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audit-augmentation审计增强

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:audit-augmentation(审计增强)
来源仓库:https://github.com/trailofbits/skills
仓库路径:skills/audit-augmentation
安装命令:
npx skills add https://github.com/trailofbits/skills --skill audit-augmentation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/trailofbits/skills --skill audit-augmentation

简介

将外部审计工具(SARIF)和人工审计结果(weAudit)投射到 Trailmark 图谱。

  • 支持跨工具结果关联、高危函数查询和覆盖率可视化分析。
  • 适用于整合 Semgrep、CodeQL 等多源安全数据的需求。
  • 安装方式:github,通过 npx skills add 命令从指定仓库添加。
  • 注意:不直接运行分析工具,而是导入已有结果进行图谱增强。

SKILL.md

Audit Augmentation

Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code graphs as annotations and subgraphs.

When to Use

  • Importing Semgrep, CodeQL, or other SARIF-producing tool results into a graph
  • Importing weAudit audit annotations into a graph
  • Cross-referencing static analysis findings with blast radius or taint data
  • Querying which functions have high-severity findings
  • Visualizing audit coverage alongside code structure

When NOT to Use

  • Running static analysis tools (use semgrep/codeql directly, then import)
  • Building the code graph itself (use the trailmark skill)
  • Generating diagrams (use the diagramming-code skill after augmenting)

Rationalizations to Reject

RationalizationWhy It's WrongRequired Action
"The user only asked about SARIF, skip pre-analysis"Without pre-analysis, you can't cross-reference findings with blast radius or taintAlways run engine.preanalysis() before augmenting
"Unmatched findings don't matter"Unmatched findings may indicate parsing gaps or out-of-scope filesReport unmatched count and investigate if high
"One severity subgraph is enough"Different severities need different triage workflowsQuery all severity subgraphs, not just error
"SARIF results speak for themselves"Findings without graph context lack blast radius and taint reachabilityCross-reference with pre-analysis subgraphs
"weAudit and SARIF overlap, pick one"Human auditors and tools find different thingsImport both when available
"Tool isn't installed, I'll do it manually"Manual analysis misses what tooling catchesInstall trailmark first

Installation

MANDATORY: If uv run trailmark fails, install trailmark first:

uv pip install trailmark

Quick Start

CLI

# Augment with SARIF
uv run trailmark augment {targetDir} --sarif results.sarif

# Augment with weAudit
uv run trailmark augment {targetDir} --weaudit .vscode/alice.weaudit

# Both at once, output JSON
uv run trailmark augment {targetDir} \
    --sarif results.sarif \
    --weaudit .vscode/alice.weaudit \
    --json

Programmatic API

from trailmark.query.api import QueryEngine

engine = QueryEngine.from_directory("{targetDir}", language="auto")

# Run pre-analysis first for cross-referencing
engine.preanalysis()

# Augment with SARIF
result = engine.augment_sarif("results.sarif")
# result: {matched_findings: 12, unmatched_findings: 3, subgraphs_created: [...]}

# Augment with weAudit
result = engine.augment_weaudit(".vscode/alice.weaudit")

# Query findings
engine.findings()                                       # All findings
engine.subgraph("sarif:error")                          # High-severity SARIF
engine.subgraph("weaudit:high")                         # High-severity weAudit
engine.subgraph("sarif:semgrep")                        # By tool name
engine.annotations_of("function_name")                  # Per-node lookup

If auto-detection is wrong for the target, rerun with an explicit language or comma-separated list such as python,rust.

Workflow

Augmentation Progress:
- [ ] Step 1: Build graph and run pre-analysis
- [ ] Step 2: Locate SARIF/weAudit files
- [ ] Step 3: Run augmentation
- [ ] Step 4: Inspect results and subgraphs
- [ ] Step 5: Cross-reference with pre-analysis

Step 1: Build the graph and run pre-analysis for blast radius and taint context:

engine = QueryEngine.from_directory("{targetDir}", language="auto")
engine.preanalysis()

If auto-detection is wrong for the target, rerun with an explicit language or comma-separated list such as python,rust.

Step 2: Locate input files:

  • SARIF: Usually output by tools like semgrep --sarif -o results.sarif or codeql database analyze --format=sarif-latest
  • weAudit: Stored in .vscode/<username>.weaudit within the workspace

Step 3: Run augmentation via engine.augment_sarif() or engine.augment_weaudit(). Check unmatched_findings in the result — these are findings whose file/line locations didn't overlap any parsed code unit.

Step 4: Query findings and subgraphs. Use engine.findings() to list all annotated nodes. Use engine.subgraph_names() to see available subgraphs.

Step 5: Cross-reference with pre-analysis data to prioritize:

  • Findings on tainted nodes: overlap sarif:error with tainted subgraph
  • Findings on high blast radius nodes: overlap with high_blast_radius
  • Findings on privilege boundaries: overlap with privilege_boundary

Annotation Format

Findings are stored as standard Trailmark annotations:

  • Kind: finding (tool-generated) or audit_note (human notes)
  • Source: sarif:<tool_name> or weaudit:<author>
  • Description: Compact single-line: [SEVERITY] rule-id: message (tool)

Subgraphs Created

SubgraphContents
sarif:errorNodes with SARIF error-level findings
sarif:warningNodes with SARIF warning-level findings
sarif:noteNodes with SARIF note-level findings
sarif:<tool>Nodes flagged by a specific tool
weaudit:highNodes with high-severity weAudit findings
weaudit:mediumNodes with medium-severity weAudit findings
weaudit:lowNodes with low-severity weAudit findings
weaudit:findingsAll weAudit findings (entryType=0)
weaudit:notesAll weAudit notes (entryType=1)

How Matching Works

Findings are matched to graph nodes by file path and line range overlap:

  1. Finding file path is normalized relative to the graph's root_path
  2. Nodes whose location.file_path matches AND whose line range overlaps are selected
  3. The tightest match (smallest span) is preferred
  4. If a finding's location doesn't overlap any node, it counts as unmatched

SARIF paths may be relative, absolute, or file:// URIs — all are handled. weAudit uses 0-indexed lines which are converted to 1-indexed automatically.

Supporting Documentation

适合场景

01

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02

需要根据任务场景推荐可安装能力包时

03

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能力概览

能力 1

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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Codex

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安装前确认

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