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/timeboxed-iterating/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/timeboxed-iterating)<a href="https://agentmods.dev/skills/av/harbor/timeboxed-iterating"><img src="https://agentmods.dev/badge/skills/av/harbor/timeboxed-iterating/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/timeboxed-iterating"><img src="https://agentmods.dev/badge/skills/av/harbor/timeboxed-iterating.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 2 findings, up to low
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 →
- low Excessive Agency · line 151 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
- low Excessive Agency · line 183 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00032 | $0.02152 |
| Opus 5.5 | $0.00013 | $0.00861 |
| Sonnet 5.5 | $0.00006 | $0.00430 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade A, and why
timeboxed-iterating 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Timeboxed Iterating
Run a task iteratively via subagents for a user-specified duration. The clock is the only authority on when to stop. You are not.
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 goal, 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:
- Goal — what to do (e.g., "improve test coverage", "refactor AI slop", "build out the spec")
- Duration — how long to do it (e.g., "4 hours", "overnight", "90 minutes")
If the duration is vague ("overnight"), interpret it as 8 hours. If truly ambiguous, ask once.
The Process
digraph timeboxed {
rankdir=TB;
node [shape=box];
start [label="Record start time\nCompute deadline" shape=doublecircle];
init [label="Create progress file\nin /tmp"];
check [label="Check current time\nagainst deadline" shape=diamond];
read_log [label="Read progress file\nfor orchestrator context"];
dispatch [label="Dispatch subagent\nwith goal + progress file path"];
update [label="Update progress file\nwith iteration results"];
summary [label="Write final summary\nto progress file" shape=doublecircle];
start -> init -> check;
check -> read_log [label="time remains"];
check -> summary [label="deadline passed"];
read_log -> dispatch;
dispatch -> update;
update -> check;
}
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 · 193 lines · 32 tokens per session scan A d32cedd064ec
timeboxed-iterating is a skill published in the GitHub repository av/harbor (3,263 stars, last pushed 6d ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,152 once invoked, about $0.0001 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.
timeboxed-iterating compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| timeboxed-iteratingav/harbor | Skill | 3.3k | 6d ago | Apache-2.0 | A | 32 | 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 |