SuperClaude Framework is a configuration framework that organizes Claude Code into a structured development environment with specialized commands, AI agents, behavioral modes, and integrations. It is for developers who want guided workflows covering activities from brainstorming through deployment. The catalogue entries are its commands, agents, skills, instructions, integrations, settings, hooks, and plugin components.
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/SuperClaude-Org/SuperClaude_FrameworkWrote 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/superclaude-org/superclaude_framework/recommend)<a href="https://agentmods.dev/commands/superclaude-org/superclaude_framework/recommend"><img src="https://agentmods.dev/badge/commands/superclaude-org/superclaude_framework/recommend/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/superclaude-org/superclaude_framework/recommend"><img src="https://agentmods.dev/badge/commands/superclaude-org/superclaude_framework/recommend.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 | $0.00022 | $0.08194 |
| Opus 5.5 | $0.00009 | $0.03278 |
| Sonnet 5.5 | $0.00004 | $0.01639 |
| Haiku 4.5 | $0.00002 | $0.00819 |
Grade A, and why
sc:recommend 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sc:recommend — 100% identical, 249 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,006 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SuperClaude Intelligent Command Recommender
Purpose: Ultra-intelligent command recommendation engine - recommends the most suitable SuperClaude commands for any user input
Command Definition
/sc:recommend [user request] --options [flags]
Multi-language Support
Language Detection and Translation System
language_mapping:
turkish_keywords:
machine_learning: ["machine learning", "ml", "artificial intelligence", "ai"]
website: ["website", "web site", "site", "page"]
application: ["app", "application", "program", "software"]
error: ["error", "bug", "issue", "problem"]
performance: ["performance", "speed", "fast", "optimization"]
new: ["new", "create", "build", "start", "develop"]
analysis: ["analyze", "analysis", "examine", "research"]
english_keywords:
machine learning: ["machine learning", "artificial intelligence", "ml", "ai"]
website: ["website", "site", "page", "web application"]
performance: ["performance", "speed", "optimization", "speed"]
error: ["error", "issue", "bug", "problem"]
universal_patterns:
question_words: ["how", "what", "why", "which"]
action_words: ["do", "create", "build", "develop"]
help_words: ["help", "suggest", "recommend", "learn"]
Language Detection Algorithm
def detect_language_and_translate(input_text):
turkish_chars = ['ç', 'ğ', 'ı', 'ö', 'ş', 'ü']
if any(char in input_text.lower() for char in turkish_chars):
return "tr"
english_common = ["the", "and", "is", "are", "was", "were", "will", "would", "could", "should"]
if any(word in input_text.lower().split() for word in english_common):
return "en"
return "en" # Default to English
Multi-language Examples
# Turkish examples
/sc:recommend "makine öğrenmesi algoritması başlat"
/sc:recommend "sitem yavaş açılıyor, ne yapayım?"
/sc:recommend "yeni bir özellik eklemeliyim"
/sc:recommend "hata alıyorum, çözüm bul"
# English examples
/sc:recommend "I want to build ML algorithm"
/sc:recommend "my website is slow, help me optimize"
/sc:recommend "I need to add a new feature"
/sc:recommend "getting errors, need debugging"
# Mixed language
/sc:recommend "makine learning projesi yapmak istiyorum"
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.
- 1mo ago First seen · 1,006 lines · 22 tokens per session scan A bd00fffbe126
sc:recommend is a command published in the GitHub repository SuperClaude-Org/SuperClaude_Framework (23,905 stars, last pushed 20d ago), licensed MIT. It adds 22 tokens to every session and 8,194 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 2026-08-30.
Other commands, from other repositories
troubleshoot
Diagnose and resolve issues in code, builds, deployments, and system behavior.
analyze
Comprehensive code analysis across quality, security, performance, and architecture domains.
10_gofer_cloud
Deploy and configure the Gofer cloud integration for remote pipeline execution.
7a_stakeholder_comms
Generate stakeholder-facing communications: release notes, demo scripts, and change briefs.
9_gofer_tests
Generate comprehensive test suites from four testing perspectives for a target component.
gofer_hydrate
Reverse-engineer specification from existing code (Hydration).