harbor: Skill for Claude Code

.agents/skills/facts-refine/SKILL.md

facts-refine is a skill for Claude Code, Codex from av/harbor. It costs 68 tokens per session (1,698 once invoked), scanned A, original, Apache-2.0.

A collaborative editing workflow for turning rough fact notes into precise specifications that are ready to implement.

In plain words
What is it for?
Use it to review a fact sheet, clarify requirements, split broad ideas into smaller specifications, and decide how draft facts should become implementation-ready facts.
Why use it?
It helps uncover vague wording, missing details, contradictions, and unclear labels before they cause implementation problems. Changes are discussed with the user instead of being applied silently.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is av/harbor's own configuration. It tells Claude Code and Codex how to work on harbor itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything harbor configures →

About the project

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.

av/harbor · 3,263 stars · on GitHub · discord.gg

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/av/harbor/main/.agents/skills/facts-refine/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/av/harbor

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for facts-refine

README.md
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Your own site
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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.

agentmods 80×15 button for facts-refine

Your own site · 80×15
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Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,698 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.01698
Opus 5.5 $0.00027 $0.00679
Sonnet 5.5 $0.00014 $0.00340
Haiku 4.5 $0.00007 $0.00170

Measured 2mo ago against content hash 2242f08f7f1f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.agents/skills/facts-refine/SKILL.md · 156 lines

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

Read the full file on GitHub · 156 lines

Changes

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.

  1. 2mo ago First seen · 156 lines · 68 tokens per session scan A 2242f08f7f1f

Subscribe to this mod's changes

facts-refine is a skill published in the GitHub repository av/harbor (3,263 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 5.5. 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.

Related

facts-refine compared with skills from other repositories

facts-refine next to 6 skills from other repositories
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facts-refineav/harborSkill3.3k6d agoApache-2.0A68Claude Code, Codex
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agent-builderchandrudp29/skillhubSkill144mo agoMITA42Claude Code, Codex
synapsedinglebear-ai/synapseSkill24d agoAGPL-3.0A67Claude Code
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docker-managementfurkangonel/cowranglerSkill11mo agoMITC16Claude Code, Codex
clinicaltrials-databasesynthetic-sciences/openscienceSkill4.3k2d agoApache-2.0A47Claude Code, Codex