菜单
Verify harness changes end-to-end without docker — drive the real pinned CLI against a header-capturing stub server with the exact env resolve_auth_env() produces.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/anthropics/defending-code-reference-harness --skill verify命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Adapt this C/C++ ASAN vulnerability pipeline to a different vulnerability class, target shape, language, or detection mechanism. Use when the user wants to port, migrate, retarget, customize, or fork the pipeline for something other than C/C++ memory-safety bugs — web apps, smart contracts, deserialization, ML systems, or any other domain.
Generate candidate fixes for verified security findings. Consumes TRIAGE.json (preferred), VULN-FINDINGS.json, INCIDENTS.json, or a vuln-pipeline results directory. Pipeline input is delegated to the execution-verified `vuln-pipeline patch` ladder; static-analysis input gets a per-finding patch subagent + independent reviewer and is written as inert diffs for human review. Writes PATCHES/bug_NN/{patch.diff,patch_result.json}, PATCHES.md, and PATCHES.json. Use when asked to "fix the findings", "patch these vulns", "generate fixes", or "close the loop on triage".
Build a threat model for a target codebase. Three modes: "interview" walks an application owner through the four-question framework and produces a threat model from their answers; "bootstrap" derives a threat model from the code plus past vulnerabilities (CVEs, git history, pentest reports) when no owner is available; "bootstrap-then-interview" chains the two when both owner and codebase are present. All write THREAT_MODEL.md in a shared schema. Use when asked to "threat model", "build a threat model", "map the attack surface", or "what should we be worried about in this codebase".
| name | verify |
| description | Verify harness changes end-to-end without docker — drive the real pinned CLI against a header-capturing stub server with the exact env resolve_auth_env() produces. |
The pipeline's real surface is the in-container claude -p process and its
outbound API requests. Without docker, drive the same pinned CLI binary
directly with the env dict the harness would inject via docker -e.
harness/agent_image.py:CLAUDE_CODE_VERSION):
npm install --no-save @anthropic-ai/claude-code@<pin> in a temp dir →
binary at node_modules/@anthropic-ai/claude-code/bin/claude.exe (the
.exe name is the real native-binary entry on Linux too, filled in by the
package's postinstall — not a Windows leftover).invalid_request_error
(non-retryable, so the CLI exits fast; exit=1 is expected).python3 -c "from harness.auth import resolve_auth_env; ..." and dump to
an export-lines file with shlex.quote (values contain newlines —
NEVER pass via env $(...), word-splitting mangles them; source the file).unset ANTHROPIC_CUSTOM_HEADERS (and any
other ambient var not in the resolved dict) before sourcing — a Claude
Code session in this repo injects .claude/settings.json env into shells,
which containers never see.ANTHROPIC_BASE_URL=http://127.0.0.1:<port> CLAUDECODE= IS_SANDBOX=1 timeout 30 <cli> -p hi --model claude-sonnet-4-5 --max-turns 1, then read
the captured JSONL.tests/test_patch.py / tests/test_patch_grade.py need
docker and fail on docker-less hosts — pre-existing, not your change.-e injection leg itself can't be exercised without docker;
it's the same mechanism that carries ANTHROPIC_API_KEY in production..claude/settings.json into a
fresh temp dir and run the host claude from there (with ambient
ANTHROPIC_CUSTOM_HEADERS unset so settings.json is the only source).