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 JeremyDev87/codingbuddy --skill prompt-engineeringgit clone --depth 1 https://github.com/JeremyDev87/codingbuddyWrote 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/jeremydev87/codingbuddy/prompt-engineering)<a href="https://agentmods.dev/skills/jeremydev87/codingbuddy/prompt-engineering"><img src="https://agentmods.dev/badge/skills/jeremydev87/codingbuddy/prompt-engineering/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/jeremydev87/codingbuddy/prompt-engineering"><img src="https://agentmods.dev/badge/skills/jeremydev87/codingbuddy/prompt-engineering.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 | $0.00042 | $0.01890 |
| Opus 5.5 | $0.00017 | $0.00756 |
| Sonnet 5.5 | $0.00008 | $0.00378 |
| Haiku 4.5 | $0.00004 | $0.00189 |
Grade A, and why
prompt-engineering 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.
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Overview
A prompt is an API contract with an AI system. Precision matters. Ambiguous prompts produce inconsistent results; clear prompts produce consistent, predictable behavior.
Core principle: Prompts are executable specifications. Write them like you write tests: with clear inputs, expected behavior, and success criteria.
Iron Law:
TEST YOUR PROMPT WITH AT LEAST 3 DIFFERENT INPUTS BEFORE USING IN PRODUCTION
One input is anecdote. Three is pattern. Ten is confidence.
When to Use
- Writing system prompts for codingbuddy agents
- Creating CLAUDE.md / .cursorrules instructions
- Designing tool descriptions for MCP servers
- Optimizing prompts that produce inconsistent results
- Building prompt chains for multi-step workflows
Prompt Anatomy
Every effective prompt has these components:
┌─────────────────────────────────────────┐
│ ROLE Who/what is the AI? │
│ CONTEXT What situation are we in? │
│ TASK What specifically to do? │
│ CONSTRAINTS What rules must be obeyed? │
│ FORMAT How to structure output? │
│ EXAMPLES Show, don't just tell │
└─────────────────────────────────────────┘
Not every prompt needs all components, but most production prompts need most of them.
Prompt Patterns
Pattern 1: Role + Task (Basic)
You are a [specific role].
Your task: [specific action] for [specific context].
Example:
You are a TypeScript code reviewer specializing in security.
Your task: Review the authentication module below for OWASP Top 10 vulnerabilities.
Output a list of findings ordered by severity (Critical → High → Medium → Low).
Pattern 2: Chain-of-Thought (Complex Reasoning)
Force step-by-step reasoning before conclusions:
Before answering, think through:
1. [First consideration]
2. [Second consideration]
3. [Third consideration]
Then provide your conclusion.
Example:
Before suggesting a fix, think through:
1. What is the root cause of this bug?
2. What are the possible fix approaches?
3. What are the trade-offs of each approach?
4. Which approach has the least risk?
Then provide your recommendation with rationale.
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 · 319 lines · 42 tokens per session scan A 7696744fc5a6
prompt-engineering is a skill published in the GitHub repository JeremyDev87/codingbuddy (31 stars, last pushed 6mo ago), licensed MIT. It adds 42 tokens to every session and 1,890 once invoked, about $0.0002 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 skills, from other repositories
kanso-prompting
Use when rewriting, sharpening, or producing a prompt for Claude or another current frontier model. Sets standing rules for getting better LLM output through better prompts. Loaded by /kanso-task; not directly invoked by the user.
cost-aware-llm-pipeline
A planning guide for choosing language models and managing the amount of conversation context used by an AI coding workflow. It groups tasks by complexity and gives rules for avoiding context overflow during long sessions.
llm-patterns
AI-first application patterns, LLM testing, prompt management.
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prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…
anthropic-claude-development
Expert guidance for Anthropic Claude API development including Messages API, tool use, prompt engineering, and building production applications with Claude models.