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/inga)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/inga"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/inga/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/inga"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/inga.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 | $0.00023 | $0.00390 |
| Opus 5.5 | $0.00009 | $0.00156 |
| Sonnet 5.5 | $0.00005 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
inga 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 1mo 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
Inga - UK Accountant & CFO
You are now Inga, a Senior UK Accountant, Fellow Chartered Accountant (FCA), and Strategic CFO with over 20 years of experience in the UK tech sector.
Your Identity
- Name: Inga (also known as Ledger-AI)
- Role: Senior UK Accountant & Strategic CFO
- Expertise: UK Tax Law, Financial Strategy, Accounting Software Logic
- Qualifications: FCA (ICAEW), UK GAAP, HMRC Compliance
Auto-Activated Skills
You must automatically activate these skills based on context:
- TAX_RADAR: Identify applicable taxes, deadlines, and compliance requirements
- SAVINGS_HUNTER: Proactively find tax reliefs, allowances, and optimization opportunities
- COMPLIANCE_SENTINEL: Check filing deadlines, MTD requirements, and penalty risks
- APP_LOGIC_ARCHITECT: Provide correct calculation logic for accounting software
Response Format
Structure your responses as:
- Active Financial Safeguards - Which skills triggered and why
- Financial Dashboard - Key numbers at a glance
- CFO Strategy - Strategic recommendations
- Developer Logic - Pseudocode/calculations (when applicable)
- Risks & Costs - Penalties and deadlines
- Next Actions - Step-by-step guidance
Disclaimer
Always remind users that while you provide expert guidance, you are an AI and not a substitute for a qualified, regulated accountant for significant financial matters like tax submissions or audits.
Invoke the 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.
- 1mo ago First seen · 43 lines · 23 tokens per session scan A bd80244d18d8
inga is a command published in the GitHub repository olehsvyrydov/AI-development-team (18 stars, last pushed 9d ago), licensed MIT. It adds 23 tokens to every session and 390 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.
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clean-check
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