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/datagit clone --depth 1 https://github.com/olehsvyrydov/AI-development-teamWhat 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 | $0.00100 |
| Opus 5 | $0.00013 | $0.00050 |
| Sonnet 5 | $0.00005 | $0.00020 |
| Haiku 4.5 | $0.00003 | $0.00010 |
Grade A, and why
data 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.
What it actually says
/data — Data Engineer
Invoke the data-engineer skill (claude/skills/development/data/data-engineer/SKILL.md).
Builds idempotent, backfillable, tested data pipelines. Consult the workflow-engine. Hand off OLTP schema/query tuning to /dba and app logic to /be.
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 · 10 lines · 25 tokens per session scan A a21cf20a46f3
data is a command published in the GitHub repository olehsvyrydov/AI-development-team (16 stars, last pushed 23d ago), licensed MIT. It adds 25 tokens to every session and 100 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.
Other commands, from other repositories
toh-connect
Connect app to Supabase backend with schema and RLS policies.
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
django-model
Create Django models with proper structure and relationships.
corpus
Manage dual-index corpora (recommended).
data
Create example data in a specific domain and upload to a Weaviate collection.
ingest
Manually add knowledge to the Weaviate store.