awesome-openclaw-agents is a curated collection of ready-to-use SOUL.md configurations that define AI agents for tasks across areas such as development, marketing, finance, support, and automation. It is for OpenClaw users who want predefined agent roles and workflows to adapt or deploy. The catalogue entries are agent-related skills drawn from this collection.
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 mergisi/awesome-openclaw-agents --skill model-cost-comparegit clone --depth 1 https://github.com/mergisi/awesome-openclaw-agentsWrote 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/mergisi/awesome-openclaw-agents/model-cost-compare)<a href="https://agentmods.dev/skills/mergisi/awesome-openclaw-agents/model-cost-compare"><img src="https://agentmods.dev/badge/skills/mergisi/awesome-openclaw-agents/model-cost-compare/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/mergisi/awesome-openclaw-agents/model-cost-compare"><img src="https://agentmods.dev/badge/skills/mergisi/awesome-openclaw-agents/model-cost-compare.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 | $0.00104 | $0.01141 |
| Opus 5.5 | $0.00042 | $0.00456 |
| Sonnet 5.5 | $0.00021 | $0.00228 |
| Haiku 4.5 | $0.00010 | $0.00114 |
Grade A, and why
model-cost-compare scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Cost Compare
Given a task description (and optionally a rough prompt / input size), estimate the cost of running it on each available model tier and recommend the cheapest one that can actually do the job.
When to use
- "Which model should I use for X?"
- "Is it worth running this on Opus or will Sonnet do?"
- "Can I offload this to a local model?"
- "/model-cost-compare — classify 10k support tickets"
Pricing table (indicative — always flag as "check provider docs")
Use these rough figures. They are not exact; confirm before quoting real numbers to the user.
| Model | Tier | Input ($/1M tok) | Output ($/1M tok) | Context | Strengths |
|---|---|---|---|---|---|
| Opus 4.6 (1M) | Frontier | ~$15 | ~$75 | 1M | Agentic, long-context, hard reasoning |
| Sonnet 4.6 | Mid | ~$3 | ~$15 | 400k | Everyday coding, agents, drafting |
| GLM-5.1 | Budget hosted | ~$0.60 | ~$2.20 | 256k | Cheap bulk work, decent reasoning |
| Minimax M2.7 | Budget hosted | ~$0.40 | ~$1.80 | 256k | Very cheap, OK for templated output |
| Gemma 4 (local) | On device | $0 marginal | $0 marginal | 32k | Free but slow, weak at multi-step logic |
Indicative pricing as of OpenClaw 2026.4.11. Check the provider docs before billing decisions.
Instructions
- Parse the user's task. Extract:
- Task type: reasoning, extraction, classification, drafting, translation, agentic tool use, long-context synthesis.
- Input size estimate: in tokens. If the user says "10k tickets averaging 500 tokens", that's 5M input tokens. If unknown, ask for a rough size.
- Output size estimate: short label? full essay? JSON record?
- Volume: one-off or batch?
- Rule out incapable models. Use this capability floor:
- Agentic multi-tool flows with long reasoning → Opus or Sonnet only.
- Structured extraction / classification with clear schema → any tier, including Gemma 4 local.
- Long-context synthesis (>400k tokens) → Opus only.
- Privacy-sensitive data that cannot leave the machine → Gemma 4 local only.
- For each surviving model, compute:
Multiply by volume. Show your arithmetic so the user can sanity-check.cost = (input_tokens / 1_000_000) * input_price + (output_tokens / 1_000_000) * output_price - Print the comparison as a Markdown table sorted cheapest first. Bold the recommended row.
- End with a one-line recommendation:
Recommended: <model> — <1-sentence reason>.
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 · 81 lines · 104 tokens per session scan A 6704391dbf64
model-cost-compare is a skill published in the GitHub repository mergisi/awesome-openclaw-agents (4,027 stars, last pushed 1mo ago), licensed MIT. It adds 104 tokens to every session and 1,141 once invoked, about $0.0004 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.
Other skills, from other repositories
claude-api-cost-optimization
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aeon
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prompt-engineer
Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
rag-architect
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
spark-engineer
Use when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.
fine-tuning-expert
Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.