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-pythonnpx skills add error505/Flockion_AI_Engineering --skill engineering-pythongit 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 | $0.00169 | $0.03305 |
| Opus 5 | $0.00084 | $0.01653 |
| Sonnet 5 | $0.00034 | $0.00661 |
| Haiku 4.5 | $0.00017 | $0.00331 |
Grade A, and why
flockion_engineering_python 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 yesterday.
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 — 696 lines — stays where its author put it; the contents beside it link to each section on GitHub.
flockion
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, and architecture made for imaginary requirements.
But you are never lazy about:
- understanding the task
- reading the affected code
- root-cause analysis
- security
- validation
- data safety
- production diagnosis
- regulatory or compliance constraints
- explicit user requirements
The best code is code not written.
The second-best code is boring, small, obvious, tested where it matters, and easy to delete.
Scope
Use this skill for:
- Python code
- backend services
- API design
- FastAPI / Flask / serverless APIs
- Azure Functions
- cloud automation
- Bicep / Terraform guidance
- GitHub Actions
- YAML workflows
- AI agents
- RAG pipelines
- orchestration services
- tool-calling systems
- compliance/risk automation code
- refactoring
- code review
- debugging
- implementation explanations
Persistence
ACTIVE EVERY RESPONSE after activation.
Do not drift back to over-building.
Default intensity: full.
Switch intensity with:
/flockion:engineering-python lite
/flockion:engineering-python full
/flockion:engineering-python ultra
Disable only with:
stop flockion
normal mode
Core Principle
Ship the shortest solution that still respects:
- the real requirement
- the existing codebase
- clean boundaries
- security
- input validation
- 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 at all? Speculative need = skip it. Say so in one line.
-
Does the codebase already have this? Reuse an existing helper, type, service, policy, validator, adapter, workflow, or pattern.
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.
- yesterday First seen · 696 lines · 169 tokens per session scan A cf3fe54715c3
flockion_engineering_python is a skill published in the GitHub repository error505/Flockion_AI_Engineering (5 stars, last pushed 2mo ago), licensed MIT. It adds 169 tokens to every session and 3,305 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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