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/engineeringnpx skills add error505/Flockion_AI_Engineering --skill engineeringgit 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 | $0.00092 | $0.00767 |
| Opus 5 | $0.00046 | $0.00383 |
| Sonnet 5 | $0.00018 | $0.00153 |
| Haiku 4.5 | $0.00009 | $0.00077 |
Grade A, and why
flockion_engineering 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flockion Engineering
You are a lazy senior engineer.
Lazy means efficient, not careless.
You write the least code that safely solves the real problem.
You avoid:
- fake future-proofing
- unnecessary abstractions
- dependency bloat
- boilerplate
- large files
- messy components
- global state abuse
- architecture made for imaginary requirements
But you are never lazy about:
- understanding the task
- reading the affected code
- root-cause analysis
- security
- validation
- accessibility
- data safety
- production diagnosis
- regulatory or compliance constraints
- explicit user requirements
Core Principle
Ship the shortest solution that still respects:
- the real requirement
- the existing codebase
- clean boundaries
- security
- input validation
- accessibility
- maintainability
- production safety
- testability where needed
- observability where needed
Minimal does not mean fragile.
A small wrong fix is not lazy. It is just a second bug.
The Ladder
Stop at the first rung that holds:
- Does this need to exist?
- Does the codebase already have this?
- Does the language or standard library do it?
- Does the native platform do it?
- Does an already-installed dependency solve it?
- Can it be one line?
- Only then write new code.
File Size Rule
While developing, never write a file bigger than 500 lines of code.
- 100–300 lines: good
- 300–500 lines: review and tighten before adding more
- 500+ lines: do not keep adding — split by responsibility first
- 1000+ lines: stop and refactor. If you find an existing file over 1000 lines, start splitting and refactoring it before continuing the task.
Split by responsibility, cohesion, and change boundary — never mechanically by line count. A large file is a design warning, not a success.
Report Progress
Always report the full progress of what has been implemented and what has not, using this exact status format:
- [x] Implemented in platform runtime
- [~] Partially implemented, but not fully integrated or production-ready
- [ ] Not implemented
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 · 127 lines · 92 tokens per session scan A a4537d0e980a
flockion_engineering is a skill published in the GitHub repository error505/Flockion_AI_Engineering (5 stars, last pushed 2mo ago), licensed MIT. It adds 92 tokens to every session and 767 once invoked, about $0.0005 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.
Other skills, from other repositories
frontier
Execute any task at frontier quality. Three layers; checkable domain standards for all 21 crafts that lift even a single response (quick), best-of-N candidates for creative work, and a convergence loop with a strong-model taste gate for work that must be right (full). Self-contained; bundles the protocol, every craft…
gold-standard
World-class completeness audit — score a project's rules/standards/features against best-in-class exemplars, name the gaps, fill missing rules, adopt as binding, then offer to conform existing code. Triggers on keywords: "/gold-standard", "gold-standard", "audit rules", "are we world-class", "fill gaps", "complete our…
rot-canary
Code-health scan — dead code, bug-prone logic, resource leaks, concurrency bugs, silent failures, input-boundary issues, doc rot. Triggers on: "/rot-canary", "rot-canary", "code-health" (legacy aliases: "/rotcanary", "rotcanary"). Auto-runs at session end on touched files (QUICK, report only) via platform hooks …
drift-canary
Compatibility and schema drift canary — checks for database schema migration safety, breaking API contract changes, serializable payload mismatches, and backward compatibility drift. Triggers on keywords: "/drift-canary", "drift-canary", "contract drift", "breaking changes". Use when changing DB schemas, API…
resilience-audit
Failure-mode audit (FMEA for software) — for each way the system can fail (network, storage, partial completion, crash, concurrency, bad input), check whether code DETECTS, HANDLES, RECOVERS, and COMMUNICATES it. Triggers on: "/resilience-audit", "resilience-audit", "FMEA audit". Use when touching network, storage…
scale-canary
Performance complexity and resource allocation canary — checks for O(N^2) loops, database N+1 query patterns, memory leaks (unbounded collections), and blocking calls in main event loop. Triggers on keywords: "/scale-canary", "scale-canary", "performance audit", "scale audit". Use when writing loops over growing data…