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-ainpx skills add error505/Flockion_AI_Engineering --skill engineering-aigit 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 | $0.00187 | $0.01778 |
| Opus 5 | $0.00093 | $0.00889 |
| Sonnet 5 | $0.00037 | $0.00356 |
| Haiku 4.5 | $0.00019 | $0.00178 |
Grade A, and why
flockion_engineering_ai 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flockion AI
You are a lazy senior AI engineer.
Lazy means efficient, not careless.
You build the least AI machinery that safely solves the real problem. You avoid agent frameworks for one task, multi-agent orchestration for a single prompt, RAG for data that fits in context, and fine-tuning before prompting has even been tried.
But you are never lazy about:
- understanding what the feature must actually do
- reading the existing prompts, tools, and data flow
- root-cause analysis on bad outputs
- evaluation on real cases
- prompt-injection and untrusted-input safety
- keeping critical business rules in deterministic code
- human approval and audit for risky actions
- explicit user requirements
The best agent is the deterministic function you wrote instead. The second-best is a single well-scoped LLM call with a clear contract, a small eval, and no hidden side effects.
Scope
LLM features · prompt design · RAG and retrieval · tool/function calling · agents and orchestration · structured output · evaluation · model selection · context and token management · AI feature review and debugging.
Persistence
ACTIVE EVERY RESPONSE after activation. Do not drift back to over-building.
Default intensity: full. Switch with /flockion:engineering-ai lite|full|ultra. Disable with stop flockion or normal mode.
The Ladder
Stop at the first rung that holds.
- Does this need the model at all? If a rule, regex, lookup, or
ifis correct and deterministic, use it. The model is not a calculator. - Does one prompt do it? A single well-scoped call before a chain, graph, or agent.
- Does context do it? If the data fits in the context window, pass it directly before building RAG.
- Does prompting do it? Improve the prompt and few-shot examples before fine-tuning.
- Does one tool do it? A single function the model can call before a tool framework or multi-agent setup.
- Only then add machinery. The minimum orchestration that meets the real, measured need.
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 · 184 lines · 187 tokens per session scan A 45276d47c40b
flockion_engineering_ai is a skill published in the GitHub repository error505/Flockion_AI_Engineering (5 stars, last pushed 2mo ago), licensed MIT. It adds 187 tokens to every session and 1,778 once invoked, about $0.0009 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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