Context Engineering Template is a repository of instructions, examples, workflows, and validation practices that give AI coding assistants the information they need to complete software tasks. It is for developers working with Claude Code or other coding assistants, and the catalogue entries package parts of its workflow as commands, agents, instructions, and a skill.
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.
git clone --depth 1 https://github.com/coleam00/context-engineering-introWrote 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/commands/coleam00/context-engineering-intro/create-plan)<a href="https://agentmods.dev/commands/coleam00/context-engineering-intro/create-plan"><img src="https://agentmods.dev/badge/commands/coleam00/context-engineering-intro/create-plan/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/commands/coleam00/context-engineering-intro/create-plan"><img src="https://agentmods.dev/badge/commands/coleam00/context-engineering-intro/create-plan.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 | $0.00011 | $0.01375 |
| Opus 5.5 | $0.00004 | $0.00550 |
| Sonnet 5.5 | $0.00002 | $0.00275 |
| Haiku 4.5 | $0.00001 | $0.00137 |
Grade A, and why
create-plan 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Implementation Plan from Requirements
You are about to create a comprehensive implementation plan based on initial requirements. This involves extensive research, analysis, and planning to produce a detailed roadmap for execution.
Step 1: Read and Analyze Requirements
Read the requirements document from: $ARGUMENTS
Extract and understand:
- Core feature requests and objectives
- Technical requirements and constraints
- Expected outcomes and success criteria
- Integration points with existing systems
- Performance and scalability requirements
- Any specific technologies or frameworks mentioned
Step 2: Research Phase
2.1 Web Research (if applicable)
- Search for best practices for the requested features
- Look up documentation for any mentioned technologies
- Find similar implementations or case studies
- Research common patterns and architectures
- Investigate potential libraries or tools
2.2 Knowledge Base Search (if instructed)
If Archon RAG is available and relevant:
- Use
mcp__archon__rag_get_available_sources()to see available documentation - Search for relevant patterns:
mcp__archon__rag_search_knowledge_base(query="...") - Find code examples:
mcp__archon__rag_search_code_examples(query="...") - Focus on implementation patterns, best practices, and similar features
2.3 Codebase Analysis (for existing projects)
If this is for an existing codebase:
IMPORTANT: Use the codebase-analyst agent for deep pattern analysis
- Launch the codebase-analyst agent using the Task tool to perform comprehensive pattern discovery
- The agent will analyze: architecture patterns, coding conventions, testing approaches, and similar implementations
- Use the agent's findings to ensure your plan follows existing patterns and conventions
For quick searches you can also:
- Use Grep to find specific features or patterns
- Identify the project structure and conventions
- Locate relevant modules and components
- Understand existing architecture and design patterns
- Find integration points for new features
- Check for existing utilities or helpers to reuse
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 · 202 lines · 11 tokens per session scan A fe9c221c3b2a
create-plan is a command published in the GitHub repository coleam00/context-engineering-intro (13,880 stars, last pushed 7mo ago), licensed MIT. It adds 11 tokens to every session and 1,375 once invoked, about $0.0000 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.