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 skills add CarbeneAI/Forge --skill promptinggit clone --depth 1 https://github.com/CarbeneAI/ForgeWrote 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/carbeneai/forge/prompting)<a href="https://agentmods.dev/skills/carbeneai/forge/prompting"><img src="https://agentmods.dev/badge/skills/carbeneai/forge/prompting/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/skills/carbeneai/forge/prompting"><img src="https://agentmods.dev/badge/skills/carbeneai/forge/prompting.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 | $0.00037 | $0.00570 |
| Opus 5 | $0.00018 | $0.00285 |
| Sonnet 5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
prompting 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 6d 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.
This is a copy
100% identical to prompting — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompting Skill
When to Activate This Skill
- Prompt engineering questions
- Context engineering guidance
- AI agent design
- Prompt structure help
- Best practices for LLM prompts
- Agent configuration
Core Philosophy
Context engineering = Curating optimal set of tokens during LLM inference
Primary Goal: Find smallest possible set of high-signal tokens that maximize desired outcomes
Key Principles
1. Context is Finite Resource
- LLMs have limited "attention budget"
- Performance degrades as context grows
- Every token depletes capacity
- Treat context as precious
2. Optimize Signal-to-Noise
- Clear, direct language over verbose explanations
- Remove redundant information
- Focus on high-value tokens
3. Progressive Discovery
- Use lightweight identifiers vs full data dumps
- Load detailed info dynamically when needed
- Just-in-time information loading
Markdown Structure Standards
Use clear semantic sections:
- Background Information: Minimal essential context
- Instructions: Imperative voice, specific, actionable
- Examples: Show don't tell, concise, representative
- Constraints: Boundaries, limitations, success criteria
Writing Style
Clarity Over Completeness
✅ Good: "Validate input before processing" ❌ Bad: "You should always make sure to validate..."
Be Direct
✅ Good: "Use calculate_tax tool with amount and jurisdiction" ❌ Bad: "You might want to consider using..."
Use Structured Lists
✅ Good: Bulleted constraints ❌ Bad: Paragraph of requirements
Context Management
Just-in-Time Loading
Don't load full data dumps - use references and load when needed
Structured Note-Taking
Persist important info outside context window
Sub-Agent Architecture
Delegate subtasks to specialized agents with minimal context
Best Practices Checklist
- Uses Markdown headers for organization
- Clear, direct, minimal language
- No redundant information
- Actionable instructions
- Concrete examples
- Clear constraints
- Just-in-time loading when appropriate
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.
- 6d ago First seen · 93 lines · 37 tokens per session scan A ceaa98f4ff0e
prompting is a skill published in the GitHub repository CarbeneAI/Forge (9 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 570 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prompting, differing in 0 lines, and is treated as a copy.
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prompt-engineer
Prompt engineering expert for chain-of-thought, few-shot learning, evaluation, and LLM optimization.
prompt-lab
LLM prompt engineering: analyzes failure modes, generates variants (direct, few-shot, CoT), designs rubrics, produces test suites. Triggers on: "prompt engineering", "generate prompt variants", "A/B test prompts", "optimize prompt", "improve this prompt". NOT for SKILL.md files, use skill-evaluator.
Prompt Engineering
Write better prompts for AI tools — structured, specific, and optimized for quality output.