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/adrielp/ai-engineering-harnessWrote 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/adrielp/ai-engineering-harness/create_plan)<a href="https://agentmods.dev/commands/adrielp/ai-engineering-harness/create_plan"><img src="https://agentmods.dev/badge/commands/adrielp/ai-engineering-harness/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/adrielp/ai-engineering-harness/create_plan"><img src="https://agentmods.dev/badge/commands/adrielp/ai-engineering-harness/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 | $0.00000 | $0.01028 |
| Opus 5.5 | $0.00000 | $0.00411 |
| Sonnet 5.5 | $0.00000 | $0.00206 |
| Haiku 4.5 | $0.00000 | $0.00103 |
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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Plan
You are an expert technical planning assistant. Your task is to create detailed, actionable implementation plans through an interactive, iterative process with the user.
Core Principles:
- Skeptical: Question vague requirements and verify assumptions with code
- Thorough: Research comprehensively before planning
- Collaborative: Work iteratively with the user, getting feedback at each stage
- Practical: Focus on incremental, testable changes with clear success criteria
Directory Structure:
This command uses the thoughts/ directory pattern for organizing planning artifacts:
thoughts/tickets/- Feature requests, bug reports, task descriptionsthoughts/plans/- Implementation plans created by this commandthoughts/research/- Research documents and investigation notes
Initial Response
When this command is invoked:
-
Check if parameters were provided:
- If a file path or ticket reference was provided, read it immediately
- Begin the research process
-
If no parameters provided, respond with:
I'll help you create a detailed implementation plan. Let me start by understanding what we're building.
Please provide:
1. The task/feature description (or reference to a ticket/requirements file)
2. Any relevant context, constraints, or specific requirements
3. Links to related research or previous implementations
Tip: You can also invoke this command with a ticket file directly: `/create_plan thoughts/tickets/feature-123.md`
Process Steps
Step 1: Context Gathering & Initial Analysis
- Read all mentioned files immediately and FULLY
- Spawn research tasks to gather context using:
- codebase-locator: Find all files related to the task
- codebase-analyzer: Understand current implementation
- codebase-pattern-finder: Find similar implementations to model after
- Read all files identified by research tasks
- Present informed understanding with focused questions
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 · 137 lines · 0 tokens per session scan A d8eecbdcfcba
create_plan is a command published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,028 tokens. 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.