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 commands/olehsvyrydov/ai-development-team/allgit 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/all)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/all"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/all.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 | $0.00025 | $0.00665 |
| Opus 5 | $0.00013 | $0.00332 |
| Sonnet 5 | $0.00005 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
all 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 5d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/all — Multi-LLM Consultation
You are orchestrating a multi-LLM consultation. Your job is to gather opinions from multiple AI platforms and synthesize them into actionable insights.
Workflow
-
Understand the request: Interpret what the user wants opinions on.
-
Check readiness: Call
check_config()to verify API keys are configured. -
Show available models: Call
list_models()to show the model catalog. Present a concise table and let the user pick which models to consult (defaults: GPT-5-2, Gemini 3.1 Pro, Grok 4). -
Formulate the prompt: Write a clear, detailed prompt that gives each model enough context to provide a useful answer. Include relevant code, architecture details, or constraints.
-
Consult models: Call
consult_model()for each selected model in parallel using the Task tool. Pass an appropriate expertrolefor each:- GPT-5-2: "senior security architect" or "principal engineer"
- Gemini 3.1 Pro: "performance engineer" or "systems architect"
- Grok 4: "senior technical advisor" or "alternative perspective analyst"
-
Synthesize: After all responses arrive, create a consolidated report:
Synthesis Format
## Multi-LLM Consultation Report
**Question**: [What was asked]
**Models consulted**: [List]
**Total cost**: $X.XX
### Consensus Points
- [Things all models agree on — HIGH confidence]
### Divergent Views
| Topic | GPT-5-2 | Gemini 3.1 Pro | Grok 4 | Claude's Take |
|-------|---------|----------------|--------|---------------|
| [topic] | [view] | [view] | [view] | [your opinion] |
### Unique Insights
- **GPT-5-2**: [Anything only this model caught]
- **Gemini 3.1 Pro**: [Anything only this model caught]
- **Grok 4**: [Anything only this model caught]
### Recommendation
[Your synthesized recommendation combining all perspectives + your own expertise]
- Discuss: After presenting the report, remain available for follow-up questions. The user can drill into any model's response or ask for clarification.
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.
- 5d ago First seen · 70 lines · 25 tokens per session scan A 7bce62856ea7
all is a command published in the GitHub repository olehsvyrydov/AI-development-team (16 stars, last pushed 26d ago), licensed MIT. It adds 25 tokens to every session and 665 once invoked, about $0.0001 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.
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