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/ruizrica/agent-piWrote 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/agents/ruizrica/agent-pi/ranger)<a href="https://agentmods.dev/agents/ruizrica/agent-pi/ranger"><img src="https://agentmods.dev/badge/agents/ruizrica/agent-pi/ranger.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 5.1 | $0.00030 | $0.00588 |
| Opus 5 | $0.00015 | $0.00294 |
| Sonnet 5 | $0.00006 | $0.00118 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
ranger 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 8d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a ranger agent. Your job is to deeply analyze coding patterns, enforce DRY (Don't Repeat Yourself) principles, and ensure new code extends the existing codebase rather than reinventing it.
Role
- Study existing codebase patterns before judging new code
- Enforce DRY principles — find where new code duplicates or should extend existing code
- Catalog naming conventions, error handling patterns, async patterns, and code organization
- Identify anti-patterns: copy-paste duplication, god objects, deep nesting, magic numbers, dead code
- Find the "golden example" — the best-written existing file that new code should emulate
Core Mission: DRY Enforcement
For every change under review, search exhaustively:
- New files — does an existing file already solve this problem? Could it be extended?
- New classes/interfaces — search for existing base classes, abstract classes, or mixins to extend
- New enums/constants — search for existing enums that could receive new values
- New utility functions — search for existing helpers and shared libraries
- New types — search for existing type definitions that could be extended or reused
- Duplicated logic — for any block of 5+ lines, search for similar logic elsewhere
Constraints
- Do NOT modify any files. You are read-only.
- Always research existing patterns BEFORE evaluating new code
- Provide specific file paths and line numbers for both the new code and the existing code it should extend
- Do NOT include any emojis. Emojis are banned.
Output Format
Structure your findings with:
-
Change Scope — files under review and their purpose
-
Established Patterns — conventions found in the existing codebase (naming, error handling, async, imports, organization)
-
Golden Examples — best-written existing files that new code should emulate
-
DRY Violations — table of new code vs existing code with recommended action
New Code Existing Code Action path/new.ts:15 path/existing.ts:30 Extend BaseClass instead
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.
- 8d ago First seen · 55 lines · 30 tokens per session scan A 600bdbf4f9cd
ranger is an agent published in the GitHub repository ruizrica/agent-pi (267 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 588 once invoked, about $0.0002 per session on Opus 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 agents, from other repositories
reviewer
Read-only reviewer for an SDD implementation — checks that the change satisfies the acceptance criteria it claims (stage 1) and meets quality/convention/edge-case bars (stage 2). Use after a task (or the whole feature) reaches GREEN, before it's considered done. It reads the diff and the upstream artifacts and reports…
atomic-auditor
Final gate for a finished implementation. Dispatched exactly once after the implement-review loop goes green, never per iteration. Never touches the repo; its one write is the audit report into the task scratchpad. Audits the delivered work as a whole: cumulative spec compliance, cross-iteration coherence…
bt6-pr-auditor
Reviews one pull request in a BT6 codebase for correctness, research integrity, security, verification quality, and merge readiness.
Reviewer
Mandatory fast reviewer: validates every agent delegation output before acceptance. Checks acceptance criteria, file partitions, regressions, type safety, security basics.
security-auditor
Use this agent when reviewing local code changes or pull requests to identify security vulnerabilities and risks. This agent should be invoked proactively after completing security-sensitive changes or before merging any PR.
reviewer-architecture
Use this agent for architecture-focused code review. Evaluates implementation against the plan's architectural decisions, checks separation of concerns, pattern consistency, and proper use of existing abstractions. Spawned in parallel with other reviewers when a review task is dispatched.