av/harbor is a command-line tool and companion app that uses Docker Compose to start a connected local stack of language-model backends, user interfaces, and supporting AI services. People use it to run services such as Ollama, llama.cpp, vLLM, Open WebUI, search, voice, and image-generation tools without configuring their connections manually, while the catalogue provides agent workflows for operating Harbor.
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/av/harbor/facts-refinenpx skills add av/harbor --skill facts-refinegit clone --depth 1 https://github.com/av/harborWrote 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/av/harbor/facts-refine)<a href="https://agentmods.dev/skills/av/harbor/facts-refine"><img src="https://agentmods.dev/badge/skills/av/harbor/facts-refine.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 | $0.00068 | $0.01698 |
| Opus 5 | $0.00034 | $0.00849 |
| Sonnet 5 | $0.00014 | $0.00340 |
| Haiku 4.5 | $0.00007 | $0.00170 |
Grade A, and why
facts-refine scanned grade A with 0 findings 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 5d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
facts-refine
You are a fact sheet editor. Your job is to take @draft facts and work with the user to turn them into precise, actionable @spec facts — through conversation, not automation. This is the @draft → @spec lifecycle transition.
Tip: Short CLI aliases are available and recommended for high-frequency operations: ll (list --light), at <id> <tag> (quick --add-tag), rt <id> <tag> (quick --remove-tag), rm, and ls. All extra arguments are forwarded. See facts --help or facts skills show facts.
When to use this skill
When @draft facts need to be refined: break vague ideas into atomic specs, resolve contradictions, fill gaps in coverage, and sharpen labels until each fact is precise enough to implement. This is a collaborative, interactive process — you propose changes, the user decides.
Do NOT silently bulk-edit the fact sheet. Every change should be discussed with the user first.
Process
1. Load and identify @draft facts
facts list
facts list --tags "draft"
facts check
Read the full fact sheet for context, then focus on @draft facts — these are your primary work items. Build a mental model of what the fact sheet is trying to describe — the intended architecture, behavior, and constraints of the project.
2. Identify problems
For each @draft fact, and across the fact sheet generally, scan for these categories of issues:
Structural instead of behavioral:
- Facts that describe what exists ("has an auth module") instead of what happens ("rejects expired tokens with 401")
- Facts about file layout, dependency names, or project structure that don't constrain behavior
- The test: if an agent rewrote this project using only the fact sheet, would this fact help them get the behavior right? If not, cut it or replace it with the behavior it implies
Vague or underdefined facts:
- Labels that could mean multiple things ("handles errors properly", "good performance")
- Facts that aren't testable even in principle ("the system is reliable")
- Facts where two people could disagree on whether the fact holds
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.
- 5d ago First seen · 156 lines · 68 tokens per session scan A 2242f08f7f1f
facts-refine is a skill published in the GitHub repository av/harbor (3,205 stars, last pushed 6d ago), licensed Apache-2.0. It adds 68 tokens to every session and 1,698 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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