Borrowing it
Nothing to install: this file belongs to grandinh/mcp-prompt-optimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/grandinh/mcp-prompt-optimizer/main/.claude/commands/ori.mdgit clone --depth 1 https://github.com/grandinh/mcp-prompt-optimizerWrote 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/grandinh/mcp-prompt-optimizer/ori)<a href="https://agentmods.dev/commands/grandinh/mcp-prompt-optimizer/ori"><img src="https://agentmods.dev/badge/commands/grandinh/mcp-prompt-optimizer/ori/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/grandinh/mcp-prompt-optimizer/ori"><img src="https://agentmods.dev/badge/commands/grandinh/mcp-prompt-optimizer/ori.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 | $0.04457 |
| Opus 5 | $0.00000 | $0.02228 |
| Sonnet 5 | $0.00000 | $0.00891 |
| Haiku 4.5 | $0.00000 | $0.00446 |
Grade A, and why
ori 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 10d 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 — 627 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Research Implement (ORI) Workflow
Version: 1.1 Purpose: Autonomous multi-phase workflow with intelligent model selection for researching, validating, and implementing changes with minimal user input.
Workflow Overview
Execute the following phases sequentially with built-in error handling, validation, and intelligent model selection:
PHASE 0: STRATEGY (Opus) → PHASE 1: RESEARCH (Dynamic) → PHASE 2: VERIFY (Sonnet) → PHASE 3: IMPLEMENT (Sonnet/Haiku) → PHASE 4: DOCUMENT (Haiku)
Model Selection Strategy
Per-Phase Model Recommendations
The workflow uses different models optimized for each phase:
| Phase | Recommended Model | Rationale |
|---|---|---|
| Phase 0: Strategy | Opus | Complex reasoning, strategic planning, research design |
| Phase 1: Research | Dynamic | Opus decides based on complexity; Sonnet for standard, Opus for complex |
| Phase 2: Verify | Sonnet | Balance of speed and accuracy for validation |
| Phase 3: Implement | Sonnet/Haiku | Sonnet for complex code, Haiku for simple edits |
| Phase 4: Document | Haiku | Fast, cost-effective for doc updates |
When to Use Each Model
Opus (claude-opus-4):
- Strategic planning and research design
- Complex multi-step reasoning
- Novel or ambiguous problems
- High-stakes decisions requiring deep analysis
- Architectural decisions
Sonnet (claude-sonnet-4-5):
- Most implementation tasks
- Code generation and refactoring
- Validation and verification
- Balanced performance/cost for general tasks
Haiku (claude-haiku-4):
- Simple file edits
- Documentation updates
- Formatting and style fixes
- Quick, straightforward tasks
Phase 0: Research Strategy (STRATEGIC PLANNING)
Objective
Use Opus to create an optimal research strategy and select the best model for execution.
Model: Opus (claude-opus-4)
Instructions
IMPORTANT: This phase MUST use Opus via the Task tool:
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.
- 10d ago First seen · 627 lines · 0 tokens per session scan A 05ad13386713
ori is a command published in the GitHub repository grandinh/mcp-prompt-optimizer (0 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,457 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 2026-08-31.
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.