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github-evidence-kitGitHub evidence KIT 搜索

Agent Skill

用于围绕 GitHub 仓库、Issue、Pull Request、分支、提交和代码协作流程提供辅助能力。它适合让 Agent 查询项目状态、整理变更、辅助创建或检查协作事项,并把仓库中的信息转成可执行的下一步。使用时需要区分只读查询和写入操作;涉及创建 PR、修改 Issue、推送分支或访问私有仓库时,应确认 token 权限、目标仓库范围和用户授权。

总安装

445

周安装

18

GitHub Stars

1,974

下载量

140
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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

3

许可证

MIT

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:github-evidence-kit(GitHub evidence KIT 搜索)
来源仓库:https://github.com/gadievron/raptor
仓库路径:skills/github-evidence-kit
安装命令:
npx skills add https://github.com/gadievron/raptor --skill github-evidence-kit
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/gadievron/raptor --skill github-evidence-kit

简介

收集 GitHub 活动作为证据链,支撑审计或合规报告。

  • 适用于导出提交记录、PR 评审日志与 issue 解决时间线。
  • 生成标准化 JSON 或 PDF 文档,符合法律取证格式要求。
  • 敏感操作日志需加密存储,防止未授权人员查看原始数据。
  • github-evidence-kit 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

GH Evidence Kit

Purpose: Create, store, and verify forensic evidence from GitHub-related public sources and local git repositories.

When to Use This Skill

  • Creating verifiable evidence objects from GitHub activity
  • Local git forensics - analyzing cloned repositories, dangling commits, reflog
  • Exporting evidence collections to JSON for sharing/archival
  • Loading and re-verifying previously collected evidence
  • Recovering deleted GitHub content (issues, PRs, commits) from GH Archive
  • Tracking IOCs (Indicators of Compromise) with source verification

Quick Start

from src.collectors import GitHubAPICollector, LocalGitCollector, GHArchiveCollector
from src import EvidenceStore

# Create collectors for different sources
github = GitHubAPICollector()
local = LocalGitCollector("/path/to/repo")
archive = GHArchiveCollector()

# Collect evidence from GitHub API
commit = github.collect_commit("aws", "aws-toolkit-vscode", "678851b...")
pr = github.collect_pull_request("aws", "aws-toolkit-vscode", 7710)

# Collect evidence from local git (first-class forensic source)
local_commit = local.collect_commit("HEAD")
dangling = local.collect_dangling_commits()  # Forensic gold!

# Store and export
store = EvidenceStore()
store.add(commit)
store.add(pr)
store.add(local_commit)
store.add_all(dangling)
store.save("evidence.json")

# Verify all evidence against original sources
is_valid, errors = store.verify_all()

Collectors

GitHubAPICollector

Collects evidence from the live GitHub API.

from src.collectors import GitHubAPICollector

collector = GitHubAPICollector()
MethodReturns
collect_commit(owner, repo, sha)CommitObservation
collect_issue(owner, repo, number)IssueObservation
collect_pull_request(owner, repo, number)IssueObservation
collect_file(owner, repo, path, ref)FileObservation
collect_branch(owner, repo, branch_name)BranchObservation
collect_tag(owner, repo, tag_name)TagObservation
collect_release(owner, repo, tag_name)ReleaseObservation
collect_forks(owner, repo)list[ForkObservation]

LocalGitCollector (First-Class Forensics)

Collects evidence from local git repositories. Essential for forensic analysis of cloned repos.

from src.collectors import LocalGitCollector

collector = LocalGitCollector("/path/to/cloned/repo")

# Collect a specific commit
commit = collector.collect_commit("HEAD")
commit = collector.collect_commit("abc123")

# Find dangling commits (not reachable from any ref)
# This is forensic gold - reveals force-pushed or deleted commits!
dangling = collector.collect_dangling_commits()
for commit in dangling:
    print(f"Found dangling: {commit.sha[:8]} - {commit.message}")
MethodReturns
collect_commit(sha)CommitObservation
collect_dangling_commits()list[CommitObservation]

GHArchiveCollector

Collects and recovers evidence from GH Archive (BigQuery). Requires credentials.

from src.collectors import GHArchiveCollector

collector = GHArchiveCollector()

# Query events by timestamp (YYYYMMDDHHMM format)
events = collector.collect_events(
    timestamp="202507132037",
    repo="aws/aws-toolkit-vscode"
)

# Recover deleted content
deleted_issue = collector.recover_issue("aws/aws-toolkit-vscode", 123, "2025-07-13T20:30:24Z")
deleted_pr = collector.recover_pr("aws/aws-toolkit-vscode", 7710, "2025-07-13T20:30:24Z")
deleted_commit = collector.recover_commit("aws/aws-toolkit-vscode", "678851b", "2025-07-13T20:30:24Z")
force_pushed = collector.recover_force_push("aws/aws-toolkit-vscode", "2025-07-13T20:30:24Z")
MethodReturns
collect_events(timestamp, repo, actor, event_type)list[Event]
recover_issue(repo, number, timestamp)IssueObservation
recover_pr(repo, number, timestamp)IssueObservation
recover_commit(repo, sha, timestamp)CommitObservation
recover_force_push(repo, timestamp)CommitObservation

WaybackCollector

Collects archived snapshots from the Wayback Machine.

from src.collectors import WaybackCollector

collector = WaybackCollector()

# Get all snapshots for a URL
snapshots = collector.collect_snapshots("https://github.com/owner/repo")

# With date filtering
snapshots = collector.collect_snapshots(
    "https://github.com/owner/repo",
    from_date="20250101",
    to_date="20250731"
)

# Fetch actual content of a snapshot
content = collector.collect_snapshot_content(
    "https://github.com/owner/repo",
    "20250713203024"  # YYYYMMDDHHMMSS format
)

Verification

Verification is separated from data collection. Use ConsistencyVerifier to validate evidence against original sources.

from src.verifiers import ConsistencyVerifier

verifier = ConsistencyVerifier()

# Verify single evidence
result = verifier.verify(commit)
if not result.is_valid:
    print(f"Errors: {result.errors}")

# Verify multiple
result = verifier.verify_all([commit, pr, issue])

Or use the convenience method on EvidenceStore:

store = EvidenceStore()
store.add_all([commit, pr, issue])
is_valid, errors = store.verify_all()

EvidenceStore

Store, query, and export evidence collections.

from src import EvidenceStore
from datetime import datetime

store = EvidenceStore()

# Add evidence
store.add(commit)
store.add_all([pr, issue, ioc])

# Query
commits = store.filter(observation_type="commit")
recent = store.filter(after=datetime(2025, 7, 1))
from_github = store.filter(source="github")
from_git = store.filter(source="git")
repo_events = store.filter(repo="aws/aws-toolkit-vscode")

# Export/Import
store.save("evidence.json")
store = EvidenceStore.load("evidence.json")

# Summary
print(store.summary())
# {'total': 5, 'events': {...}, 'observations': {...}, 'by_source': {...}}

# Verify all against sources
is_valid, errors = store.verify_all()

Loading Evidence from JSON

from src import load_evidence_from_json
import json

with open("evidence.json") as f:
    data = json.load(f)

for item in data:
    evidence = load_evidence_from_json(item)
    # Evidence is now a typed Pydantic model

Evidence Types

Events (from GH Archive)

All 12 GitHub event types are supported:

TypeDescription
PushEventCommits pushed
PullRequestEventPR opened/closed/merged
IssueEventIssue opened/closed
IssueCommentEventComment on issue/PR
CreateEventBranch/tag created
DeleteEventBranch/tag deleted
ForkEventRepository forked
WatchEventRepository starred
MemberEventCollaborator added/removed
PublicEventRepository made public
ReleaseEventRelease published/created/deleted
WorkflowRunEventGitHub Actions run

Observations (from GitHub API, Local Git, Wayback, Vendors)

TypeDescriptionSources
CommitObservationCommit metadata and filesGitHub, Git, GH Archive
IssueObservationIssue or PRGitHub, GH Archive
FileObservationFile content at refGitHub
BranchObservationBranch HEADGitHub
TagObservationTag targetGitHub
ReleaseObservationRelease metadataGitHub
ForkObservationFork relationshipGitHub
SnapshotObservationWayback snapshotsWayback
IOCIndicator of CompromiseVendor
ArticleObservationSecurity report/blogVendor

IOC Types

from src import EvidenceSource, IOCType
from src.schema import IOC, VerificationInfo
from pydantic import HttpUrl
from datetime import datetime, timezone

# IOCs are created directly as schema objects
ioc = IOC(
    evidence_id="ioc-commit-sha-abc123",
    observed_when=datetime.now(timezone.utc),
    observed_by=EvidenceSource.SECURITY_VENDOR,
    observed_what="Malicious commit SHA found in vendor report",
    verification=VerificationInfo(
        source=EvidenceSource.SECURITY_VENDOR,
        url=HttpUrl("https://vendor.com/report")
    ),
    ioc_type=IOCType.COMMIT_SHA,
    value="678851bbe9776228f55e0460e66a6167ac2a1685",
)

Available IOC types: COMMIT_SHA, FILE_PATH, FILE_HASH, CODE_SNIPPET, EMAIL, USERNAME, REPOSITORY, TAG_NAME, BRANCH_NAME, WORKFLOW_NAME, IP_ADDRESS, DOMAIN, URL, API_KEY, SECRET

Testing

Run Unit Tests

cd .claude/skills/github-forensics/github-evidence-kit
pip install -r requirements.txt
pytest tests/ -v --ignore=tests/test_integration.py

Run Integration Tests (Optional)

Integration tests hit real external services (GitHub API, BigQuery, vendor URLs):

# All integration tests
pytest tests/test_integration.py -v -m integration

# Skip integration tests in CI
pytest tests/ -v -m "not integration"

Note: GitHub API integration tests use 60 req/hr unauthenticated rate limit. BigQuery tests require credentials (see below).

GCP BigQuery Credentials (for GH Archive)

GH Archive queries require Google Cloud BigQuery credentials. Two options:

Option 1: JSON File Path

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/credentials.json

Option 2: JSON Content in Environment Variable

Useful for .env files or CI secrets:

export GOOGLE_APPLICATION_CREDENTIALS='{"type":"service_account","project_id":"...","private_key":"..."}'

The client auto-detects JSON content vs file path.

Setup Steps

  1. Create a Google Cloud Project
  2. Enable BigQuery API
  3. Create a Service Account with BigQuery User role
  4. Download JSON credentials
  5. Set GOOGLE_APPLICATION_CREDENTIALS env var

Free Tier: 1 TB/month of BigQuery queries included.

Requirements

pip install -r requirements.txt
  • pydantic - Schema validation
  • requests - HTTP client
  • google-cloud-bigquery - GH Archive queries (optional)
  • google-auth - GCP authentication (optional)

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用户想查找某类 Agent Skill 时

02

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03

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04

需要参考平台分布和安装热度时

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能力 4

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能力 5

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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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