Dwarf Expert

Overview

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/dwarf-expert from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses metadata.json plus ORIGIN.md as the provenance anchor for review.

Overview This skill provides technical knowledge and expertise about the DWARF standard and how to interact with DWARF files. Tasks include answering questions about the DWARF standard, providing examples of various DWARF features, parsing and/or creating DWARF files, and writing/modifying/analyzing code that interacts with DWARF data.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Structural Validation, Quality Metrics, Common Verification Patterns, readelf, dwarfdump, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Understanding or parsing DWARF debug information from compiled binaries
• Answering questions about the DWARF standard (v3, v4, v5)
• Writing or reviewing code that interacts with DWARF data
• Using dwarfdump or readelf to extract debug information
• Verifying DWARF data integrity with llvm-dwarfdump --verify
• Working with DWARF parsing libraries (libdwarf, pyelftools, gimli, etc.)

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Structural Validation

# Verify DWARF structure (compile units, DIE relationships, address ranges)
llvm-dwarfdump --verify <binary>

# Detailed error output with summary
llvm-dwarfdump --verify --error-display=full <binary>

# Machine-readable JSON error summary
llvm-dwarfdump --verify --verify-json=errors.json <binary>

Examples

Example 1: Ask for the upstream workflow directly

Use @dwarf-expert to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @dwarf-expert against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @dwarf-expert for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @dwarf-expert using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/dwarf-expert, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @devops-deploy - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @devops-troubleshooter - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @differential-review - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @discord-automation - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Quality Metrics

# Output debug info quality metrics as JSON
llvm-dwarfdump --statistics <binary>

The --statistics output helps compare debug info quality across compiler versions and optimization levels.

Imported: Common Verification Patterns

• After compilation: Verify binaries have valid DWARF before distribution
• Comparing builds: Use --statistics to detect debug info quality regressions
• Debugging debuggers: Identify malformed DWARF causing debugger issues
• DWARF tool development: Validate parser output against known-good binaries

Parsing DWARF Debug Information

Imported: readelf

ELF files can be parsed via the readelf command ({baseDir}/reference/readelf.md). Use this for general ELF information, but prefer dwarfdump for DWARF-specific parsing.

Imported: dwarfdump

DWARF files can be parsed via the dwarfdump command, which is more effective at parsing and displaying complex DWARF information than readelf and should be used for most DWARF parsing tasks ({baseDir}/reference/dwarfdump.md).

Working With Code

This skill supports writing, modifying, and reviewing code that interacts with DWARF data. This may involve code that parses DWARF debug data from scratch or code that leverages libraries to parse and interact with DWARF data ({baseDir}/reference/coding.md).

Choosing Your Approach

┌─ Need to verify DWARF data integrity?
│   └─ Use `llvm-dwarfdump --verify` (see Verification Workflows above)
├─ Need to answer questions about the DWARF standard?
│   └─ Search dwarfstd.org or reference LLVM/libdwarf source
├─ Need simple section dump or general ELF info?
│   └─ Use `readelf` ({baseDir}/reference/readelf.md)
├─ Need to parse, search, and/or dump DWARF DIE nodes?
│   └─ Use `dwarfdump` ({baseDir}/reference/dwarfdump.md)
└─ Need to write, modify, or review code that interacts with DWARF data?
    └─ Refer to the coding reference ({baseDir}/reference/coding.md)

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.