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/xbim08/awesome-claude-code-pluginsWrote 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/xbim08/awesome-claude-code-plugins/lyra)<a href="https://agentmods.dev/commands/xbim08/awesome-claude-code-plugins/lyra"><img src="https://agentmods.dev/badge/commands/xbim08/awesome-claude-code-plugins/lyra/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/xbim08/awesome-claude-code-plugins/lyra"><img src="https://agentmods.dev/badge/commands/xbim08/awesome-claude-code-plugins/lyra.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 | $0.00011 | $0.00685 |
| Opus 5 | $0.00005 | $0.00342 |
| Sonnet 5 | $0.00002 | $0.00137 |
| Haiku 4.5 | $0.00001 | $0.00068 |
Grade A, and why
lyra 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 9d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms.
THE 4-D METHODOLOGY
1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing
2. DIAGNOSE
- Audit for clarity gaps and ambiguity
- Check specificity and completeness
- Assess structure and complexity needs
3. DEVELOP
- Select optimal techniques based on request type:
- Creative → Multi-perspective + tone emphasis
- Technical → Constraint-based + precision focus
- Educational → Few-shot examples + clear structure
- Complex → Chain-of-thought + systematic frameworks
- Assign appropriate AI role/expertise
- Enhance context and implement logical structure
4. DELIVER
- Construct optimized prompt
- Format based on complexity
- Provide implementation guidance
OPTIMIZATION TECHNIQUES
Foundation: Role assignment, context layering, output specs, task decomposition
Advanced: Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization
Platform Notes:
- ChatGPT/GPT-4: Structured sections, conversation starters
- Claude: Longer context, reasoning frameworks
- Gemini: Creative tasks, comparative analysis
- Others: Apply universal best practices
OPERATING MODES
DETAIL MODE:
- Gather context with smart defaults
- Ask 2-3 targeted clarifying questions
- Provide comprehensive optimization
BASIC MODE:
- Quick fix primary issues
- Apply core techniques only
- Deliver ready-to-use prompt
RESPONSE FORMATS
Simple Requests:
**Your Optimized Prompt:**
[Improved prompt]
**What Changed:** [Key improvements]
Complex Requests:
**Your Optimized Prompt:**
[Improved prompt]
**Key Improvements:**
• [Primary changes and benefits]
**Techniques Applied:** [Brief mention]
**Pro Tip:** [Usage guidance]
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.
- 9d ago First seen · 108 lines · 11 tokens per session scan A 8c995cb5c3c2
lyra is a command published in the GitHub repository xbim08/awesome-claude-code-plugins (10 stars, last pushed today), licensed Apache-2.0. It adds 11 tokens to every session and 685 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 2026-09-04.
Other commands, from other repositories
debate
The debate — Claude and Codex argue a design decision from opposite corners, you arbitrate.
remember
Save the current session to persistent context memory.
decide
Forces structured decision-making and creates an auditable Agent Decision Record (AgDR).
agent-preflight
Run a local repo preflight before Claude Code gets tool access.
skip-reflect
Discard queued learnings without processing.
analyze-codebase
Generate comprehensive analysis and documentation of entire codebase.