coleam00/context-engineering-intro: a PRP workflow for Claude Code

The problem it targets: agent failures that are really context failures

The README makes a specific claim about where AI coding assistants break: "Most agent failures aren't model failures - they're context failures." That framing is the whole reason the repository exists. Instead of tuning a single prompt until the model behaves, you assemble the material the assistant needs before it writes anything: project rules, example code, links to documentation, and the gotchas that a model without your codebase in view would never guess.

The intended user is someone already running Claude Code on a real project and repeatedly disappointed by the output. The README is blunt that Claude Code is the centre of the repository, while adding that the strategy can be applied with any AI coding assistant. That second half matters, because the shipped artifacts are Claude Code slash commands, so porting the idea elsewhere means rewriting the commands rather than reusing them.

This is a template repository, not a package. There is nothing to import and no runtime to add to your application. You clone it, edit a few Markdown files, and the value you get back is proportional to how much real context you put in.

How the PRP loop actually moves data through the repository

The mechanism is a two-stage transformation. You write a feature request in INITIAL.md using four headings: FEATURE, EXAMPLES, DOCUMENTATION, OTHER CONSIDERATIONS. Then `/generate-prp INITIAL.md` reads that file, researches the codebase for patterns, searches for relevant documentation, and writes a PRP into PRPs/your-feature-name.md. A PRP is described as a comprehensive implementation blueprint containing context, implementation steps with validation, error handling patterns and test requirements. The README positions it as similar to a PRD but written to instruct an AI assistant rather than a human team.

The second stage is `/execute-prp PRPs/your-feature-name.md`. According to the README, the assistant reads the PRP, creates an implementation plan, executes each step with validation, runs tests, fixes issues, and checks the success criteria. That validation loop is the part worth noticing: the PRP is supposed to carry its own pass/fail gates, so the assistant can detect a broken step instead of declaring victory.

The commands themselves live in .claude/commands/generate-prp.md and .claude/commands/execute-prp.md, and the README tells you to read them to see how research and implementation are performed. They receive their input through a `$ARGUMENTS` variable holding whatever you typed after the command name. CLAUDE.md supplies the standing rules that apply in every conversation: project awareness, file size limits, module organisation, testing expectations, style conventions and documentation standards. Examples live in examples/, which the repository structure marks as critical, and the shipped examples/.gitkeep means the folder arrives empty on purpose.

Installing the template and running your first PRP

There is no install step in the usual sense. The README's Quick Start is a clone followed by edits, and the commands run inside Claude Code rather than in a shell. Cloning gives you the directory the rest of the workflow assumes.

git clone https://github.com/coleam00/Context-Engineering-Intro.git
cd Context-Engineering-Intro

Next you fill in INITIAL.md. The README gives this shape for the file, and the FEATURE section is where specificity pays off: the README contrasts a vague "Build a web scraper" with a request that names the library, the target sites, rate limiting and the storage layer.

## FEATURE:
[Describe what you want to build - be specific about functionality and requirements]

## EXAMPLES:
[List any example files in the examples/ folder and explain how they should be used]

## DOCUMENTATION:
[Include links to relevant documentation, APIs, or MCP server resources]

## OTHER CONSIDERATIONS:
[Mention any gotchas, specific requirements, or things AI assistants commonly miss]

With INITIAL.md written, run the generator inside Claude Code. The README states the command reads your request, researches the codebase, searches documentation, and writes the PRP into PRPs/, so the file you should see afterwards is PRPs/your-feature-name.md.

/generate-prp INITIAL.md

Then execute that generated file. The README's expected sequence is a plan, step-by-step execution with validation, a test run with fixes, and a check against the success criteria.

/execute-prp PRPs/your-feature-name.md

One caveat before you start: the examples/ folder ships with only a .gitkeep, so the "highly recommended" step of adding examples is work you have to do yourself. A PRP generated against an empty examples folder has less to imitate.

Where the template stops helping

The README is candid that the template does not cover RAG or tool-based context engineering, saying more is planned. If your assistant needs to retrieve from a vector store or call external tools as part of the loop, this repository does not demonstrate that, and the PRP workflow will not fill the gap.

The second limitation is the empty examples folder. The README calls examples critical and highly recommended, but the repository ships a .gitkeep rather than a set of worked patterns. The quality of a generated PRP therefore depends on context you supply, not on context the template supplies. Teams expecting a turnkey setup will find the first hour is spent copying their own code into examples/.

The third is the Claude Code dependency. The two entry points are slash commands defined in .claude/commands/, and the README's own instruction for adapting to other assistants is to apply the strategy, not to reuse the commands. If your team is standardised on a different assistant, you are porting Markdown prompts by hand.

Finally, there is no release history to lean on. The repository has no releases, so there is no versioned artifact to pin and no changelog describing behaviour changes between revisions. The last push was on 2026-03-16.

How this differs from plain prompt engineering and from spec-driven tools

The README draws the contrast itself: prompt engineering is described as clever wording limited to how you phrase a task, like a sticky note, while context engineering is a complete system covering documentation, examples, rules, patterns and validation, like a full screenplay. The practical difference is where the work sits. With prompt engineering you iterate on one string. Here you maintain a small set of files, and the assistant's behaviour changes when those files change.

Compared with spec-driven development tools that turn a written specification into tasks and code, the PRP is doing something narrower and more assistant-specific. A PRD is written to align humans; the README says a PRP is crafted specifically to instruct an AI coding assistant, which is why it carries validation gates, error handling patterns and test requirements inside the same document. That makes the artifact less useful as a stakeholder document and more useful as an execution plan.

The honest comparison is not against another repository but against doing nothing structured. If your current practice is pasting a paragraph into a chat window and reviewing whatever comes back, the PRP loop adds a research step and a written blueprint before any code appears. That is extra work per feature, and it only pays off on features large enough that a wrong first attempt costs more than the blueprint.

Licence, maintenance and the cost of upgrading a template

The repository is MIT licensed, which permits use, modification and redistribution provided the licence and copyright notice are preserved. Since you clone the template into your own project rather than depending on a published package, the practical implication is that your copy becomes yours to maintain. There is no dependency to bump and no transitive supply chain to audit, but there is also no upstream fix that arrives automatically.

Upgrade cost is therefore a diff problem. If the upstream commands or PRP base template change, you compare .claude/commands/generate-prp.md, .claude/commands/execute-prp.md and PRPs/templates/prp_base.md against your edited versions and merge by hand. Any local customisation you made to those files, or to CLAUDE.md, is the part that will conflict.

On maintenance status: the last push to the default branch was on 2026-03-16, and the repository is not archived. The README does not document a rollback path for a PRP that produced bad code, which is worth knowing before you let the execute step run unattended against a branch you care about. Nothing here is legal advice; the MIT text in LICENSE is the authority.

Editorial conclusion

Adopt it if you already work inside Claude Code and want a repeatable two-command loop for feature-sized tasks: clone the template, put real code patterns in examples/, and run /generate-prp INITIAL.md followed by /execute-prp PRPs/<name>.md. Do not adopt it if you need a library to import, a non-Claude-Code workflow, or a RAG pipeline, since the README states RAG and tooling are explicitly out of scope for now and the repository contains no release artifacts. Before relying on it, verify two things yourself: that the slash commands in .claude/commands/ match your Claude Code version's command format, and that the PRP base template in PRPs/templates/prp_base.md produces plans that fit your codebase, because the repository's own example PRP is a sample rather than a specification.

Frequently asked questions

Official sources

1. coleam00/context-engineering-intro on GitHub
2. Issues
3. License: MIT
4. README