproducible execution. Commands, manifests, and review expectations stay explicit.
  • Markdown-first outputs. The important contract surfaces remain readable and auditable in version control.
  • Portable contracts. The family is designed to work without requiring downstream speckit extensions or custom constitution edits.

Runtime artifacts remain outside the tracked skill package. When Frida runtime capture is in scope, reusable scripts operate only on the configured targets for the approved scenario, produced outputs stay under .work/ghidra-artifacts/, and manifests reference those local artifacts rather than copying raw runtime output into tracked skill directories. Tracked documentation must redact or generalize raw runtime values. Requests for broader capture scope, new helper logic, or new output fields route to headless-ghidra-script-review instead of expanding the runtime flow ad hoc.

Skill Map

Skill Use it when Key surfaces
headless-ghidra You need the family entrypoint, routing help, or the shared collaboration sequence. SKILL.md, examples/, templates/, ghidra-scripts/, scripts/
headless-ghidra-intake You are defining target identity, provenance, scope, deliverables, and initialization assumptions. planning-brief.md, examples/
headless-ghidra-evidence You need evidence extraction, replay expectations, and audit surfaces after intake is stable. planning-brief.md, examples/
headless-ghidra-frida-runtime-injection You need reproducible CLI/headless Frida capture planning and reusable script selection. planning-brief.md, templates/frida-capture-manifest.md, frida-scripts/, examples/
headless-ghidra-frida-evidence You already have captured Frida outputs and need import-only evidence normalization and review. planning-brief.md, templates/frida-evidence-manifest.md, examples/
headless-ghidra-progressive-decompilation You are at Stage 6 and need the standalone contract for selected decompilation and incremental compare. planning-brief.md, examples/
headless-ghidra-script-review The plan introduces reusable scripts, manifest-generation logic, or Frida helper coverage changes. planning-brief.md, examples/
headless-ghidra-auto-evolution A completed real task exposed a reusable improvement that should be accepted, deferred, or rejected explicitly. SKILL.md, templates/auto-evolution-review-record.md, examples/

Frida Runtime Support

The Frida runtime branch of the family is intentionally split into two phases:

The shipped common Frida script library supports five first-class scenarios:

  • function signature analysis
  • decompilation-to-original comparison
  • runtime call-tree tracing
  • dynamic dispatch or vtable observation
  • hot-path or coverage observation

If a request falls outside the documented coverage or changes a reusable script's behavior or output shape, route the follow-up into headless-ghidra-script-review instead of extending the runtime phase ad hoc.

Typical Usage

  1. Start with headless-ghidra/SKILL.md if the correct phase is not obvious.
  2. Move into the phase-specific skill directory that matches the current stage of work.
  3. Fill in that phase's planning-brief.md.
  4. Hand the brief to speckit as a file or inline paste.
  5. Re-open the same skill and audit the generated spec.md, plan.md, and tasks.md against the same phase contract.
  6. Use the local examples/ and templates/ to keep manifests, review notes, and audit findings in the expected shape.
  7. After a real task finishes, use headless-ghidra-auto-evolution when a reusable improvement should be reviewed for promotion into the tracked workflow.

Common Routes

Repository Layout

  • headless-ghidra/ is the umbrella skill and shared support surface. It includes routing guidance, walkthroughs, example artifacts, reusable headless Ghidra scripts, and helper shell scripts.
  • Each phase directory contains the phase skill, a fixed-name planning-brief.md, and phase-specific examples or templates.
  • The Frida runtime phase also includes the tracked common script library and its manifest.
  • The auto-evolution phase includes the review template used to decide whether a reusable improvement becomes a tracked asset.

If you are opening the repository for the first time, start in headless-ghidra/SKILL.md, then follow the phase links above.

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Headless Ghidra reverse-engineering skill family for reproducible, evidence-backed workflows and audit-ready Markdown outputs.

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