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/comparegit 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/compare)<a href="https://agentmods.dev/commands/rohitg00/awesome-claude-code-toolkit/compare"><img src="https://agentmods.dev/badge/commands/rohitg00/awesome-claude-code-toolkit/compare.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 | $0.00016 | $0.00164 |
| Opus 5 | $0.00008 | $0.00082 |
| Sonnet 5 | $0.00003 | $0.00033 |
| Haiku 4.5 | $0.00002 | $0.00016 |
Grade A, and why
compare 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 yesterday.
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
Compare multiple ML experiment runs side-by-side to identify the best configuration.
Steps
- Load experiment records from the tracking store.
- Select experiments to compare:
- Build a comparison table:
- Analyze parameter sensitivity:
- Generate visualizations:
- Identify the winning configuration:
- Recommend next experiments to try.
Format
Comparison: <N> experiments
Best Run: <experiment name>
Key Findings:
- <parameter X> has <impact> on <metric Y>
Rules
- Only compare experiments with the same dataset version.
- Use consistent metrics across all compared runs.
- Statistical significance matters; do not draw conclusions from single runs.
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.
- yesterday First seen · 36 lines · 16 tokens per session scan A efe525af246a
compare 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 16 tokens to every session and 164 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 2026-09-03.
Other commands, from other repositories
create-meta-prompt
Create optimized prompts for Claude-to-Claude pipelines (research -> plan -> implement).
two
Research & create an implementation plan with 2 approaches.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.