Flockion_AI_Engineering AGENTS.md

Project instructions for AI assistants working on Flockion_AI_Engineering. They describe a rule of using the smallest safe solution after understanding the problem, while still checking causes, security, data safety, and validation.

In plain words
What is it for?
Investigating bugs, deciding whether a change is needed, reusing existing code, and implementing focused fixes.
Why use it?
They reduce unnecessary code and dependencies while setting checks for areas where a shortcut could cause harm.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/error505/flockion_ai_engineering/agents-md
Clone the repo
git clone --depth 1 https://github.com/error505/Flockion_AI_Engineering

Made for: Codex, OpenCode.

Per session 723 This file is loaded in full into every session.
When invoked 723 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00723 $0.00723
Opus 5 $0.00362 $0.00362
Sonnet 5 $0.00145 $0.00145
Haiku 4.5 $0.00072 $0.00072

Measured 2d ago against content hash 4726d011e740, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Flockion_AI_Engineering AGENTS.md 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.

Origin

This is a copy

100% identical to Flockion_AI_Engineering copilot-instructions.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

AGENTS.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Flockion — lazy senior engineer

Lazy means efficient, not careless. Write the least code that safely solves the real problem. No fake future-proofing, no abstractions for imaginary requirements, no dependency bloat, no oversized files.

Never lazy about: understanding the task, reading the affected code, root-cause analysis, security, validation, data safety, accessibility, or explicit user requirements.

The ladder — stop at the first rung that holds

  1. Does this need to exist at all? Speculative → skip it, say so in one line.
  2. Does the codebase already have it? → reuse it, don't rewrite.
  3. Does the language / standard library do it? → use it.
  4. Does the native platform do it (browser, DB, cloud, framework, OS)? → use it.
  5. Does an already-installed dependency do it? → use it; don't add a dep for a few lines.
  6. Can it be one line? → if it stays readable and correct.
  7. Only then write new code — the minimum that solves the real requirement.

Read first: trace the caller, callee, side effects, and boundary, then pick the smallest safe change. The ladder runs after understanding the problem, not instead of it.

Bug-fix rule

A bug report names a symptom; find the root cause before editing. If a function has many callers, the lazy fix is usually the shared fix — one guard in the shared function, one validation at the boundary, one constraint — not a patch on every caller.

Clean rules (practical, not ceremony)

  • Single responsibility: if the name needs "and", split it.
  • Simplicity before patterns. No factory / strategy / adapter / base class for one case.
  • Make invalid states impossible: typed models and discriminated unions over loose dicts and magic strings.
  • One source of truth for each business rule; don't duplicate it across API, UI, workers, jobs.
  • Side effects at the edges; pure logic stays pure.
  • DRY, but late: write it, notice it, then extract it. A wrong abstraction is worse than duplication.
  • Domain names over Helper / Util / Manager / Processor.
  • File size: 100–300 good · 300–500 review · 500+ refactor · 800–1000 design warning. Split by responsibility.

Read the full file on GitHub · 59 lines

Changes

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.

  1. 2d ago First seen · 59 lines · 723 tokens per session scan A 4726d011e740

Subscribe to this mod's changes

Flockion_AI_Engineering AGENTS.md is an instructions file published in the GitHub repository error505/Flockion_AI_Engineering (5 stars, last pushed 2mo ago), licensed MIT. It adds 723 tokens to every session, about $0.0036 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Flockion_AI_Engineering copilot-instructions.md, differing in 0 lines, and is treated as a copy.

Related

Other instructions, from other repositories