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 hajekim/agentic-design-patterns-extension --skill resource-awaregit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote 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/hajekim/agentic-design-patterns-extension/resource-aware)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/resource-aware"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/resource-aware.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00408 | $0.03557 |
| Opus 5 | $0.00204 | $0.01778 |
| Sonnet 5 | $0.00082 | $0.00711 |
| Haiku 4.5 | $0.00041 | $0.00356 |
Grade A, and why
resource-aware 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 7d 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.
This is a copy
100% identical to resource-aware — 3 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.
How it starts
The opening of the file, as written. The whole thing — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resource-Aware Optimization Pattern
Overview
The Resource-Aware Optimization Pattern equips agents with awareness of the computational resources they consume — tokens, API calls, latency, and cost — and strategies to optimize their use. Rather than calling the most capable model for every request or retrieving maximum context every time, resource-aware agents make intelligent trade-offs to achieve goals within budget constraints.
Core Principle: Capability without efficiency is waste — match resource consumption to task requirements.
When This Skill Applies
Activate this pattern when:
- API costs need to stay within budgets (production deployments at scale)
- Latency requirements are strict (real-time user interactions)
- Token limits constrain context window usage
- Rate limits throttle request throughput
- Different sub-tasks have vastly different complexity requirements
- Long-running batch processes must manage resource consumption over time
Rule of thumb: If cost, latency, or quota is a constraint — build resource awareness in from the start, not as an afterthought.
Resource Dimensions
| Resource | Constraint | Optimization Strategy |
|---|---|---|
| Tokens (input) | Context window limit | Summarize, truncate, selective retrieval |
| Tokens (output) | Cost, latency | Precise prompting, structured output |
| API calls | Rate limits, cost | Caching, batching, model selection |
| Latency | User experience | Async, streaming, parallel calls |
| Compute | Server cost | Efficient inference, model cascade |
DEFINE → PLAN → ACTION Workflow
DEFINE
Map resource constraints:
- What are the token budget limits per session/task?
- What is the cost budget? (Per query, per day, per user)
- What are the latency requirements? (P50, P95, P99 targets)
- What rate limits apply? (Requests per minute, tokens per minute)
- Which sub-tasks are latency-sensitive vs. batch-OK?
PLAN
Design resource optimization architecture:
- Define model selection policy: capability vs. cost trade-off by task type
- Design caching strategy: what to cache, TTL, cache invalidation
- Plan context management: how to stay within token limits
- Design batching: group independent requests to amortize overhead
- Build monitoring: track resource usage against budgets in real-time
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.
- 7d ago First seen · 378 lines · 408 tokens per session scan A 237a530f9803
resource-aware is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 5mo ago), licensed MIT. It adds 408 tokens to every session and 3,557 once invoked, about $0.0020 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to resource-aware, differing in 3 lines, and is treated as a copy.
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