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 agentmods add skills/ssube/conclave/ollamanpx skills add ssube/conclave --skill ollamagit clone --depth 1 https://github.com/ssube/conclaveWrote 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/ssube/conclave/ollama)<a href="https://agentmods.dev/skills/ssube/conclave/ollama"><img src="https://agentmods.dev/badge/skills/ssube/conclave/ollama.svg" alt="Measured on agentmods" 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 | $0.00058 | $0.01287 |
| Opus 5 | $0.00029 | $0.00643 |
| Sonnet 5 | $0.00012 | $0.00257 |
| Haiku 4.5 | $0.00006 | $0.00129 |
Grade A, and why
ollama 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 4d 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.
Verify with `curl $OLLAMA_HOST/api/tags` that the vision model is pulled. How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ollama — Local LLM Generation
Generate text and caption images using a local Ollama instance. No cloud API keys required — everything runs on your own hardware.
Actions
caption — Caption images for training datasets
Uses a two-stage pipeline: a vision model sees the image, then a text model writes a polished caption in the requested style.
Single image:
python3 {baseDir}/ollama_generate.py caption /path/to/image.png [--style training|tags|detailed]
Batch directory (writes .txt files alongside each image):
python3 {baseDir}/ollama_generate.py caption /path/to/dataset/ [--style training] [--overwrite]
Creative mode — embellishes with atmosphere, mood, and descriptive richness:
python3 {baseDir}/ollama_generate.py caption /path/to/image.png --style detailed --creative
Without --creative: faithful, accurate captions — only what the vision model sees.
With --creative: adds atmosphere, mood, and sensory detail. Use for model cards,
gallery descriptions, social alt text — anywhere prose matters more than raw data.
Caption styles:
| Style | Format | Use for |
|---|---|---|
training |
Natural language paragraph (50-150 words) | Flux/T5 LoRA training datasets |
tags |
Booru-style comma-separated tags (20-40 tags) | SDXL/Pony/Illustrious training |
detailed |
Rich prose description (2-4 sentences) | Model cards, galleries, catalogs |
Options:
--creative/-c— Embellish with atmosphere and mood (default: accurate)--trigger <word>— Include a trigger word in every caption (for LoRA training)--prefix <text>— Prepend text to every caption--overwrite— Overwrite existing.txtfiles (default: skip existing)
Batch output pattern:
dataset/
├── image_001.png
├── image_001.txt ← generated caption
├── image_002.jpg
├── image_002.txt ← generated caption
└── ...
prompt — Generate an image prompt from a concept
python3 {baseDir}/ollama_generate.py prompt "<concept>" [--encoder clip|t5]
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.
- 4d ago First seen · 153 lines · 58 tokens per session scan A e3e53cf4d80d
ollama is a skill published in the GitHub repository ssube/conclave (2 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 1,287 once invoked, about $0.0003 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.
Other skills, from other repositories
strategy
Run structured brainstorming sessions (divergent/convergent), improve prompts with 7-dimension framework, and apply decision frameworks (RICE, weighted scoring, first principles, pre-mortem).
autoresearch
Self-improving optimization via Karpathy autoresearch pattern. Generates → evaluates → scores → mutates prompts/descriptions in a loop. Targets — tool-selection, system-prompt, skill, decision-parser. Use when "optimize tools", "autoresearch", "improve skill X", "self-improve prompts", "optimize tool descriptions".
prompt
Use ONLY when the user explicitly asks to improve a prompt aimed at an AI: "improve my prompt", "fix this prompt", "how should I ask for this", "make this prompt better". A prompt pasted to be answered or executed is a task, not a rewrite request: do that task instead. About prompts for an AI, not about optimizing…
prompt-architect
Designs clear, testable prompts for agent workflows. Use when creating new prompts, refining weak prompts, or establishing reusable prompting patterns.
sc:image-gen-prompting
Craft effective text-to-image prompts for AI generators (Higgsfield, Midjourney, DALL·E/GPT-Image, Stable Diffusion, Flux). Subject + style + composition + lighting structure, negative prompts, aspect ratios, model-specific tips, and Hebrew→English prompt translation. Activate for: generate image, AI art, product…
prompt-engineering-expert
Expert prompt engineering: prompt structure, few-shot, chain-of-thought, structured output, and iteration. Trigger keywords: prompt, prompt engineering, system prompt, few-shot, chain-of-thought, output format, JSON schema, structured output, instructions, role, examples, eval. Use for writing, debugging, or…