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 IchenDEV/prompt-optimizer-plugins --skill optimize-glm-promptsgit clone --depth 1 https://github.com/IchenDEV/prompt-optimizer-pluginsWrote 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/ichendev/prompt-optimizer-plugins/optimize-glm-prompts)<a href="https://agentmods.dev/skills/ichendev/prompt-optimizer-plugins/optimize-glm-prompts"><img src="https://agentmods.dev/badge/skills/ichendev/prompt-optimizer-plugins/optimize-glm-prompts.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 | $0.00134 | $0.01263 |
| Opus 5 | $0.00067 | $0.00632 |
| Sonnet 5 | $0.00027 | $0.00253 |
| Haiku 4.5 | $0.00013 | $0.00126 |
Grade A, and why
optimize-glm-prompts 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize GLM Prompts
Turn rough ideas and existing prompt stacks into copy-ready GLM prompts. Preserve the user's intent and hard constraints, ask for genuinely blocking information, and add structure only when it changes model behavior.
Use the official basis
Read references/official-guidance.md when model-specific rationale or the longer checklist is needed. Treat its prose as a dated summary and its URL as the canonical source. Fetch the live Zhipu AI page before making claims about current model IDs, API features, parameters, context limits, availability, or pricing.
Follow the workflow
1. Capture the prompt contract
Identify:
- the intended user-visible outcome, audience, and language;
- the inputs, background, references, or retrieval results GLM will receive;
- standing behavior that belongs in a system prompt, if the integration supports one;
- facts and hard constraints to preserve;
- the output content, format, schema, and length;
- evidence, reasoning visibility, validation, and fallback behavior needed for success.
Treat these as diagnostic dimensions, not mandatory headings. Preserve system, developer, user, and tool-description boundaries when the user supplies a layered prompt stack.
2. Apply the clarity gate
Ask a question only when two reasonable answers would materially change the outcome, source boundary, prompt layer, schema, permissions, or success criteria.
Treat these gaps as blocking by default:
- a vague topic and generic verb with no identifiable deliverable or audience;
- a required backend schema with undefined fields or semantics;
- an unspecified source set whose choice changes the answer;
- an external action without clear authorization;
- conflicting role, scope, evidence, or output requirements.
When clarification is blocking, ask one to three high-information questions and do not produce a provisional prompt. Otherwise proceed with conservative local assumptions and list only assumptions actually made. Never invent facts, sources, policies, access, or permissions.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 91 lines · 134 tokens per session scan A cb1323c7f826
optimize-glm-prompts is a skill published in the GitHub repository IchenDEV/prompt-optimizer-plugins (6 stars, last pushed yesterday), licensed MIT. It adds 134 tokens to every session and 1,263 once invoked, about $0.0007 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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