slopbeth

A software adapter that exposes an AI agent through the Agent2Agent (A2A) Protocol, a standard way for AI agents to communicate. It accepts text and returns text while the adapter handles the protocol details.

In plain words
What is it for?
Use it to expose agents or workflows built with tools such as LangChain, CrewAI, LangGraph, n8n, Ollama, or custom code. It can also start local agents through a command-line interface.
Why use it?
It avoids writing the networking, task-management, streaming, and notification code needed to make an existing agent available as an A2A server.

Skill for Claude CodeCodex

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 skills/ehmo/slopkit/slopbeth
Any agent
npx skills add ehmo/slopkit --skill slopbeth
Clone the repo
git clone --depth 1 https://github.com/ehmo/slopkit

Made for: Claude Code, Codex.

Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,566 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00065 $0.01566
Opus 5 $0.00032 $0.00783
Sonnet 5 $0.00013 $0.00313
Haiku 4.5 $0.00006 $0.00157

Measured 2d ago against content hash dfb2d461b8c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

slopbeth 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 2d ago.

The scan reads SKILL.md. This mod also ships 13 executable files (scripts/cadence_score.py, scripts/competitor_output_score.py, scripts/density_report.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/slopbeth/skills/slopbeth/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Slopbeth

Remove machine-writing tells without sanding away the author's meaning or voice. The target is not "detector-proof" prose; it is dense, specific writing where every sentence carries load and detector results stay dated and tool-specific.

Workflow

  1. Classify the task: rewrite; critique; benchmark; detector-facing validation; or skill maintenance.
  2. Separate the brief from the artifact. Long inputs often mix the material with instructions about it: "the note should keep that texture"; "do not turn this into a lesson"; "the rewrite must not promise that the problem cannot recur". Those sentences address you, not the reader. Do what they ask and leave them out of the output. Reprinting them is the same class of error as inventing content, and preservation and density checks will not catch it, because instruction text is specific, sourced, and dense.
  3. Preserve facts first. Lock named entities; numbers; dates; URLs; citations; quotations; technical claims; explicit uncertainty; and the user's requested stance.
  4. Set the evidence boundary. When the user supplies only vague copy, switch to evidence-bound mode: do not invent or assert product features; dates; people; metrics; workflows; examples; customer facts; or outcome claims. Unsupported claims such as "faster decisions," "better alignment," "reduced friction," "confidence," or "momentum" must become proof gaps, questions, or explicitly attributed claims.
  5. Diagnose clusters, not isolated words. Look for filler; vague significance language; formulaic contrast; promotional inflation; padded lists; generic uplift; actorless claims; summary endings; and ornamental formatting.
  6. Rewrite in this order: preserve claims and constraints; cut scaffolding and inflated abstract nouns; apply Orwell's six rules as generation defaults (short word over long, cut deletable words, active over passive, no printed-cliche metaphor or jargon, but break any rule sooner than write something unclear or graceless); make each sentence carry a claim, example, constraint, image, number, consequence, or argumentative move; match the user's register; remove concrete details that are not sourced or clearly labeled; check for meaning loss, bland-clean prose, formula replacement, and over-editing.
  7. Validate when files or before/after text are available. Use the scripts in scripts/ for repeatable checks, then apply judgment for meaning, voice, and sentence-load failures.
  8. Output the revised text first for normal rewrite requests. Add a compact note only when it helps explain material changes, preservation risks, or remaining issues.

Read the full file on GitHub · 68 lines

Files

What ships with it

46 files 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.

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. 2d ago First seen · 68 lines · 65 tokens per session scan A dfb2d461b8c4

Subscribe to this mod's changes

slopbeth is a skill published in the GitHub repository ehmo/slopkit (96 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 1,566 once invoked, about $0.0003 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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