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trailmark-summary

Get a quick structural overview of a codebase with Trailmark. The skill identifies programming languages, counts entry points where execution begins, and returns dependency information to support orientation before deeper analysis.

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Runs a Trailmark summary analysis on a codebase. Returns auto-detected languages, entry point count, and dependency list. Use when vivisect or galvanize needs a quick structural overview. Triggers: trailmark summary, code summary, structural overview.

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适用场景

  • Getting oriented in an unfamiliar codebase
  • Checking languages and entry point counts before analysis
  • Preparing a structural overview for Vivisect or Galvanize

包含的文件(3)

  • SKILL.md3.6 KB
  • agents/openai.yaml238 B
  • assets/trail-of-bits-mark.svg3.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Trailmark Summary

Runs trailmark analyze --language auto --summary on a target directory. This is a v0.2-safe workflow; do not require Trailmark 0.4.0 just to produce a summary.

When to Use

  • Vivisect Phase 0 needs a quick structural overview before decomposition
  • Galvanize Phase 1 needs detected languages and entry point count
  • Quick orientation on an unfamiliar codebase before deeper analysis

When NOT to Use

  • Full structural analysis with all passes needed (use trailmark-structural)
  • Detailed code graph queries (use the main trailmark skill directly)
  • You need hotspot scores or taint data (use trailmark-structural)

Rationalizations to Reject

RationalizationWhy It's WrongRequired Action
"I can read the code manually instead"Manual reading misses parser-based language detection, dependency data, and entry point enumerationInstall and run trailmark
"Language detection doesn't matter"Wrong language selection produces empty or partial analysisUse Trailmark's parser-based detection or --language auto
"Partial output is good enough"Missing any of the three required outputs (detected languages, entry points, dependencies) means incomplete analysisVerify all three are present
"Tool isn't installed, I'll skip it"This skill exists specifically to run trailmarkReport the installation gap instead of skipping

Usage

The target directory is passed via the args parameter.

Execution

Step 1: Check that trailmark is available.

trailmark analyze --help 2>/dev/null || \
  uv run trailmark analyze --help 2>/dev/null

If neither command works, report "trailmark is not installed" and return. Do NOT run pip install, uv pip install, git clone, or any install command. The user must install trailmark themselves.

Optionally record the version if the installed build supports it:

trailmark --version 2>/dev/null || uv run trailmark --version 2>/dev/null || true

Do not fail if the version command is missing; older v0.2.x builds may still support the summary workflow.

Step 2: Detect languages with Trailmark's parse API.

python3 - "{args}" <<'PY'
import json
import sys

try:
    from trailmark.parse import detect_languages  # canonical location since 0.3.x
except ModuleNotFoundError:
    # v0.2.x predates trailmark.parse; the same function lives in query.api
    from trailmark.query.api import detect_languages

print(json.dumps(detect_languages(sys.argv[1])))
PY

If the import fails, rerun the same snippet with uv run --with trailmark python - "{args}". If the result is [], report "Trailmark found no supported languages under target" and return.

Step 3: Run the summary with auto-detection.

trailmark analyze --language auto --summary {args} 2>&1 || \
  uv run trailmark analyze --language auto --summary {args} 2>&1

Step 4: Verify the output.

The output must include ALL THREE of:

  1. Detected languages from Step 2
  2. Entrypoints: line from the summary output
  3. Dependencies: line from the summary output

If any are missing, report the gap. Do not fabricate output.

Return the detected language list plus the full Trailmark summary output. If a version string was available, include it in the returned metadata.

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