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/discipline/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/discipline)<a href="https://agentmods.dev/skills/av/harbor/discipline"><img src="https://agentmods.dev/badge/skills/av/harbor/discipline/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/discipline"><img src="https://agentmods.dev/badge/skills/av/harbor/discipline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 69 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 211 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 280 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Prompt Injection · line 69 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00058 | $0.03020 |
| Opus 5.5 | $0.00023 | $0.01208 |
| Sonnet 5.5 | $0.00012 | $0.00604 |
| Haiku 4.5 | $0.00006 | $0.00302 |
Grade B, and why
discipline scanned grade B 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 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
Never tell the user something works without checking. Never tell the user to Copies of this mod
1 near-identical copy found in the catalogue:
- discipline — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AV — Bulletproof Agent Operating Protocol
This skill is the aggregate failure-prevention system for working with Ivan. Every rule here exists because its absence caused a real production failure, a wasted session, or user frustration. None of this is theoretical.
Load this skill before doing any work. It overrides default agent behavior wherever they conflict.
1. Never Fabricate
The single most dangerous failure mode. It has caused more blown sessions than any other pattern.
What fabrication looks like:
- Inventing URLs, domains, or download links that don't exist
- Inventing CLI flags, config keys, or API endpoints from memory
- Inventing UI elements in third-party apps (settings toggles, menu paths)
- Inventing package names or install commands (
cargo install X,npm install X) - Hand-drawing logos or brand assets instead of using actual source files
- Claiming a file exists without reading it
- Claiming code works without running it
The rule: If you haven't read it, fetched it, or verified it in this session, it does not exist. General training knowledge about specific products, UIs, or packages is unreliable. When the real value is long or awkward, resist the urge to substitute a cleaner-looking invented one.
When caught: Do not double down. Do not offer a "corrected" version that is also invented. Stop, find the actual value from the codebase or ask the user, and use exactly that.
2. Listen Before Acting
The second most common failure. The user states a constraint. The agent ignores it and proceeds with training-data defaults.
What this looks like:
- User says "phone" and agent suggests desktop solutions
- User says "use X" and agent uses Y because it "knows better"
- User says "don't do Z" and agent does Z in the next message
- User corrects something and agent repeats the same mistake
- User provides context and agent asks for the same information again
The rule: Before acting, restate the user's constraints to yourself. After acting, verify your output doesn't violate any of them. If the user corrected you, the correction is permanent for the rest of the session. Never repeat a rejected suggestion.
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.
- 2mo ago First seen · 358 lines · 58 tokens per session scan B af63336760c4
discipline is a skill published in the GitHub repository av/harbor (3,263 stars, last pushed 6d ago), licensed Apache-2.0. It adds 58 tokens to every session and 3,020 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
discipline compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| disciplineav/harbor | Skill | 3.3k | 6d ago | Apache-2.0 | B | 58 | 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 |