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/anneal/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/anneal)<a href="https://agentmods.dev/skills/av/harbor/anneal"><img src="https://agentmods.dev/badge/skills/av/harbor/anneal/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/anneal"><img src="https://agentmods.dev/badge/skills/av/harbor/anneal.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 | $0.00049 | $0.03417 |
| Opus 5.5 | $0.00020 | $0.01367 |
| Sonnet 5.5 | $0.00010 | $0.00683 |
| Haiku 4.5 | $0.00005 | $0.00342 |
Grade A, and why
anneal 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- anneal — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anneal
Systematically harden a codebase by fixing AI-introduced code slop over a user-specified duration. The clock decides when work stops, not you.
Your Role
You are the orchestrator. You do exactly two things:
- Manage the clock — check time before every dispatch, stop when the deadline passes
- Dispatch subagents — give them the slop catalog, the progress file path, and get out of the way
You do NOT do any actual work. No code changes, no file edits, no exploration, no analysis, no "quick fixes." All productive work happens inside subagents. Your context is reserved exclusively for the dispatch loop. If you catch yourself doing anything other than checking time, reading the progress file, and dispatching — stop. That work belongs in a subagent.
The Iron Law
YOU DO NOT DECIDE WHEN THE WORK IS DONE. THE CLOCK DECIDES.
Your only job is to keep dispatching useful work until the deadline passes. You have zero authority to judge completeness, sufficiency, or "good enough." The user gave you a duration. You use all of it.
Inputs
The user provides two things:
- Codebase — the project to anneal (defaults to the current working directory)
- Duration — how long to run (e.g., "4 hours", "overnight", "90 minutes")
If the duration is vague ("overnight"), interpret it as 8 hours. If truly ambiguous, ask once.
The Slop Catalog
These are the patterns AI agents actually introduce into codebases. Every subagent receives this catalog as its detection guide. The subagent picks the highest-impact instance it can find — the catalog is a field guide, not a queue.
1. Duplication Instead of Reuse
Reimplements logic that already exists elsewhere in the codebase. The agent lacked full-repo context and produced a new version instead of calling the existing one. Look for: near-identical functions across files, same algorithm implemented with different variable names, utility code that duplicates a library the project already depends on.
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 · 283 lines · 49 tokens per session scan A e00230717069
anneal is a skill published in the GitHub repository av/harbor (3,263 stars, last pushed 6d ago), licensed Apache-2.0. It adds 49 tokens to every session and 3,417 once invoked, about $0.0002 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.
anneal compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| annealav/harbor | Skill | 3.3k | 6d ago | Apache-2.0 | A | 49 | Claude Code, Codex |
| perfepicsagas/epic-harness | Skill | 20 | yesterday | Apache-2.0 | A | 32 | Claude Code |
| impactnikolay-e/diffctx | Skill | 6 | today | Apache-2.0 | A | 53 | Claude Code |
| sephera-change-safetyReim-developer/Sephera | Skill | 77 | 7d ago | GPL-3.0 | A | 80 | Claude Code, Codex |
| docker-managementfurkangonel/cowrangler | Skill | 1 | 1mo ago | MIT | C | 16 | Claude Code, Codex |
| printing-press-output-reviewmvanhorn/cli-printing-press | Skill | 4.9k | yesterday | MIT | A | 102 | Claude Code |
| smiles-validationsynthetic-sciences/openscience | Skill | 4.3k | 2d ago | Apache-2.0 | A | 24 | Claude Code, Codex |