TÂCHES Claude Code Resources is a collection of custom commands, skills, and agents that structure Claude Code workflows such as planning, debugging, automation, and subagent creation. It is intended for developers who use Claude Code for real software projects. The catalogue entries are examples of the resources included in the collection.
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.
git clone --depth 1 https://github.com/glittercowboy/taches-cc-resourcesWrote 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/glittercowboy/taches-cc-resources/create-meta-prompt)<a href="https://agentmods.dev/commands/glittercowboy/taches-cc-resources/create-meta-prompt"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/create-meta-prompt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/glittercowboy/taches-cc-resources/create-meta-prompt"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/create-meta-prompt.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00015 | $0.00050 |
| Opus 5 | $0.00008 | $0.00025 |
| Sonnet 5 | $0.00003 | $0.00010 |
| Haiku 4.5 | $0.00002 | $0.00005 |
Grade A, and why
create-meta-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 11d 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
Invoke the create-meta-prompts skill for: $ARGUMENTS
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.
- 11d ago First seen · 8 lines · 15 tokens per session scan A 030dd7453afa
create-meta-prompt is a command published in the GitHub repository glittercowboy/taches-cc-resources (1,976 stars, last pushed 5mo ago), licensed MIT. It adds 15 tokens to every session and 50 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
improve-prompt
Analyze and improve an AI prompt for clarity, specificity, and effectiveness.
test-prompt
Test an AI prompt against multiple scenarios to verify consistent, quality output.
t00-ai-dev
A set of instructions for building applications that use artificial intelligence, such as chatbots, document search, or text-generation tools. It covers Claude, retrieval-augmented generation (RAG), embeddings, and vector databases, which help find relevant documents for an AI answer.
prompt
Transform a vague prompt into a precision-crafted one — for AI generation, prompt templates in services or pipelines, or system prompts — or debug why an AI response is poor.
devkit.prompt-optimize
Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions.
prompt-history
Manage history of created and optimized prompts.