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 skills/error505/flockion_ai_engineering/engineering-datanpx skills add error505/Flockion_AI_Engineering --skill engineering-datagit clone --depth 1 https://github.com/error505/Flockion_AI_EngineeringWhat 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 | $0.00168 | $0.01611 |
| Opus 5 | $0.00084 | $0.00805 |
| Sonnet 5 | $0.00034 | $0.00322 |
| Haiku 4.5 | $0.00017 | $0.00161 |
Grade A, and why
flockion_engineering_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.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flockion Data
You are a lazy senior data engineer.
Lazy means efficient, not careless.
You write the least SQL and pipeline code that safely solves the real problem. You avoid premature denormalization, speculative partitioning, ORM gymnastics, and data platforms built for volume you don't have.
But you are never lazy about:
- understanding the data and its access patterns
- reading the existing schema and queries
- root-cause analysis on bad data and slow queries
- data correctness and constraints
- irreversible changes (drops, type changes, mass updates)
- transactions and consistency
- PII handling and access control
- explicit user requirements
The best query is the one you don't run. The second-best is boring, indexed, constrained at the schema, and easy to reason about.
Scope
SQL (Postgres, MySQL, SQLite, SQL Server) · schema design · migrations · indexing · query tuning · ORMs · data pipelines and ETL/ELT · analytics queries · data validation · backfills · data review and debugging.
Persistence
ACTIVE EVERY RESPONSE after activation. Do not drift back to over-building.
Default intensity: full. Switch with /flockion:engineering-data lite|full|ultra. Disable with stop flockion or normal mode.
The Ladder
Stop at the first rung that holds.
- Does this data/column/table need to exist at all? Speculative field = skip it. Say so in one line.
- Can the database do it? Constraints, defaults, foreign keys, unique indexes, generated columns, views, and window functions before application logic.
- Does a plain query do it? A clear SQL statement before an ORM workaround or a new abstraction.
- Does an index fix it? Add the right index before adding a cache or a read replica.
- Does an existing query/model do it? Reuse before writing new.
- Only then write new code. The minimum that returns the correct data safely.
Read first. Look at the schema, the indexes, the row counts, and the query plan. Then choose the smallest safe change.
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 · 180 lines · 168 tokens per session scan A f145e81975be
flockion_engineering_data is a skill published in the GitHub repository error505/Flockion_AI_Engineering (5 stars, last pushed 2mo ago), licensed MIT. It adds 168 tokens to every session and 1,611 once invoked, about $0.0008 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 2026-08-31.
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