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.
npx agentmods add commands/adrielp/ai-engineering-harness/init_harnessgit 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/init_harness)<a href="https://agentmods.dev/commands/adrielp/ai-engineering-harness/init_harness"><img src="https://agentmods.dev/badge/commands/adrielp/ai-engineering-harness/init_harness.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00332 |
| Opus 5 | $0.00000 | $0.00166 |
| Sonnet 5 | $0.00000 | $0.00066 |
| Haiku 4.5 | $0.00000 | $0.00033 |
Grade A, and why
init_harness 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 4d 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.
What it actually says
Initialize Harness
Initialize the AI Engineering Harness in this repository.
What This Command Does
- Runs the built-in
/initcommand to generateAGENTS.md - Creates the
thoughts/directory structure for context engineering - Adds a ticket template for consistent ticket creation
- Provides guidance on next steps
Instructions
Load and follow the init-harness skill for detailed instructions on how to:
- Check the current state of the repository
- Run
/initto generate AGENTS.md (the built-in codebase analysis command) - Create the thoughts/ directory structure
- Add the ticket template
- Optionally create a personal thoughts directory
- Present next steps to the user
Important: The /init command is OpenCode's built-in command that analyzes the codebase and generates AGENTS.md. This /init_harness command wraps that functionality and adds the context engineering setup.
Quick Reference
After running this command, the repository will have:
AGENTS.md # Codebase context (from /init)
thoughts/
├── shared/
│ ├── tickets/ # Feature requests, bugs, tasks
│ │ └── ticket-template.md # Template for new tickets
│ ├── plans/ # Implementation plans
│ └── research/ # Research documents
└── global/ # Cross-repository concerns
Workflow After Initialization
Ticket → /create_plan → /implement_plan → /validate_plan → /commit
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.
- 4d ago First seen · 45 lines · 0 tokens per session scan A 5f736acfc549
init_harness is a command published in the GitHub repository adrielp/ai-engineering-harness (20 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 332 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
git
Git operations with intelligent commit messages and workflow optimization.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.