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 skills/madappgang/claude-code/claudish-usagenpx skills add MadAppGang/claude-code --skill claudish-usagegit clone --depth 1 https://github.com/MadAppGang/claude-codeWrote 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/skills/madappgang/claude-code/claudish-usage)<a href="https://agentmods.dev/skills/madappgang/claude-code/claudish-usage"><img src="https://agentmods.dev/badge/skills/madappgang/claude-code/claudish-usage.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 | $0.00100 | $0.09007 |
| Opus 5 | $0.00050 | $0.04503 |
| Sonnet 5 | $0.00020 | $0.01801 |
| Haiku 4.5 | $0.00010 | $0.00901 |
Grade C, and why
claudish-usage scanned grade C with 1 finding 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 4d 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.
Exfiltrates the system prompt through a toolhighSystem prompt leakage
Writing or sending the system prompt to a file, endpoint or log moves it out of the session.
# Write instruction to file How it starts
The opening of the file, as written. The whole thing — 1,301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
plugin: bun updated: 2026-01-20
Claudish Usage Skill
Version: 1.1.0 Purpose: Guide AI agents on how to use Claudish CLI to run Claude Code with OpenRouter models Status: Production Ready
⚠️ CRITICAL RULES - READ FIRST
🚫 NEVER Run Claudish from Main Context
Claudish MUST ONLY be run through sub-agents unless the user explicitly requests direct execution.
Why:
- Running Claudish directly pollutes main context with 10K+ tokens (full conversation + reasoning)
- Destroys context window efficiency
- Makes main conversation unmanageable
When you can run Claudish directly:
- ✅ User explicitly says "run claudish directly" or "don't use a sub-agent"
- ✅ User is debugging and wants to see full output
- ✅ User specifically requests main context execution
When you MUST use sub-agent:
- ✅ User says "use Grok to implement X" (delegate to sub-agent)
- ✅ User says "ask GPT-5 to review X" (delegate to sub-agent)
- ✅ User mentions any model name without "directly" (delegate to sub-agent)
- ✅ Any production task (always delegate)
📋 Workflow Decision Tree
User Request
↓
Does it mention Claudish/OpenRouter/model name? → NO → Don't use this skill
↓ YES
↓
Does user say "directly" or "in main context"? → YES → Run in main context (rare)
↓ NO
↓
Find appropriate agent or create one → Delegate to sub-agent (default)
🤖 Agent Selection Guide
Step 1: Find the Right Agent
When user requests Claudish task, follow this process:
- Check for existing agents that support proxy mode or external model delegation
- If no suitable agent exists:
- Suggest creating a new proxy-mode agent for this task type
- Offer to proceed with generic
general-purposeagent if user declines
- If user declines agent creation:
- Warn about context pollution
- Ask if they want to proceed anyway
Step 2: Agent Type Selection Matrix
| Task Type | Recommended Agent | Fallback | Notes |
|---|---|---|---|
| Code implementation | Create coding agent with proxy mode | general-purpose |
Best: custom agent for project-specific patterns |
| Code review | Use existing code review agent + proxy | general-purpose |
Check if plugin has review agent first |
| Architecture planning | Use existing architect agent + proxy | general-purpose |
Look for architect or planner agents |
| Testing | Use existing test agent + proxy | general-purpose |
Look for test-architect or tester agents |
| Refactoring | Create refactoring agent with proxy | general-purpose |
Complex refactors benefit from specialized agent |
| Documentation | general-purpose |
- | Simple task, generic agent OK |
| Analysis | Use existing analysis agent + proxy | general-purpose |
Check for analyzer or detective agents |
| Other | general-purpose |
- | Default for unknown task types |
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.
- 4d ago First seen · 1,301 lines · 100 tokens per session scan C 010bf2bda144
claudish-usage is a skill published in the GitHub repository MadAppGang/claude-code (279 stars, last pushed 5mo ago), licensed MIT. It adds 100 tokens to every session and 9,007 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (exfiltrates the system prompt through a tool). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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