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.
Borrowing it
Nothing to install: this file belongs to av/harbor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/av/harbor/main/.agents/skills/facts-discover/SKILL.mdgit 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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/av/harbor/facts-discover"><img src="https://agentmods.dev/badge/skills/av/harbor/facts-discover.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.5 | $0.00033 | $0.01745 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo ago First seen · 312 lines · 82 tokens per session scan A 66766f8691fc
facts-discover is a skill published in the GitHub repository av/harbor (3,263 stars, last pushed 6d ago), licensed Apache-2.0. It adds 82 tokens to every session and 4,362 once invoked, about $0.0003 per session on Opus 5.5. 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.
facts-discover compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| facts-discoverav/harbor | Skill | 3.3k | 6d ago | Apache-2.0 | A | 82 | Claude Code, Codex |
| hugging-face-tool-buildersynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 67 | Claude Code, Codex |
| agent-builderchandrudp29/skillhub | Skill | 14 | 4mo ago | MIT | A | 42 | Claude Code, Codex |
| synapsedinglebear-ai/synapse | Skill | 2 | 4d ago | AGPL-3.0 | A | 67 | Claude Code |
| docker-essentialsasgarovf/locusai | Skill | 23 | 8mo ago | MIT | A | 35 | Claude Code |
| docker-managementfurkangonel/cowrangler | Skill | 1 | 1mo ago | MIT | C | 16 | Claude Code, Codex |
| clinicaltrials-databasesynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 47 | Claude Code, Codex |