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 agents/orangeproai/orangepro-mcp/codexgit clone --depth 1 https://github.com/OrangeproAI/orangepro-mcpWhat 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 | $0.00461 |
| Opus 5 | $0.00000 | $0.00230 |
| Sonnet 5 | $0.00000 | $0.00092 |
| Haiku 4.5 | $0.00000 | $0.00046 |
Grade A, and why
codex 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 2d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OrangePro With Codex
Install
npm install -g @orangepro/orangepro-mcp
cd /path/to/your/repo
opro
opro writes .orangepro/behavior-coverage.html and .orangepro/rtm.md. If a model key is available in the environment, it also applies weak AI grounding and AI-suggested candidate flows automatically. Proven coverage still comes only from dynamic targeted proof.
Codex Plugin
The public, supported path today is MCP setup through opro agent --client codex below. codex plugin add orangepro is only valid after OrangePro is listed in a Codex marketplace configured for your Codex install.
The bundled plugin metadata in this repo is for marketplace submission and maintainer validation. It starts the same orangepro-local MCP server and does not pin an LLM model; OrangePro uses the provider/model configured in your environment, .env.provider.local, .env.local, .env, or opro setup.
MCP Setup
Print a Codex-ready config block:
opro agent --client codex
Add the printed mcpServers.orangepro-local block to the MCP settings your Codex environment uses. The printed config intentionally omits model settings. If Codex does not inherit your shell environment, put OPENAI_API_KEY, ANTHROPIC_API_KEY, or OLLAMA_BASE_URL in the local client config, or keep them in a repo-local .env.provider.local file that is not committed.
If your Codex build supports plugin/shortcut commands, map $opro (or /opro) to: call orangepro_start for the current checkout, then follow the returned next_actions. The MCP tool is the source of truth; the shortcut is just a convenience wrapper.
Agent Prompt
Use OrangePro first. Call orangepro_start for this checkout, inspect the returned next_actions, then generate tests only through orangepro_generate_tests. Write only runnable tests with run_hints. After running them, call `orangepro_prove` with the returned `prove_run` args for Dynamically Proven. Use record_run only for static diagnostics.
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.
- 2d ago First seen · 36 lines · 0 tokens per session scan A 6b4090aad7ce
codex is an agent published in the GitHub repository OrangeproAI/orangepro-mcp (17 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 461 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 agents, from other repositories
ci-medic
Runs the ci-settle loop (formula convergence, step X3) — watches CI checks, triages bot comments, fixes real findings or declines with a stated reason, regenerates generated artifacts. No merge authority.
docs-auditor
Weekly standing patrol for docs drift vs code, broken links, index consistency, and i18n lag reporting (which locales are behind English and by how much). Patrols for progressive-disclosure violations doc-writer may have missed.
prs-expert
PromptScript language expert. Helps with syntax, compilation issues, and migrations.
debugger
Debugs errors, test failures, and unexpected behavior. Knows PromptScript architecture.
speckit.analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
speckit.implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.