s1-shortlist

s1-shortlist is an agent for coding agents from TGPSKI/leather. It costs 4 tokens per session (273 once invoked), scanned A, original, GPL-3.0.

An agent instruction for narrowing down one Kubernetes issue from a supplied analysis note. Kubernetes is software for running and managing containerized applications.

In plain words
What is it for?
Use it to reduce the search field for an issue using its repository, component, symptom, keyword, and summary details.
Why use it?
It limits the agent to shortlisting relevant possibilities without making the final decision or using the catalogue.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/tgpski/leather/s1-shortlist
Clone the repo
git clone --depth 1 https://github.com/TGPSKI/leather

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 s1-shortlist

README.md
[![agentmods](https://agentmods.dev/badge/agents/tgpski/leather/s1-shortlist.svg)](https://agentmods.dev/agents/tgpski/leather/s1-shortlist)
Your own site
<a href="https://agentmods.dev/agents/tgpski/leather/s1-shortlist"><img src="https://agentmods.dev/badge/agents/tgpski/leather/s1-shortlist.svg" alt="Measured on agentmods" height="20"></a>
Per session 4 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 273 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00004 $0.00273
Opus 5 $0.00002 $0.00137
Sonnet 5 $0.00001 $0.00055
Haiku 4.5 $0.00000 $0.00027

Measured 5d ago against content hash e3d9fc6f10d1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

s1-shortlist 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 5d 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.

examples/14-sig-triage/agents/s1-shortlist.agent.md · 29 lines

The source is not reproduced here

Licensed GPL-3.0

The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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. 5d ago First seen · 29 lines · 4 tokens per session scan A e3d9fc6f10d1

Subscribe to this mod's changes

s1-shortlist is an agent published in the GitHub repository TGPSKI/leather (17 stars, last pushed 27d ago), licensed GPL-3.0. It adds 4 tokens to every session and 273 once invoked, about $0.0000 per session on Opus 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.

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