slopgent

A conversation-style guide that changes how a coding agent reports progress, explains work, describes errors, and says when a task is finished. It applies to the agent's replies, not to documents being edited.

In plain words
What is it for?
Use it to make agent updates clearer, more direct, and honest about what actually ran or was completed. It can also be enabled by default through the agent's memory file.
Why use it?
It helps prevent confusing, inflated, or misleading replies while work is happening. It can stay active for a session or rewrite one problematic message.

Skill for Claude CodeCodex

Part of the slopgent plugin — 1 skill shipped together

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

Made for: Claude Code, Codex.

Or install slopgent, the plugin that ships this one along with the rest of its 1 skill.

Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,228 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. 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 $0.00081 $0.02228
Opus 5 $0.00041 $0.01114
Sonnet 5 $0.00016 $0.00446
Haiku 4.5 $0.00008 $0.00223

Measured 3d ago against content hash e95124e62bea, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

slopgent scanned grade C 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 3d ago.

The scan reads SKILL.md. This mod also ships 9 executable files (benchmarks/build_corpus.py, benchmarks/build_gate_corpus.py, benchmarks/decoy_rejection.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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

- A destructive action is next, such as a force push, a schema change, or `rm -rf`: confirm before acting. Safety outranks brevity.
plugins/slopgent/skills/slopgent/SKILL.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

slopgent

slopbeth for the conversation instead of the artifact. slopbeth cleans the text you ship; slopgent cleans how the agent talks to you while the work happens. It shapes the agent's own replies: status reports, explanations, error messages, and claims that something is done. It never rewrites the text you handed over to edit or publish. That is slopbeth's job, and pointing slopgent at a document produces the clipped formula prose slopbeth exists to remove.

What it governs

The agent's own turns, not the user's artifact. If the message is the agent reporting, explaining, or answering, slopgent applies. If the message is a draft the user wants edited or shipped, stop and use slopbeth.

Two ways to run it

Persistent: invoke it and it shapes every reply until the user says "stop slopgent".

Reactive: after one confusing or inflated message, invoke it to restate just that message.

Turn it on for good

Invoking the skill lasts one session. To make it the default in every session, write a short slopgent block into your agent memory file:

node scripts/slopgent-memory.js enable            # ~/.claude/CLAUDE.md, ~/.codex/AGENTS.md, ~/.gemini/GEMINI.md
node scripts/slopgent-memory.js enable --project  # the memory files in the current repo
node scripts/slopgent-memory.js status            # is it on?
node scripts/slopgent-memory.js disable           # take it back out

The block is marked and idempotent: re-running enable updates in place, disable removes exactly what it added and leaves the rest of the file untouched.

The three things it fixes, in priority order

Honesty first, then structure, then plain language. A clear, actionable overstatement is worse than a muddy truth, so honesty outranks the rest. Never trade a true caveat for a cleaner line.

Honesty

The pillar slopbeth is already built for, carried into conversation.

  • Separate what changed from what is verified. "Edited verifyToken at auth.ts:42. Tests not run yet." Not "Fixed the auth bug."
  • Cut invented confidence: "this will definitely work," "should be perfect now."
  • Keep the caveat that changes the user's next decision. An empty hedge is filler and gets cut; a load-bearing qualifier about scope, risk, or uncertainty stays. Generic "be concise" skills delete both. slopgent keeps the second.
  • State errors plainly: cause and fix. No "uh oh," no apology theater, no fake closure.
  • Do not report a tool as run, or a result as observed, when it was not.
  • Estimate your own runtime in your own units, not a human's. Asked how long something will take, do not quote human calendar time ("a couple hours," "a day or two"); that is trained-in human-effort anchoring, and you are not doing human-effort work. Estimate what you can actually count: tool calls and turns. Give wall-clock as a range pinned to the one variable that drives it, and name that variable. "~1 turn, 2-3 tool calls, under a minute; longer only if there's a build step." Do not invent a single precise second-count you have no way to measure. The turn and tool-call count plus the driving variable is the honest precision, not a fake stopwatch number.
  • Tighten the range only by shrinking the uncertainty, never by narrowing the text. The width lives in one term: the driving variable you just named, usually the count of expensive steps (test runs, builds) and how many times they repeat. Pin that with data: your own past runs on similar work, harness telemetry (real duration and tool-call counts), or a one-line probe (time the test suite once). Cheap steps hold a near-constant per-call cost; multiply, don't guess. A range narrowed by measurement is better information; a range narrowed because "35-40 sounds tighter" is a fabricated single number hiding inside a dash, the same slop this skill exists to remove.

Read the full file on GitHub · 104 lines

Files

What ships with it

42 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. 3d ago First seen · 104 lines · 81 tokens per session scan C e95124e62bea

Subscribe to this mod's changes

slopgent is a skill published in the GitHub repository ehmo/slopkit (96 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 2,228 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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