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-discovernpx skills add av/harbor --skill facts-discovergit 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-discover)<a href="https://agentmods.dev/skills/av/harbor/facts-discover"><img src="https://agentmods.dev/badge/skills/av/harbor/facts-discover.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 5.1 | $0.00082 | $0.04362 |
| Opus 5 | $0.00041 | $0.02181 |
| Sonnet 5 | $0.00016 | $0.00872 |
| Haiku 4.5 | $0.00008 | $0.00436 |
Grade A, and why
facts-discover 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 6d 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
facts-discover
You are a fact sheet maintainer. Your job is to scan the codebase, classify every fact by lifecycle stage, and add missing truths — in a single session.
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
This skill classifies facts and syncs the fact sheet with reality. Only use when the user explicitly asks to discover, audit, or sync facts. If the user says "work on facts" or "add facts", they want to define spec — use the facts skill instead, not this one.
Goal
After running this skill, every fact should have the correct lifecycle tag:
@draft— the fact is vague or high-level; needs refinement before it can be implemented (e.g. "this project supports stripe payments")@spec— the fact is precise and actionable, but the code doesn't back it up yet (e.g. "POST /payments creates a Stripe PaymentIntent and returns the client secret")@implemented— the fact is true and the codebase proves it- Untagged — ground truth discovered from the codebase; already verified by observation
Additionally, add facts about important truths not yet in the fact sheet (these go in untagged, since they're already true), fix inaccurate facts, and remove obsolete ones.
Facts with good validation commands are self-enforcing — they catch regressions automatically. But a manual fact is better than a fact with a useless command. A command that always passes regardless of whether the fact is true gives false confidence and is worse than no command at all. Only add a command when it genuinely tests the claim.
Process
1. Load the current fact sheet
Run facts list to see all current facts. Note which sections exist and what they cover.
Run facts check to see which command-facts pass and which fail. Failing facts are candidates for removal or correction.
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.
- 6d ago First seen · 312 lines · 82 tokens per session scan A 66766f8691fc
facts-discover is a skill published in the GitHub repository av/harbor (3,205 stars, last pushed 6d ago), licensed Apache-2.0. It adds 82 tokens to every session and 4,362 once invoked, about $0.0004 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.
Other skills, from other repositories
frontend-feature
Build a new page, view, or data-driven feature in the Next.js frontend. Use when adding a route under the dashboard/marketing area, wiring UI to a backend endpoint, adding client state, or creating a localized page. Covers App Router, data fetching, Zustand stores, and i18n.
alembic-migration
Create, review, and apply database schema changes with Alembic. Use whenever a SQLAlchemy model is added or changed, a column/index/constraint needs to change, or a data backfill is required — anything that alters the PostgreSQL schema.
billing-stripe
Work with Stripe billing — subscriptions, plans/prices, the Customer Portal, credits, usage metering, invoices, and webhook events. Use when changing plans, handling a new Stripe webhook, debugging a payment/subscription flow, or touching credit balances and usage.
docker-local-build
Build and test Kurtosis from source on local Docker. Compiles all components (engine, core, files-artifacts-expander), builds Docker images, installs the CLI, and restarts the engine. Use when developing Kurtosis and testing changes locally with Docker.
docker-debug
Debug Kurtosis running on local Docker. Inspect engine, API container, and service logs. Diagnose container crashes, port conflicts, and networking issues. Use when kurtosis commands fail or services aren't reachable on Docker.
starlark-dev
Develop and debug Kurtosis Starlark packages. Create packages from scratch, understand the plan-based execution model, use print() debugging, handle future references, and test packages locally. Use when writing or troubleshooting .star files.