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/data)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/data"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/data/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/data"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/data.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 | $0.00100 |
| Opus 5.5 | $0.00010 | $0.00040 |
| Sonnet 5.5 | $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 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
/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.
- 1mo 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 (17 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 100 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
pgvector-search
Production hybrid search with PGVector and BM25 using Reciprocal Rank Fusion, metadata filtering, and performance tuning for semantic retrieval. Use when building hybrid semantic and keyword search, tuning PGVector performance, or filtering by metadata. Triggers on pgvector, hybrid search, BM25, reciprocal rank…
semantic-caching
Redis semantic caching for LLM applications. Use when caching LLM responses by vector similarity, cutting cost on repeated queries, or building a multi-level cache. Triggers on semantic cache, Redis cache, vector similarity cache, LLM cache, response caching, cache warming, TTL cache.
optimize-pipeline
Optimize an existing pipeline on demand — survey topology, model-tier cost, and past-run signals, lock an optimization plan with the user, then batch-apply it atomically with a mandatory post-apply audit.
change-models
Set, change, or audit model preferences for pipeline agents — interactive 6-mode workflow (global/workspace prefs, per-agent overrides, first-run wizard, catalog refresh).
multi-execute
Multi-model collaborative execution: prototype from plan, refactor, multi-model audit, delivery.
multi-workflow
Multi-model collaborative dev workflow with intelligent routing (frontend, backend).