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/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/generate-llms-txt)<a href="https://agentmods.dev/commands/technickai/ai-coding-config/generate-llms-txt"><img src="https://agentmods.dev/badge/commands/technickai/ai-coding-config/generate-llms-txt/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/technickai/ai-coding-config/generate-llms-txt"><img src="https://agentmods.dev/badge/commands/technickai/ai-coding-config/generate-llms-txt.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 | $0.00023 | $0.01347 |
| Opus 5.5 | $0.00009 | $0.00539 |
| Sonnet 5.5 | $0.00005 | $0.00269 |
| Haiku 4.5 | $0.00002 | $0.00135 |
Grade A, and why
generate-llms-txt 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 25d 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.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate llms.txt
Creates or updates llms.txt - a standardized file that helps Large Language Models understand and navigate your website or documentation at inference time.
Think: "What does an LLM need to understand and effectively use this site?"
Choose the location that makes the file accessible at /llms.txt when the site is deployed.
# Project Name
> Brief 1-2 sentence description of what this project/site is and what it helps users
> accomplish.
Additional context paragraph (optional): More detailed information about the project's
purpose, key features, or important background that helps LLMs understand how to use the
site effectively.
## Documentation
- [Getting Started](url): Brief description of what this covers
- [Core Concepts](url): Brief description
- [API Reference](url): Brief description
## Guides
- [Guide Title](url): Brief description
- [Another Guide](url): Brief description
## Reference
- [Configuration](url): Brief description
- [CLI Commands](url): Brief description
- [Architecture](url): Brief description
## Optional
- [Less Critical Resource](url): Brief description
- [Advanced Topics](url): Brief description
Structure requirements:
- H1 title with project name
- Blockquote summary for quick context
- H2 sections organizing documentation by category
- Markdown links with concise descriptions
- "Optional" section for less critical resources
Link format:
- Use relative paths for internal documentation
- Use absolute URLs for external resources
- Prioritize most important links first within each section
Organize by importance:
- Getting started and quickstart guides
- Core concepts and tutorials
- API reference and technical documentation
- Advanced topics and edge cases
- Contributing and community resources
Target file size: Under 2KB for focused navigation.
# AI Coding Config
> Plugin marketplace for Claude Code and Cursor providing reusable coding standards,
> workflows, and AI agents.
Provides a marketplace of plugins that bundle coding rules, slash commands, and
specialized agents. Plugins maintain single source of truth through symlinks.
## Documentation
- [Getting Started](README.md): Installation and basic setup
- [Plugin Structure](docs/plugin-structure.md): How plugins are organized
- [Available Plugins](plugins/README.md): Browse the marketplace
## Development
- [Creating Plugins](docs/creating-plugins.md): Build your own plugins
- [Bootstrap Script](scripts/bootstrap.sh): Automated setup
- [Git Workflow](.cursor/rules/git-interaction.mdc): Version control standards
## Reference
- [Cursor Rules](rules/): Coding standards and conventions
- [Claude Commands](.claude/commands/): Available slash commands
- [AI Agents](.claude/agents/): Specialized agent capabilities
## Optional
- [Contributing](CONTRIBUTING.md): How to contribute
- [Heart-Centered AI](.cursor/rules/heart-centered-ai-philosophy.mdc): Philosophy
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.
- 25d ago First seen · 204 lines · 23 tokens per session scan A 41cde2d1250a
generate-llms-txt is a command published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 23 tokens to every session and 1,347 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-22.
Other commands, from other repositories
ai-feature-eval-harness
Design an eval plan for a product AI feature: success criteria, a held-out labeled dataset, per-criterion grading (code-based, then LLM-based), and a pass threshold. Use when a task ships or changes a feature with non-deterministic model output (classification, extraction, summarization, ranking, agent action) needing…
prompt
Convert a rough ask into a structured XML-tagged prompt using the factory-prompting vocabulary.
data-pipeline
Design and implement ETL/ELT data pipelines.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
log
Show the last N captured prompts in this repo (git-log style, defaults to 10).
dejavu-weaviate-rescue
Weaviate corruption rescue toolkit — scan, quarantine-segment, redo-class. Codifies the 2026-05-14 LSM-segment rescue playbook.