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 G1Joshi/Agent-Skills --skill deepseekgit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/deepseek)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/deepseek"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/deepseek.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 | $0.00015 | $0.00301 |
| Opus 5 | $0.00008 | $0.00151 |
| Sonnet 5 | $0.00003 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
deepseek 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 8d 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.
What it actually says
DeepSeek
DeepSeek (from China) disrupted the market in late 2024/2025 by releasing DeepSeek-V3 and R1 (Reasoning) with performance matching Claude/GPT-4 at 1/10th the cost.
When to Use
- Cost Efficiency: The API is incredibly cheap.
- Reasoning: DeepSeek-R1 uses Chain-of-Thought reinforcement learning (like OpenAI o1) but is open weights.
- Coding: DeepSeek-Coder-V2 is a top-tier coding model.
Core Concepts
MLA (Multi-Head Latent Attention)
Architectural innovation that drastically reduces KV cache memory usage (allowing huge context).
DeepSeek-R1
A reasoning model that outputs its "thought process" before the final answer.
Best Practices (2025)
Do:
- Use R1 for Math/Logic: It rivals o1-preview in math benchmarks.
- Local Distillations: Run
DeepSeek-R1-Distill-Llama-70Blocally for private reasoning.
Don't:
- Don't suppress thoughts: When using R1, the "thought" trace is valuable for debugging the model's logic.
References
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.
- 8d ago First seen · 40 lines · 15 tokens per session scan A eb36cfff3ccf
deepseek is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 301 once invoked, about $0.0001 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 2026-08-30.
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