glimmer-delegation
SkillProductivityDelegates tasks to a locally served Muse Glimmer via ollama. Use when delegation-core selects glimmer or the prompt must not leave the machine.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the glimmer-delegation skill
What this skill tells your AI
The instructions your AI receives, as published by athola/claude-night-market in plugins/conjure/skills/glimmer-delegation/SKILL.md and read by ahel’s review.
Table of Contents
- Overview
- When To Use
- When NOT To Use
- Prerequisites
- Quick Start
- Smart Delegation
- Glimmer-Specific Details
- Exit Criteria
Glimmer Delegation
Overview
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.
When To Use
delegation-coreselectedglimmerfor the task- Every network provider is exhausted and the work still has to happen
- The prompt must not leave the machine, whatever the cost in quality
When NOT To Use
- The task needs the strongest available model. Glimmer is the floor, not a peer of the network providers
ollama listshows nomuse-glimmer:30b. A registered provider whose model was never pulled fails at the first delegation, not at registration
Prerequisites
Installation
curl -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.
Authentication
None. auth_method is "none" and the auth probe is empty, because a
local server has no credential to check.
Quick Start
Using the shared delegation executor
uv run python scripts/delegation_executor.py glimmer "Summarize" \
--files src/
Through the Makefile
make -C plugins/conjure delegate-glimmer PROMPT='Summarize' FILES='src/'
Direct CLI usage
ollama run muse-glimmer:30b "Explain this module"
Smart Delegation
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.
Glimmer-Specific Details
| 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.
Exit Criteria
-
ollamaresolves on PATH and answersollama --version -
ollama listincludesmuse-glimmer:30bbefore a task is delegated; an absent model stops execution with the pull command rather than failing inside the delegation - The built command is
ollama run muse-glimmer:30bwith the prompt on stdin and no prompt flag - No
--modelflag appears in the built command - Output saved to
delegations/glimmer/YYYYMMDD_HHMMSS.md - This skill ran because
delegation-coreselectedglimmer, or because the prompt was required to stay local
Signals
- GitHub stars
- 337
- Forks
- 34
- Last commit
- Sep 2026
Advanced
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glimmer-delegation- Source
- github.com/athola/claude-night-market