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/glm-delegation

Delegates tasks to Z.ai GLM-5.x via the stock claude binary and an endpoint swap. Use when delegation-core selects GLM or a 1M-token context is needed.

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Install
$ npx -y skills add athola/claude-night-market --skill glm-delegation --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/glm-delegation

Context preview

The summary Claude sees to decide when to auto-load this skill.

Delegates tasks to Z.ai GLM-5.x via the stock claude binary and an endpoint swap. Use when delegation-core selects GLM or a 1M-token context is needed.

SKILL.md

glm-delegation.SKILL.md
name: glm-delegation
description: Delegates tasks to Z.ai GLM-5.x via the stock claude binary and an endpoint swap. Use when delegation-core selects GLM or a 1M-token context is needed.
alwaysApply: false
category: delegation-implementation
tags:
- glm
- zai
- endpoint-swap
- delegation
- large-context
dependencies:
- delegation-core
tools:
- claude
- delegation_executor.py
usage_patterns:
- endpoint-swap-integration
- large-context-analysis
- coding-plan-routing
complexity: intermediate
model_hint: standard
estimated_tokens: 650
references:
- delegation-core/shared-shell-execution.md

Table of Contents

  • [Overview](#overview)
  • [When To Use](#when-to-use)
  • [When NOT To Use](#when-not-to-use)
  • [Prerequisites](#prerequisites)
  • [Quick Start](#quick-start)
  • [Smart Delegation](#smart-delegation)
  • [GLM-Specific Details](#glm-specific-details)
  • [Exit Criteria](#exit-criteria)

GLM Delegation

Overview

GLM ships no CLI of its own. Z.ai serves an Anthropic-compatible endpoint, so the stock `claude` binary reaches GLM by changing two environment variables and nothing else. This is the endpoint-swap archetype: the redirection is environment, never argv.

GLM-5.3 was released on 2026-08-14 and claims a large coding gain over GLM-5.2 from post-training alone.

When To Use

  • `delegation-core` selected `glm` for the task
  • A 1M-token context is wanted through `glm-5.3[1m]`
  • A GLM Coding Plan subscription should absorb the work instead of the

Anthropic quota

When NOT To Use

  • `ZAI_API_KEY` is unset. Verification refuses rather than sending an

unauthenticated request

  • The `claude` binary is not installed. GLM has no fallback CLI
  • A pinned GLM-5.3 identifier is required in a script that must not

break: see the rollout note below

Prerequisites

Installation

Only the stock Claude Code binary is needed:

npm install -g @anthropic-ai/claude-code

Authentication

export ZAI_API_KEY="<your-zai-key>"

The delegation service builds the child environment from that one variable. Z.ai requires `ANTHROPIC_AUTH_TOKEN`, not `ANTHROPIC_API_KEY`. Putting the key in the wrong one is the most common way to get a 401 here, so the service names the correct variable and the overlay references `${ZAI_API_KEY}` rather than storing a secret.

Quick Start

Using the shared delegation executor

uv run python scripts/delegation_executor.py glm "Review this design" \
  --files docs/

Through the Makefile

make -C plugins/conjure delegate-glm PROMPT='Review this design'

Direct CLI usage

ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic \
ANTHROPIC_AUTH_TOKEN="$ZAI_API_KEY" \
claude --model glm-5.3 -p "Review this design"

Smart Delegation

`glm` declares `code_execution` and `large_context` strengths and ranks behind gemini, qwen and minimax in the candidate order. It declares `glm-5.3` as its default, `glm-5.3[1m]` for large context, and `glm-4.7` for fast response.

GLM-Specific Details

| Property | Value | |----------|-------| | Binary | `claude` (stock) | | Base URL | `https://api.z.ai/api/anthropic` | | Auth variable | `ANTHROPIC_AUTH_TOKEN`, from `ZAI_API_KEY` | | Default model | `glm-5.3` | | Large context | `glm-5.3[1m]` | | OpenAI-compatible URL | `https://api.z.ai/api/coding/paas/v4` |

The `[1m]` suffix is what unlocks the 1M-token window. Omitting it is a documented setup mistake. Pair it with `CLAUDE_CODE_AUTO_COMPACT_WINDOW=1000000` when driving `claude` directly.

Two operational notes. Environment changes only take effect in a freshly launched shell, so an already-open session keeps reaching Anthropic's real endpoint. And `claude` shows a first-run trust prompt when it sees a non-Anthropic base URL. The `-p` non-interactive form this service uses does not block on it.

Rollout caveat: at the time of writing, `docs.z.ai/devpack/latest-model` documented `glm-5.3` while `docs.z.ai/guides/llm/glm-5.3` still read "coming soon" and the base connection page still showed `glm-5.2`. `GLM_52` stays defined for that reason, and `glm-4.7` remains the fast-response id rather than being promoted on assumption.

The same archetype generalizes: MiniMax, Moonshot/Kimi, DeepSeek and OpenRouter all expose Anthropic-compatible endpoints, so adding one is a `ServiceConfig` with a different base URL.

Exit Criteria

  • [ ] `claude` resolves on PATH and answers `claude --version`
  • [ ] `ZAI_API_KEY` is set, and `--verify` reports the service

available rather than naming an unset variable

  • [ ] The built command contains no `z.ai` URL: the endpoint travels in

the child environment, not argv

  • [ ] The child environment carries `ANTHROPIC_AUTH_TOKEN`, not

`ANTHROPIC_API_KEY`

  • [ ] Output saved to `delegations/glm/YYYYMMDD_HHMMSS.md`
  • [ ] This skill ran because `delegation-core` selected `glm`
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