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 agentmods add skills/wecode-ai/wegent/prompt-optimizationnpx skills add wecode-ai/Wegent --skill prompt-optimizationgit clone --depth 1 https://github.com/wecode-ai/WegentWrote 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/wecode-ai/wegent/prompt-optimization)<a href="https://agentmods.dev/skills/wecode-ai/wegent/prompt-optimization"><img src="https://agentmods.dev/badge/skills/wecode-ai/wegent/prompt-optimization.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 | $0.00062 | $0.00938 |
| Opus 5 | $0.00031 | $0.00469 |
| Sonnet 5 | $0.00012 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
prompt-optimization 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 4d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimization Skill
This skill allows you to view and modify the system prompts of the current AI agent (Team).
Available Tools
get_team_prompt()— Get the current prompt and source mapping for the teamsubmit_prompt_changes(changes)— Send optimized prompts to the user for review
Workflow
Step 1: Get Current Prompts
Call get_team_prompt(). It returns:
team_id: The team's database IDassembled_prompt: The full assembled promptsources: Array of prompt sources, each with:type:"ghost"(base prompt) or"member"(team member prompt)id: Resource ID (Ghost ID or Team ID)name: Display name (Ghost name or Bot name)field: Field name (e.g.,"systemPrompt"or"prompt")content: The actual prompt textindex: Member index (only for"member"type)
Step 2: Analyze and Rewrite
Based on the user's request, determine which source(s) need modification:
- If the user wants to change the agent's core behavior → modify the
ghostsource - If the user wants to change a specific team member's role → modify the
membersource - You may modify multiple sources if needed
Rules:
- Only modify the sources that are relevant to the user's request
- Preserve the overall structure and intent of unrelated parts
- Write complete, production-quality prompts (not just appending text)
- If the original prompt is in Chinese, write the modification in Chinese
- If the original prompt is in English, write the modification in English
Step 3: Submit Changes
Call submit_prompt_changes(changes=[...]) with your changes. Each change must include:
{
"type": "ghost", // or "member"
"id": 561, // from source.id
"name": "Ghost名称", // from source.name, used for display
"field": "systemPrompt", // from source.field
"original": "原始内容", // from source.content
"suggested": "修改后内容", // your optimized version
"index": 0 // only for "member" type, from source.index
}
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.
- 4d ago First seen · 100 lines · 62 tokens per session scan A 75522fcda67f
prompt-optimization is a skill published in the GitHub repository wecode-ai/Wegent (776 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 938 once invoked, about $0.0003 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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