Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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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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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
delegation-core selected glm for the taskglm-5.3[1m]ZAI_API_KEY is unset. Verification refuses rather than sending an
unauthenticated requestclaude binary is not installed. GLM has no fallback CLIOnly the stock Claude Code binary is needed:
npm install -g @anthropic-ai/claude-code
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 ${ZAI_API_KEY} rather than
storing a secret.
uv run python scripts/delegation_executor.py glm "Review this design" \
--files docs/
make -C plugins/conjure delegate-glm PROMPT='Review this design'
ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic \
ANTHROPIC_AUTH_TOKEN="$ZAI_API_KEY" \
claude --model glm-5.3 -p "Review this design"
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.
| 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.
claude resolves on PATH and answers claude --versionZAI_API_KEY is set, and --verify reports the service
available rather than naming an unset variablez.ai URL: the endpoint travels in
the child environment, not argvANTHROPIC_AUTH_TOKEN, not
ANTHROPIC_API_KEYdelegations/glm/YYYYMMDD_HHMMSS.mddelegation-core selected glm아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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