external/trailofbits-security/trailmark/skills/trailmark-structural/SKILL.md
Runs full Trailmark structural analysis on Trailmark 0.2.x by building a graph, running `preanalysis()`, and reporting hotspots, taint, blast radius, privilege boundaries, and attack surface. Use when vivisect needs detailed structural data for a target. Triggers: structural analysis, blast radius, taint analysis, complexity hotspots.
npx skillsauth add seikaikyo/dash-skills trailmark-structuralInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Builds a Trailmark graph and runs engine.preanalysis() to compute all
four pre-analysis passes.
trailmark-summary instead)trailmark skill directly)| Rationalization | Why It's Wrong | Required Action | |-----------------|----------------|-----------------| | "Summary analysis is enough" | Summary skips taint, blast radius, and privilege boundary data | Run full structural analysis when detailed data is needed | | "One pass is sufficient" | Passes cross-reference each other — taint without blast radius misses critical nodes | Run all four passes | | "Tool isn't installed, I'll analyze manually" | Manual analysis misses what tooling catches | Report "trailmark is not installed" and return | | "Empty pass output means the pass failed" | Some passes produce no data for some codebases (e.g., no privilege boundaries) | Return full output regardless |
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.
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:
languagessummarypreanalysishotspots (possibly empty)subgraphs with counts and sample IDsSome subgraphs may have zero nodes for some codebases (this is normal). Return the full JSON payload regardless.
development
拋棄式 HTML mockup 比稿:產出 2 到 3 個設計立場不同的變體(密度 / 版式 / 強調軸,不是換色),各附取捨說明,最後給有立場的對比結論。適用:「畫個草圖」「比較 A 版 B 版」「先看方向再做」「給我看幾種做法」。要 production 元件或設計已定案時不適用。
tools
需求不明時的意圖萃取訪談:一次一題、每題附上自己的猜測、聽出「真正想要 vs 覺得應該要」,直到能預測使用者反應(約 95% 信心)才動工。適用:需求缺少對象 / 動機 / 成功標準 / 約束,或使用者點名「訪談我」「先確認一下」「我們確定嗎」。明確自足的指示、純資訊查詢、機械性操作不適用。
development
對非平凡決策啟動新鮮 context 對抗審查(找碴不背書),在修正還便宜的時候抓出錯誤方向。適用:高風險改動(production、資安敏感邏輯、不可逆操作)、不熟的程式碼、要宣稱「這樣是安全的 / 可行的」之前。機械性操作與一行修改不適用。
testing
Reference for writing and editing agent skills well — the vocabulary and principles that make a skill predictable. Consult when authoring, reviewing, or pruning a SKILL.md.