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Agent Skill · 研究检索

graph-evolution(图演化)

graph-evolution 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

graph-evolution 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 9,382。

总安装 14,327周安装 603GitHub Stars 4,862下载 5,017核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:graph-evolution(图演化)
来源仓库:https://github.com/trailofbits/skills
仓库路径:skills/graph-evolution
安装命令:
npx skills add https://github.com/trailofbits/skills --skill graph-evolution
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 graph-evolution

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

graph-evolution 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。它能帮助 Agent 搜索图演化模型、匹配动态分析方法或复用时间序列策略。

通过 npx skills add 命令从指定仓库安装,具体用法请参考原始 README。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/graph-evolution

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/trailofbits/skills --skill graph-evolution;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

graph-evolution 怎么安装?

在终端执行 npx skills add https://github.com/trailofbits/skills --skill graph-evolution。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

graph-evolution 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

graph-evolution 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

graph-evolution 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

graph-evolution 用的人多吗?

公开数据是累计安装 14,327 次,近一周安装 603 次,下载 5,017 次,GitHub 4,862 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

graph-evolution 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 4,862 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
graph-evolution

图演化 · 本页

14,3274,862需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Graph Evolution

Builds Trailmark code graphs at two source snapshots and computes a structural diff. Surfaces security-relevant changes that text-level diffs miss: new attack paths, complexity shifts, blast radius growth, taint propagation changes, and privilege boundary modifications.

When to Use

  • Comparing two git refs to understand what structurally changed
  • Auditing a range of commits for security-relevant evolution
  • Detecting new attack paths created by code changes
  • Finding functions whose blast radius or complexity grew silently
  • Identifying taint propagation changes across refactors
  • Pre-release structural comparison (tag-to-tag or branch-to-branch)

When NOT to Use

  • Line-level code review (use differential-review for text-diff analysis)
  • Single-snapshot analysis (use the trailmark skill directly)
  • Diagram generation from a single snapshot (use the diagramming-code skill)
  • Mutation testing triage (use the genotoxic skill)

Rationalizations to Reject

RationalizationWhy It's WrongRequired Action
"We just need the structural diff, skip pre-analysis"Without pre-analysis, you miss taint changes, blast radius growth, and privilege boundary shiftsRun engine.preanalysis() on both snapshots
"Text diff covers what changed"Text diffs miss new attack paths, transitive complexity shifts, and subgraph membership changesUse structural diff to complement text diff
"Only added nodes matter"Removed security functions and shifted privilege boundaries are equally dangerousReview removals and modifications, not just additions
"Low-severity structural changes can be ignored"INFO-level changes (dead code removal) can mask removed security checksClassify every change, review removals for replaced functionality
"One snapshot's graph is enough for comparison"Single-snapshot analysis can't detect evolution — you need both before and afterAlways build and export both graphs
"Tool isn't installed, I'll compare manually"Manual comparison misses what graph analysis catchesInstall trailmark first

Prerequisites

trailmark must be installed. If uv run trailmark fails, run:

uv pip install trailmark

DO NOT fall back to "manual comparison" or reading source files as a substitute for running trailmark. The tool must be installed and used programmatically. If installation fails, report the error.


Quick Start

# Compare two git refs (e.g., tags, branches, commits)
# 1. Build graphs at each snapshot
# 2. Run pre-analysis on both
# 3. Compute structural diff
# 4. Generate report

# Step-by-step: see Workflow below

Decision Tree

├─ Need to understand what each metric means?
│  └─ Read: references/evolution-metrics.md
│
├─ Need the report output format?
│  └─ Read: references/report-format.md
│
├─ Already have two graph JSON exports?
│  └─ Jump to Phase 3 (run native diff + graph_diff.py)
│
└─ Starting from two git refs?
   └─ Start at Phase 1

Workflow

Graph Evolution Progress:
- [ ] Phase 1: Create snapshots (git worktrees)
- [ ] Phase 2: Build graphs + pre-analysis on both snapshots
- [ ] Phase 3: Compute structural diff
- [ ] Phase 4: Interpret diff and generate report
- [ ] Phase 5: Clean up worktrees

Phase 1: Create Snapshots

Use git worktrees to get clean copies of each ref without disturbing the working tree.

# Create temp directories for worktrees
BEFORE_DIR=$(mktemp -d)
AFTER_DIR=$(mktemp -d)

# Create worktrees (run from repo root)
git worktree add "$BEFORE_DIR" {before_ref}
git worktree add "$AFTER_DIR" {after_ref}

If comparing two directories instead of git refs, skip this phase and use the directory paths directly in Phase 2.

Phase 2: Build Graphs and Run Pre-Analysis

Build Trailmark graphs for both snapshots and run pre-analysis on each. Pre-analysis computes blast radius, taint propagation, privilege boundaries, and entrypoint enumeration.

from trailmark.query.api import QueryEngine

def build_and_export(target_dir, output_path, language="auto"):
    """Build graph, run pre-analysis, export JSON."""
    engine = QueryEngine.from_directory(target_dir, language=language)
    engine.preanalysis()
    json_str = engine.to_json()
    with open(output_path, "w") as f:
        f.write(json_str)
    return engine.summary()

import tempfile, os
work_dir = tempfile.mkdtemp(prefix="trailmark_evolution_")
before_json = os.path.join(work_dir, "before_graph.json")
after_json = os.path.join(work_dir, "after_graph.json")

before_summary = build_and_export(
    "{before_dir}", before_json
)
after_summary = build_and_export(
    "{after_dir}", after_json
)

Verify both graphs built successfully by checking the summary output. If either fails, rerun with an explicit language or comma-separated list instead of auto.

Phase 3: Compute Structural Diff

Run both:

  1. Trailmark's native structural diff for nodes, edges, and entrypoints
  2. The plugin's graph_diff.py helper for subgraph membership changes

Using the same work_dir from Phase 2:

trailmark diff --json "{before_dir}" "{after_dir}" > "{work_dir}/trailmark_diff.json" || \
  uv run trailmark diff --json "{before_dir}" "{after_dir}" > "{work_dir}/trailmark_diff.json"

uv run {baseDir}/scripts/graph_diff.py \
    --before "{before_json}" \
    --after "{after_json}" > "{work_dir}/subgraph_diff.json"

If either diff command fails or writes an empty JSON file, stop and report the error instead of continuing to Phase 4.

The native Trailmark diff contains:

KeyContents
summary_deltaChanges in node/edge/entrypoint counts
nodes.addedNew functions, classes, methods
nodes.removedDeleted functions, classes, methods
nodes.modifiedFunctions with changed CC, params, line span
edges.addedNew call/inheritance/import relationships
edges.removedDeleted relationships
entrypointsAdded, removed, and modified entrypoints

The subgraph diff contains:

KeyContents
subgraphsPer-subgraph membership changes (tainted, high_blast_radius, etc.)

Phase 4: Interpret Diff and Generate Report

Read both diff JSON files and generate a security-focused markdown report. See references/report-format.md for the full template.

Interpretation priorities (highest to lowest):

  1. New tainted paths — nodes entering the tainted subgraph, especially if they also appear in added edges targeting sensitive functions
  2. Privilege boundary changes — new or removed trust transitions from the native entrypoint/edge diff plus the subgraph diff
  3. Attack surface growth — new entrypoints, especially untrusted_external, from trailmark_diff.json
  4. Blast radius increases — nodes entering high_blast_radius
  5. Complexity spikes — CC increases > 3 on tainted or entrypoint-reachable nodes
  6. Structural additions — new nodes and edges (review needed)
  7. Structural removals — verify removed security functions were replaced

Cross-reference structural changes with git diff {before_ref}..{after_ref} to add source-level context to findings.

Severity classification:

SeverityStructural Signal
CRITICALNew tainted path to sensitive function, removed auth boundary
HIGHNew entrypoint + high blast radius, large CC increase on tainted node
MEDIUMNew trust-boundary-crossing edges, moderate CC increase
LOWAdded nodes without entrypoint reachability
INFODead code removal, complexity reductions

For detailed metric definitions, see references/evolution-metrics.md.

Phase 5: Clean Up

Remove git worktrees after the report is written:

git worktree remove "{before_dir}"
git worktree remove "{after_dir}"

Diff Reference

trailmark diff --json BEFORE AFTER
uv run {baseDir}/scripts/graph_diff.py [OPTIONS]

Use trailmark diff for:

  • Node/edge changes
  • Added/removed/modified entrypoints
  • Human-readable structural diff reports

Use graph_diff.py for:

  • Subgraph membership changes derived from engine.preanalysis()
  • tainted, high_blast_radius, privilege_boundary, and related sets
ArgumentDefaultDescription
--beforerequiredPath to the "before" graph JSON
--afterrequiredPath to the "after" graph JSON
--indent2JSON output indentation

graph_diff.py input format: Trailmark JSON exports from engine.to_json(). graph_diff.py output: JSON structural diff for nodes, edges, and subgraphs.


Quality Checklist

Before delivering the report:

  • Both graphs built successfully (check summaries)
  • Pre-analysis ran on both snapshots
  • Native Trailmark diff computed and non-empty (trailmark_diff.json)
  • Subgraph diff computed and non-empty (subgraph_diff.json)
  • All subgraph changes interpreted (tainted, blast radius, etc.)
  • Critical findings include evidence (node IDs, edge diffs)
  • Severity levels assigned to all findings
  • Source-level context added via git diff cross-reference
  • Worktrees cleaned up (or temp dirs removed)
  • Report written to GRAPH_EVOLUTION_*.md

Integration

trailmark skill: Phase 2 uses the trailmark API for graph building and pre-analysis. All trailmark query patterns work on either snapshot's engine.

differential-review skill: Use graph-evolution for structural analysis, differential-review for line-level code review. The two are complementary — graph-evolution finds attack paths that text diffs miss, while differential-review provides git blame context and micro-adversarial analysis.

genotoxic skill: If graph-evolution reveals new high-CC tainted nodes, feed them to genotoxic for mutation testing triage.

diagramming-code skill: Generate before/after diagrams to visualize structural changes. Use call-graph or data-flow diagrams focused on changed nodes.


Supporting Documentation

平台分布

Codex

32.39%
按下载量换算1,625

Claude

31.63%
按下载量换算1,587

Cursor

18.76%
按下载量换算941

Gemini CLI

9.35%
按下载量换算469

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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