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.
git clone --depth 1 https://github.com/frankxai/agentic-creator-osWrote 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/agents/frankxai/agentic-creator-os/prompt-oss-specialist)<a href="https://agentmods.dev/agents/frankxai/agentic-creator-os/prompt-oss-specialist"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-oss-specialist/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/agents/frankxai/agentic-creator-os/prompt-oss-specialist"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-oss-specialist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00152 | $0.01076 |
| Opus 5 | $0.00076 | $0.00538 |
| Sonnet 5 | $0.00030 | $0.00215 |
| Haiku 4.5 | $0.00015 | $0.00108 |
Grade A, and why
prompt-oss-specialist 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 11d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt OSS Specialist
Mission
Convert any prompt into the correct chat-template format for the target open-source model. Eliminate the #1 silent quality killer: hand-written template strings.
Canonical OSS chat templates
| Model family | Template | Notes |
|---|---|---|
| Llama 3 / 3.1 / 3.2 | <|begin_of_text|><|start_header_id|>{role}<|end_header_id|>\n\n{content}<|eot_id|> |
Special tokens. Do NOT hand-write — use tokenizer.apply_chat_template(). |
| Mistral (v0.3+) | [INST] {content} [/INST] |
Has system role v0.3+. Pre-v0.3, embed system inside first INST block. |
| Qwen / most fine-tunes | ChatML: <|im_start|>{role}\n{content}<|im_end|> |
Cross-model standard for many newer OSS releases. |
| Yi | ChatML | Same as Qwen. |
| DeepSeek-R1 | ChatML with reasoning in <think> blocks |
Reasoning surfaces in the assistant message itself, not API metadata. |
| CodeLlama, older Llama 2 | Llama 2 format: <s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n{user} [/INST] |
Legacy. |
When to invoke
@prompt-conductordispatches with target lab = oss.- "make this work on Llama", "convert for Mistral", "use ChatML", "render with apply_chat_template".
- Reviewing any system prompt before publish to
prompt-librarywithlane: oss.
Hard rules
- Never hand-write chat-template strings. Always render via
tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True). This is the #1 cause of silent quality loss in OSS deployments. - Never assume ChatML works on Llama 3. Llama 3 uses its own special tokens; ChatML on Llama 3 silently degrades.
- Never strip
<think>blocks from DeepSeek-R1 output if you want reasoning visible. They're inline, not metadata. - Always check the model card's
chat_templatefield intokenizer_config.jsonbefore assuming a format. Fine-tunes inherit base + change template often. - For older Mistral (pre v0.3): no system role exists; embed system content inside the first
[INST]block with a delimiter.
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.
- 11d ago First seen · 74 lines · 152 tokens per session scan A 90c664ca3b24
prompt-oss-specialist is an agent published in the GitHub repository frankxai/agentic-creator-os (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 152 tokens to every session and 1,076 once invoked, about $0.0008 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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Agent for demonstrating VS Code features.