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 skills add error505/Flockion_AI_Engineering --skill engineering-aigit clone --depth 1 https://github.com/error505/Flockion_AI_EngineeringWrote 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/skills/error505/flockion_ai_engineering/engineering-ai)<a href="https://agentmods.dev/skills/error505/flockion_ai_engineering/engineering-ai"><img src="https://agentmods.dev/badge/skills/error505/flockion_ai_engineering/engineering-ai/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/skills/error505/flockion_ai_engineering/engineering-ai"><img src="https://agentmods.dev/badge/skills/error505/flockion_ai_engineering/engineering-ai.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 | $0.00187 | $0.01778 |
| Opus 5.5 | $0.00075 | $0.00711 |
| Sonnet 5.5 | $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 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.
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.
- 1mo 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 yesterday), licensed MIT. It adds 187 tokens to every session and 1,778 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
genai-hub-orchestration
Configure and use the SAP AI Core Orchestration service: build prompt templates, add content filtering, set up document grounding (RAG) pipelines, and call models via the Harmonized API. WHEN: user asks about the orchestration service, Harmonized API, prompt templating with variables, content filtering, grounding, RAG…
ai-llm-skills-guide
Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
ai-engineering
Reviews and guides LLM/AI application engineering — prompts, caching, RAG, agent loops and tools, resilience, model migration, evals, prompt-injection defence, and observability. Invoke with /ai-engineering for "review my prompt", "set up RAG", or "write evals".
genkit-go-flows
Guides building AI features with Genkit for Go - flows, prompts, structured generation, tool calling, and middleware. Use when defining a Genkit flow, writing a prompt, calling genkit.Generate/GenerateData, defining a tool, debugging Genkit output with traces, or asked how Genkit differs from Google's Agent…
ai-teacher
A guide to teaching about artificial intelligence, including how large language models work, prompt writing, AI agents, tools, and AI ethics.