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 a locally served Muse Glimmer via ollama. Use when delegation-core selects glimmer or the prompt must not leave the machine.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Glimmer is Meta's Muse Glimmer served locally through ollama, so a delegation to it spends no quota and sends no prompt off the machine. It is the last entry in the candidate order for the same reason it is the safe one: a local 30B model is slower and weaker than any of the network providers ahead of it.
delegation-core selected glimmer for the taskollama list shows no muse-glimmer:30b. A registered provider
whose model was never pulled fails at the first delegation, not at
registrationcurl -fsSL https://ollama.com/install.sh | sh
ollama pull muse-glimmer:30b
Both steps are required. Installing ollama registers the binary and
pulls nothing, so ollama --version answering is not evidence that a
delegation will succeed.
None. auth_method is "none" and the auth probe is empty, because a
local server has no credential to check.
uv run python scripts/delegation_executor.py glimmer "Summarize" \
--files src/
make -C plugins/conjure delegate-glimmer PROMPT='Summarize' FILES='src/'
ollama run muse-glimmer:30b "Explain this module"
glimmer carries priority=80, the highest number in the registry,
which places it last in the candidate order. It declares no model ids,
because the model is fixed by the subcommand rather than chosen per
call.
| Property | Value |
|---|---|
| Binary | ollama |
| Headless form | ollama run muse-glimmer:30b |
| Prompt delivery | stdin |
| File context | inlined into the prompt |
| Version probe | ollama --version |
| Auth probe | none |
| Output format flag | --format |
| Model flag | none |
Read off ollama run --help at 0.13.1: the usage line is ollama run MODEL [PROMPT] [flags], so the model is positional and is carried by
the subcommand. The flag list has no --model, which is why this
service declares model_flag=None; passing one exits 1 on an unknown
flag. --format string is documented there and is the reason
output_format_flag is --format rather than the registry default
--output-format, which the same CLI rejects.
No temperature flag is declared. ollama run --help documents none,
and inventing one is the error class install_hint and login_hint
already guard against.
ollama resolves on PATH and answers ollama --versionollama list includes muse-glimmer:30b before a task is
delegated; an absent model stops execution with the pull command
rather than failing inside the delegationollama run muse-glimmer:30b with the
prompt on stdin and no prompt flag--model flag appears in the built commanddelegations/glimmer/YYYYMMDD_HHMMSS.mddelegation-core selected glimmer, or
because the prompt was required to stay local아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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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