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 knopki/agent-skills --skill glm-promptgit clone --depth 1 https://github.com/knopki/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/knopki/agent-skills/glm-prompt)<a href="https://agentmods.dev/skills/knopki/agent-skills/glm-prompt"><img src="https://agentmods.dev/badge/skills/knopki/agent-skills/glm-prompt/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/knopki/agent-skills/glm-prompt"><img src="https://agentmods.dev/badge/skills/knopki/agent-skills/glm-prompt.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 | $0.00083 | $0.03655 |
| Opus 5 | $0.00042 | $0.01827 |
| Sonnet 5 | $0.00017 | $0.00731 |
| Haiku 4.5 | $0.00008 | $0.00365 |
Grade A, and why
glm-prompt scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- For a fix: `curl … ; expect { "ok": true }` How it starts
The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GLM Prompt Skill
Overview
Build prompts that play to GLM 5.2's strengths and avoid its known failure modes.
Two operating modes:
- Interactive mode (prompt is for the end user in this session): run the interrogation in §3 — collect the info the user has, then fill the template.
- Subagent / autonomous mode (prompt will be sent to a GLM subagent or a fresh GLM session): decide the content yourself from available context — do NOT interrogate the user. Use §4.
Decide the mode from how the request is phrased:
- "write me a prompt for GLM to do X" / "I want to prompt GLM to…" → interactive.
- "launch a subagent to do X on GLM" / "prepare a prompt for the GLM agent" /
the prompt is a parameter you pass to a
Task/Agentcall → subagent mode.
If the intent is ambiguous, ask one clarifying question: "Is this prompt for you to send into a GLM session yourself, or should I write it as a subagent/Agent task prompt?" — then proceed.
When to Use
Trigger this skill whenever you are about to produce a prompt whose target runtime is a GLM model. Concretely:
- The user asks you to "write / draft / compose a prompt for GLM", "make a prompt for GLM", "format this task for GLM".
- You are composing the
prompt:argument of atask()/ subagent call that will run on a GLM model. - You are translating a vague user request into a clean GLM task spec.
Do not trigger this skill for non-GLM models.
1) The format — Goal, Context, Constraints, Done
GLM is trained on structured long-horizon tasks. Four sections are enough for it to plan a trajectory. Use this exact structure:
### Goal
[1–2 sentences: what to do]
### Context
- Files: [paths, with line ranges when known]
- Current state: [how it is now; if a bug — what's wrong]
- Why: [the reason this is being done]
### Constraints
- Do not touch: [boundaries — what cannot change]
- Style: [language, code style, architectural rules]
- Versions: [dependencies, API versions]
### Done
- Validation: [command or scenario — how to be sure]
- Result format: [what to return — file, summary, diff]
What ships with it
1 file 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.
- 12d ago First seen · 371 lines · 83 tokens per session scan A 8105473e99c3
glm-prompt is a skill published in the GitHub repository knopki/agent-skills (2 stars, last pushed 10d ago), licensed MIT. It adds 83 tokens to every session and 3,655 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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