trailmark-summary
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.
- 0
- Installs
- —
- Rating
- —
- Success rate
- 2
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 4e4ee8b89d0f8ee0… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
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
trailmarkskill directly) - You need hotspot scores or taint data (use
trailmark-structural)
Rationalizations to Reject
| 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 |
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:
- Detected languages from Step 2
Entrypoints:line from the summary outputDependencies: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.
Files
2- SKILL.md
c6a6b5feb73.6 KB - agents/openai.yaml
b2a69e67af238 B
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from trailofbits/skills8
Builds and runs code under AddressSanitizer to catch buffer overflows, use-after-free, and other memory errors during fuzzing or tests. Covers -fsanitize=address builds, ASAN_OPTIONS, reading the crash report, LeakSanitizer, and the overhead and platform trade-offs. Use when fuzzing C/C++ or Rust th
Sets up and runs AFL++ for multi-core fuzzing of C/C++ projects built with afl-clang-fast or afl-gcc-fast. Covers instrumentation modes, parallel main and secondary campaigns, persistent mode, corpus minimization, and crash triage. Use when scaling fuzzing across cores, fuzzing a mature C/C++ codeba
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary p
Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal).
Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C ex
Augments Trailmark code graphs with external audit findings from SARIF static analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x binary-analysis graph exports. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and enables cross-refere
Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use when starting an audit, threat model, or architecture review on unfamiliar code, and before any vulnerability-hunting pass.
Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase
Related knowledge skillsscan passed
PostHog logs for Datadog
Per-channel strict prompts, mention gating, silent observation, and a communication autonomy policy for agents that sit in shared channels with external counterparties. Use when an agent joins group chats, shared channels, or DMs where outsiders can read every message and you need it to speak only w