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 jasonkneen/kiro --skill ai-promptinggit clone --depth 1 https://github.com/jasonkneen/kiroWrote 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/jasonkneen/kiro/ai-prompting)<a href="https://agentmods.dev/skills/jasonkneen/kiro/ai-prompting"><img src="https://agentmods.dev/badge/skills/jasonkneen/kiro/ai-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/jasonkneen/kiro/ai-prompting"><img src="https://agentmods.dev/badge/skills/jasonkneen/kiro/ai-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 | $0.00039 | $0.02125 |
| Opus 5 | $0.00019 | $0.01063 |
| Sonnet 5 | $0.00008 | $0.00425 |
| Haiku 4.5 | $0.00004 | $0.00213 |
Grade A, and why
ai-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 11d 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Prompting Strategies
Master the art of communicating with AI coding assistants to get better results faster. These strategies are optimized for spec-driven development but apply broadly to AI collaboration.
When to Use This Skill
Use these prompting strategies when:
- Working with Claude Code, Cursor, or other AI assistants
- Creating specs through AI collaboration
- Getting inconsistent or low-quality AI responses
- Need to improve AI output accuracy
- Want faster iteration cycles
Core Strategies
Strategy 1: Context-First Prompting
Always provide sufficient context before making requests.
Poor Approach:
Create requirements for a user profile feature.
Better Approach:
I'm working on a web application for a fitness tracking platform. We need to add user profile functionality where users can manage their personal information and fitness goals.
Context:
- Technology: React frontend, Node.js backend
- User base: Health-conscious individuals, age 18-65
- Key constraint: Must comply with GDPR for EU users
- Integration: Will connect with existing authentication system
Please help me create requirements for the user profile feature.
Why It Works:
- Provides domain context for better decisions
- Identifies technical constraints early
- Clarifies compliance requirements
- Enables more relevant suggestions
Strategy 2: Phased Interaction
Work through spec phases sequentially. Complete each phase before moving to the next.
Phase 1 - Requirements:
Let's start with the requirements phase for [feature name].
Current situation: [describe current state]
Problem to solve: [describe the problem]
Users affected: [describe user types]
Success criteria: [how we'll know it works]
Please help me develop comprehensive requirements using the EARS format.
Phase 2 - Design (after requirements approved):
Now that we have clear requirements, let's create the technical design.
Requirements summary: [key requirements]
Technical context: [architecture, frameworks, patterns]
Constraints: [performance, scalability, security]
Please propose a technical design that addresses these requirements.
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.
- 11d ago First seen · 393 lines · 39 tokens per session scan A 0526b557a643
ai-prompting is a skill published in the GitHub repository jasonkneen/kiro (746 stars, last pushed 8mo ago), licensed MIT. It adds 39 tokens to every session and 2,125 once invoked, about $0.0002 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 2026-08-30.
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