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auto-agent: Skill for Claude Code

.claude/skills/prompt-engineering/SKILL.md

prompt-engineering is a skill for Claude Code from alfonsograziano/auto-agent. It costs 109 tokens per session (1,284 once invoked), scanned A, original, MIT.

A guide to writing clear instructions for large language models (LLMs), the systems that generate text or code from prompts. It provides rules for making those instructions shorter, clearer, and easier to test.

In plain words
What is it for?
Use it when creating, reviewing, or improving system prompts for an AI agent, tool, or workflow.
Why use it?
It helps reduce conflicting instructions and makes an agent more likely to follow the rules that matter.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is alfonsograziano/auto-agent's own configuration. It tells Claude Code how to work on auto-agent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything auto-agent configures →

not rated 58repo 7mo ago A scan 109 tokens original MIT
View source ↗ alfonsograziano/auto-agent
Reuse

Borrowing it

Nothing to install: this file belongs to alfonsograziano/auto-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/alfonsograziano/auto-agent/master/.claude/skills/prompt-engineering/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/alfonsograziano/auto-agent

Made for: Claude Code.

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Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,284 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00109 $0.01284
Opus 5.5 $0.00044 $0.00514
Sonnet 5.5 $0.00022 $0.00257
Haiku 4.5 $0.00011 $0.00128

Measured 2mo ago against content hash 85e5f4aee34d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/skills/prompt-engineering/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prompt Engineering

Reference for writing and reviewing LLM system prompts. Based on Harada et al. (2024) — Curse of Instructions, EARS syntax, and ARQ pattern.

1. Minimize the Rule Count (Curse of Instructions)

The probability of the model following all rules simultaneously is approximately P = p^N, where N is the number of independently verifiable instructions and p is the per-rule adherence rate.

N rules p = 0.95 p = 0.90
5 77% 59%
10 60% 35%
20 36% 12%
30 21% 4%

Target: 10 or fewer distinct rules in the system prompt.

The math is unforgiving — every additional rule multiplicatively reduces the chance the model follows all of them. This is why minimizing rule count matters more than perfecting any single rule.

  • Move procedural/structural rules (e.g., parameter formatting, count constraints) into tool descriptions, not the system prompt.
  • Merge rules that protect the same invariant into one EARS statement.
  • Delete rules already enforced by the tool schema or the agent framework.

2. Write Rules in EARS Syntax

Easy Approach to Requirements Syntax produces unambiguous, testable rules. Free-form instructions invite misinterpretation because the model has to guess the scope and trigger condition. EARS removes that ambiguity.

Four forms:

Form Template Use when
Ubiquitous The system shall [action]. Rule always applies
Event-driven When [trigger], the system shall [action]. Rule fires on a condition
State-driven While [condition], the system shall [action]. Rule applies during a state
Unwanted behavior If [situation], the system shall [response]. Error / boundary case

Before (free-form): "NEVER output a URL that contains {product_name} as a literal string. Always make sure the product name is properly substituted before including any link."

Read the full file on GitHub · 94 lines

Changes

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.

  1. 2mo ago First seen · 94 lines · 109 tokens per session scan A 85e5f4aee34d

Subscribe to this mod's changes

prompt-engineering is a skill published in the GitHub repository alfonsograziano/auto-agent (58 stars, last pushed 7mo ago), licensed MIT. It adds 109 tokens to every session and 1,284 once invoked, about $0.0004 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.

Data and AIPrompting
Related

prompt-engineering compared with skills from other repositories

prompt-engineering next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
prompt-engineeringalfonsograziano/auto-agentSkill587mo agoMITA109Claude Code
llm-app-patternsdavila7/claude-code-templatesSkill33kyesterdayMITA54Claude Code, Codex
prompt-engineerJeffallan/claude-skillsSkill12k7d agoMITA93Claude Code
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omh-model-optimizationrlaope/oh-my-hermesSkill5.2kyesterdayMITA84Claude Code, Codex
ideogram4digitalsamba/claude-code-video-toolkitSkill2.5kyesterdayMITA99Claude Code
prompt-library-for-startupsaws/agent-toolkit-for-awsSkill3.4kyesterdayApache-2.0A228Claude Code