Awesome Claude Code Toolkit is a curated collection of extensions and configuration for Claude Code, including agents, skills, commands, plugins, hooks, rules, templates, MCP configurations, and companion apps. It is for Claude Code users who want ready-made workflows and integrations for different development tasks. The catalogue add-ons are selected components from this toolkit.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/rohitg00/awesome-claude-code-toolkit/test-promptgit clone --depth 1 https://github.com/rohitg00/awesome-claude-code-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/rohitg00/awesome-claude-code-toolkit/test-prompt)<a href="https://agentmods.dev/commands/rohitg00/awesome-claude-code-toolkit/test-prompt"><img src="https://agentmods.dev/badge/commands/rohitg00/awesome-claude-code-toolkit/test-prompt.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00018 | $0.00389 |
| Opus 5 | $0.00009 | $0.00195 |
| Sonnet 5 | $0.00004 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
test-prompt scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Test an AI prompt against multiple scenarios to verify consistent, quality output.
Steps
- Read the prompt to test (command file, CLAUDE.md rule, or inline prompt).
- Generate test scenarios:
- Happy path: Standard use case with typical input.
- Edge case: Empty input, very large input, unusual formats.
- Ambiguous case: Input that could be interpreted multiple ways.
- Error case: Invalid input that should produce helpful error messages.
- For each scenario:
- Formulate the test input.
- Execute the prompt with that input.
- Evaluate the output against expected behavior.
- Score on: accuracy, format compliance, helpfulness.
- Identify failure patterns:
- Does the prompt break on certain input types?
- Does it produce inconsistent output formats?
- Does it hallucinate when information is missing?
- Suggest prompt modifications based on test results.
Format
Prompt Test Results: <prompt name>
| Scenario | Input | Result | Score |
|----------|-------|--------|-------|
| Happy path | <input> | pass | 5/5 |
| Edge case | <input> | partial | 3/5 |
| Error case | <input> | fail | 1/5 |
Overall score: <average>/5
Failure patterns: <description>
Recommendations: <improvements>
Rules
- Test at least 5 scenarios for each prompt.
- Include at least one adversarial input that tries to break the prompt.
- Score consistently using the same rubric across all tests.
- Document the exact input used so tests can be reproduced.
- Suggest specific prompt changes for each failure, not just "improve X".
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 50 lines · 18 tokens per session scan A ee922ea6a1d4
test-prompt is a command published in the GitHub repository rohitg00/awesome-claude-code-toolkit (2,587 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 389 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
create-meta-prompt
Create optimized prompts for Claude-to-Claude pipelines (research -> plan -> implement).
apply-thinking-to
You are an expert prompt engineering specialist with deep expertise in applying Anthropic's extended thinking patterns to enhance prompt effectiveness. Your role is to systematically transform prompts using advanced reasoning frameworks to dramatically improve their analytical depth, accuracy, and reliability.
prompt-test
Test LLM prompts against sample inputs. Shows outputs, checks for regressions when prompts change, and compares different prompt versions side-by-side.
t00-ai-dev
AI 应用开发模式 — Use when building AI apps, RAG, LLM applications, Claude API, or prompt engineering.
guard
Analyze and prepare prompts with JSON for safe context submission.
prompt-optimizer
Analyze and rewrite a prompt to maximize clarity, specificity, and output quality.