Agent skill
verify
Verify a code change by running the app, the relevant command, or a focused server flow and reporting concrete evidence.
Install this agent skill to your Project
npx add-skill https://github.com/oboard/claude-code-rev/tree/main/src/skills/bundled/verify
SKILL.md
Verify
Use this skill when a task is not finished until the change is exercised.
Goal
Produce a short verification result grounded in execution, not inference. Prefer the narrowest check that proves the changed behavior works.
Workflow
- Identify the changed surface area.
- Pick the smallest realistic verification path.
- Run the relevant command or request flow.
- Capture the observable result: exit status, key output, HTTP status, or changed behavior.
- Report what passed, what was not verified, and any remaining risk.
Rules
- Do not claim success without running something.
- Prefer focused checks over broad smoke tests.
- If the repo has no formal test target, use the nearest runnable workflow.
- If a check is blocked by environment limits, state that explicitly.
- Include exact commands when they are useful to repeat the verification.
Verification Patterns
CLI changes
- Run the exact command path affected by the edit.
- Check help text, flags, output formatting, exit codes, and side effects.
- For interactive flows, prefer the most scriptable subcommand first.
See examples/cli.md.
Server changes
- Start only the needed service.
- Exercise the changed route, handler, or background path.
- Validate status code, response shape, logs, and failure handling.
See examples/server.md.
Reporting Format
Verified:what you ran and what passed.Not verified:anything you could not run.Risk:the main remaining uncertainty, if any.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
claude-api
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Claude Code Guide
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Didn't find tool you were looking for?