How do I install the Verify skill?

Run npx skillmds@latest add anthropics/verify in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Verify safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Verify?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Verify free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Verify?

anthropics (@anthropics) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Verify by anthropics — Agent Skill

Name: Verify
Rating: 4.2 (5 reviews)
Author: anthropics

File contents

Verifying harness changes on a docker-less host

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.

Recipe

Get the pinned CLI (version from 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).
Stub API server: a tiny HTTP server that appends each request's headers to a JSONL file and returns a 400 invalid_request_error (non-retryable, so the CLI exits fast; exit=1 is expected).
Build the agent env exactly as the pipeline does: 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).
Emulate the container env: 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.
Run: 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.

Gotchas

Unit tests in tests/test_patch.py / tests/test_patch_grade.py need docker and fail on docker-less hosts — pre-existing, not your change.
The docker -e injection leg itself can't be exercised without docker; it's the same mechanism that carries ANTHROPIC_API_KEY in production.
For the interactive-skills surface, copy .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).

---name: verifydescription: 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.--- # Verifying harness changes on a docker-less host 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`. ## Recipe 1. **Get the pinned CLI** (version from `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). 2. **Stub API server**: a tiny HTTP server that appends each request's headers to a JSONL file and returns a 400 `invalid_request_error` (non-retryable, so the CLI exits fast; exit=1 is expected). 3. **Build the agent env exactly as the pipeline does**: `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). 4. **Emulate the container env**: `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. 5. **Run**: `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. ## Gotchas - Unit tests in `tests/test_patch.py` / `tests/test_patch_grade.py` need docker and fail on docker-less hosts — pre-existing, not your change. - The docker `-e` injection leg itself can't be exercised without docker; it's the same mechanism that carries `ANTHROPIC_API_KEY` in production. - For the interactive-skills surface, copy `.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).

Frequently asked questions

Verify