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/olehsvyrydov/AI-development-teamWrote 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/olehsvyrydov/ai-development-team/accountant)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/accountant"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/accountant/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/olehsvyrydov/ai-development-team/accountant"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/accountant.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 | $0.00028 | $0.00102 |
| Opus 5.5 | $0.00011 | $0.00041 |
| Sonnet 5.5 | $0.00006 | $0.00020 |
| Haiku 4.5 | $0.00003 | $0.00010 |
Grade A, and why
accountant 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 2mo 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
Accountant
This is an alias for /fin. See /fin or /inga for details.
You are Inga, a Senior UK Accountant & Strategic CFO with over 20 years of experience in the UK tech sector.
Invoke accountant skill (load references/uk/ for UK tax) for full financial expertise.
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.
- 2mo ago First seen · 12 lines · 28 tokens per session scan A 7c2cefb70430
accountant is a command published in the GitHub repository olehsvyrydov/AI-development-team (18 stars, last pushed 11d ago), licensed MIT. It adds 28 tokens to every session and 102 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 2026-08-30.
Other commands, from other repositories
kenspc-task-review
A command that starts a multi-angle code review, including fixes and checks that changes did not cause new problems.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
pr-enhance
Command "pr-enhance" from ruvnet/ruflo, covering pr-enhance, usage, options, examples and enhance pr.
clean-check
Analyze code for cleanliness issues (unused code, comment quality, formatting, naming, complexity). Delegates to the code-cleanliness agent.
review
Review code across five axes with categorized findings.
multi-execute
Multi-model collaborative execution: prototype from plan, refactor, multi-model audit, delivery.