Home / jnpiyush / agentx · .github/skills/ai-systems/prompt-engineering/SKILL.md · GitHub

prompt-engineering skillA

prompt-engineering is agent-read markdown (skill) from jnpiyush/agentx: Use when designing coding-agent prompts, tool contracts, structured outputs and model-adaptive context, or evaluating prompt changes..

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# Prompt Engineering

## When to Use

Use for system prompts, tool-use instructions, structured responses or prompt
regressions. This skill makes repo-specific acceptance and host limits explicit.

## Prerequisites

Read the target task, active host capabilities and existing evaluation cases.
Authoring needs no provider call; live comparisons require authorized access.

## Decision Guide

Use direct instructions first; add examples for repeated ambiguity, structured
schemas for machine consumers, and reference retrieval for large context.

## Core Rules

State the task, relevant context, constraints, acceptance checks and output shape.
Use the smallest prompt that passes representative evaluations. Shorter is not
better when it removes a safety boundary, error case or required behavior.

## Workflow

1. Inspect the active host's available models, tool schemas, context/output
   limits and supported reasoning controls. Names in frontmatter are preferences,
   not proof that the host executed that model. Record the resolved configuration.
2. Start with direct instructions. Add examples only for demonstrated ambiguity.
…

Read the whole file at its exact version.

How to install

Latest version
mdr add jnpiyush/agentx/prompt-engineering@v1.1.0
Exact content
mdr add jnpiyush/agentx/prompt-engineering@sha256:dc758773942f4471

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_qngikss5gzdgbwkx.svg)](https://markdownregistry.com/a/art_qngikss5gzdgbwkx)

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Versions

versioncommittedcommitsizeaudit
v1.1.0 latest2026-09-05 586498e 4,832 BA view · diff
v1.0.02026-09-03 b3f2610 8,500 BF view · diff
v1.0.02026-03-04 848caee 8,478 BF view · diff
v1.0.02026-03-01 969f335 8,473 BF view · diff
v1.0.02026-02-16 30e409a 8,475 BF view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (4832 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

jnpiyush/agentx · 16 stars · license Apache-2.0 · pushed 2026-09-21 · branch master

API

GET https://markdownregistry.com/api/v1/artifacts/art_qngikss5gzdgbwkx
GET https://markdownregistry.com/api/v1/resolve?ref=jnpiyush/agentx/prompt-engineering
GET https://markdownregistry.com/api/v1/blob/dc758773942f4471e13447aa2ddbe6c7022eb53e3908c69989bc94dd389c4d47

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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