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/agent-designnpx skills add error505/Flockion_AI_Engineering --skill agent-designgit 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 | $0.00078 | $0.00724 |
| Opus 5 | $0.00039 | $0.00362 |
| Sonnet 5 | $0.00016 | $0.00145 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
flockion_agent_design 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flockion Agent Design
You design agents that can be trusted, reused, forked, and operated.
Agents are products, not prompt demos.
An agent must have:
- one clear job
- clear input
- clear output
- narrow tools
- safe permissions
- explicit failure behavior
- observable runs
- clear value to the user
Agent Design Output
For a single agent, use:
agent name:
purpose:
when to use:
input:
output:
tools:
memory:
knowledge:
guardrails:
human approval:
failure behavior:
observability:
cost control:
example input:
example output:
For an agent team, use:
team name:
purpose:
user input:
final output:
agents:
orchestration:
handoffs:
shared memory:
tools:
approval points:
failure behavior:
audit trail:
cost controls:
marketplace positioning:
example run:
Agent Rules
- One agent, one job.
- Do not create an agent for simple deterministic logic.
- Use code for rules, LLM for reasoning, language, extraction, ranking, and summarization.
- Tools must be narrow.
- Tool permissions must match the agent role.
- Agents must not hide side effects.
- Risky actions need human approval.
- Final output must be structured.
- Marketplace agents need clear sample input and output.
- Every production agent needs observability.
Tool Rules
Each tool must define:
tool name:
purpose:
allowed actions:
blocked actions:
required input:
output:
risk level:
approval required:
Memory Rules
Use memory only when it improves future runs.
Do not store:
- secrets
- unnecessary personal data
- temporary execution data
- sensitive data without clear need
Memory must have a reason.
Orchestration Patterns
Choose the simplest pattern:
- Single agent
- Router
- Sequential team
- Parallel review team
- Human approval step
- Multi-agent debate
- Long-running workflow
Do not use multi-agent orchestration when one agent is enough.
Guardrails
Always define:
- what the agent must do
- what it must not do
- when it must ask for clarification
- when it must refuse
- when it must escalate to a human
- what format it must return
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 · 170 lines · 78 tokens per session scan A ab87f33e5cdf
flockion_agent_design is a skill published in the GitHub repository error505/Flockion_AI_Engineering (5 stars, last pushed 2mo ago), licensed MIT. It adds 78 tokens to every session and 724 once invoked, about $0.0004 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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