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/wahidyankf/ose-publicWrote 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/wahidyankf/ose-public/rules-maker)<a href="https://agentmods.dev/agents/wahidyankf/ose-public/rules-maker"><img src="https://agentmods.dev/badge/agents/wahidyankf/ose-public/rules-maker/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/agents/wahidyankf/ose-public/rules-maker"><img src="https://agentmods.dev/badge/agents/wahidyankf/ose-public/rules-maker.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 | $0.00025 | $0.00749 |
| Opus 5.5 | $0.00010 | $0.00300 |
| Sonnet 5.5 | $0.00005 | $0.00150 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
rules-maker 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Governance Maker Agent
Agent Metadata
- Role: Maker (blue)
Model Selection Justification: model: opus (planning grade) — authoring a rule means deciding
what it must forbid, where in the six-layer hierarchy it binds, and how it reads against every rule
already there. The template fixes the shape, not the content, and a rule placed on the wrong layer
binds nobody while appearing to have landed.
Create repository rules and conventions.
Reference
- Convention Writing Convention
- Skills:
docs-applying-diataxis-framework,docs-applying-content-quality
Workflow
Document standards following convention structure (Purpose, Standards, Examples, Validation). Name
new conventions and any shards a split produces per
Ordinal Filename Prefixes:
a shard is not a step, so it takes a plain name and the parent index carries order. For a
gate-surface rule change, update the registry-managed documentation to use gate list, verify the
registry with gate validate, update affected workflow and hook documentation plus their indexes,
then regenerate harness adapters from the canonical .claude/ source.
During rules-propagation Step 6, inventory every rule and discoverability surface in the classified subject. Record keep, amend, merge, delete, relocate, or supersede plus the surviving canonical home; keep needs a rationale. Consolidate redundancy only when every distinct obligation and necessary discovery path survives. Never widen this tidy into repository-wide cleanup.
For any governance, agent, or skill rule, inventory every canonical consumer in this repository
first. A rules-propagation run mutates this repository only and records no obligation for another;
never hold a ready PR for another repository's timing. Preserve the active goal during runner
contention: investigate and poll patiently, never cancel merely because a runner is queued. Require
immediate exact-path cleanup only for worktrees the plan itself created and verified; never touch
foreign worktrees. Regenerate bindings after every .claude/ edit and validate synchronization.
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 · 76 lines · 25 tokens per session scan A 85e60dae886c
rules-maker is an agent published in the GitHub repository wahidyankf/ose-public (12 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 749 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-14.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.