Trailmark Structural Analysis

Builds a Trailmark graph and runs engine.preanalysis() to compute all four pre-analysis passes.

When to Use

• Vivisect Phase 1 needs full structural data (hotspots, taint, blast radius, privilege boundaries)
• Detailed pre-analysis passes for a specific target scope
• Generating complexity and taint data for audit prioritization

When NOT to Use

• Quick overview only (use trailmark-summary instead)
• Ad-hoc code graph queries (use the main trailmark skill directly)
• Target is a single small file where structural analysis adds no value

Rationalizations to Reject

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.

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

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

from trailmark.parse import detect_languages

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

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

Step 3: Run the full structural analysis via QueryEngine.

Run this snippet with python3. If the import fails, rerun the same snippet under uv run python - "{args}".

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

from trailmark.parse import detect_languages
from trailmark.query.api import QueryEngine

target = sys.argv[1]
languages = detect_languages(target)
engine = QueryEngine.from_directory(target, language="auto")
preanalysis = engine.preanalysis()

def summarize_subgraph(name: str, limit: int = 25) -> dict[str, object]:
    nodes = engine.subgraph(name)
    return {
        "count": len(nodes),
        "sample_ids": [node["id"] for node in nodes[:limit]],
    }

payload = {
    "languages": languages,
    "summary": engine.summary(),
    "preanalysis": preanalysis,
    "attack_surface": engine.attack_surface()[:25],
    "hotspots": engine.complexity_hotspots(10)[:25],
    "subgraphs": {
        name: summarize_subgraph(name)
        for name in engine.subgraph_names()
    },
}

print(json.dumps(payload, indent=2))
PY

Step 4: Verify the output.

The output should include:

• languages
• summary
• preanalysis
• hotspots (possibly empty)
• subgraphs with counts and sample IDs

Some subgraphs may have zero nodes for some codebases (this is normal). Return the full JSON payload regardless.