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 greedychipmunk/agent-skills --skill agent-developmentgit clone --depth 1 https://github.com/greedychipmunk/agent-skillsWrote 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/greedychipmunk/agent-skills/agent-development)<a href="https://agentmods.dev/skills/greedychipmunk/agent-skills/agent-development"><img src="https://agentmods.dev/badge/skills/greedychipmunk/agent-skills/agent-development/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/greedychipmunk/agent-skills/agent-development"><img src="https://agentmods.dev/badge/skills/greedychipmunk/agent-skills/agent-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.01694 |
| Opus 5.5 | $0.00020 | $0.00678 |
| Sonnet 5.5 | $0.00010 | $0.00339 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
agent-development 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Development
Design and build effective AI agents with appropriate architectures, memory configurations, model selection, and tool setups. Works across any agent framework or custom implementation.
When to Use
- Starting a new agent project
- Choosing between agent architectures (single-agent, multi-agent, stateless, stateful)
- Designing memory structure and context management
- Selecting appropriate models for your use case
- Planning tool configurations
- Optimizing memory management and performance
- Implementing shared memory between agents
- Debugging memory-related issues
Architecture Selection
| Architecture | When to use |
|---|---|
| Single agent, stateful | Most common case. Agent maintains context across turns. Best for personal assistants, coding agents, support bots. |
| Single agent, stateless | Simple request/response patterns. No conversation memory needed. Good for one-shot tools. |
| Multi-agent, shared memory | Complex workflows where different agents specialize. Coordinate via shared memory blocks or message passing. |
| Multi-agent, orchestrated | Pipeline or fan-out patterns. A router agent dispatches to specialist agents. |
Read resources/architectures.md for detailed comparison and tradeoffs.
Memory Architecture
Three memory types cover most agent needs:
Core Memory (in-context):
- Always accessible in the agent's context window
- Use for: current state, active context, frequently referenced information
- Limit: Keep total core memory under 80% of context window
Archival Memory (out-of-context):
- Semantic search over vector database or document store
- Use for: historical records, large knowledge bases, past interactions
- Access: Agent must explicitly search — not automatically populated from context overflow
Conversation History:
- Past messages from current conversation
- Use for: referencing earlier discussion, tracking conversation flow
- Older messages may be evicted; store durable facts in core/archival memory
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 206 lines · 51 tokens per session scan A ab688b62f427
agent-development is a skill published in the GitHub repository greedychipmunk/agent-skills (24 stars, last pushed 5d ago), licensed MIT. It adds 51 tokens to every session and 1,694 once invoked, about $0.0002 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 2026-08-30.
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