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.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
trailmark-structural)trailmark skill directly)trailmark-structural)| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "I can read the code manually instead" | Manual reading misses parser-based language detection, dependency data, and entry point enumeration | Install and run trailmark |
| "Language detection doesn't matter" | Wrong language selection produces empty or partial analysis | Use 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 analysis | Verify all three are present |
| "Tool isn't installed, I'll skip it" | This skill exists specifically to run trailmark | Report the installation gap instead of skipping |
The target directory is passed via the args parameter.
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:
Entrypoints: line from the summary outputDependencies: line from the summary outputIf 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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Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.
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Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.
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Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.
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Reviews Algorand smart contracts written in TEAL or PyTeal for 11 vulnerability patterns. Produces a complete coverage table and findings with code references, severity ratings, and suggested fixes, with optional automated checks using Tealer.
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Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.
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Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.
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