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.
npx agentmods add skills/code-yeongyu/lazyclaudecode/remove-ai-slopsnpx skills add code-yeongyu/lazyclaudecode --skill remove-ai-slopsgit clone --depth 1 https://github.com/code-yeongyu/lazyclaudecodeWrote 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/code-yeongyu/lazyclaudecode/remove-ai-slops)<a href="https://agentmods.dev/skills/code-yeongyu/lazyclaudecode/remove-ai-slops"><img src="https://agentmods.dev/badge/skills/code-yeongyu/lazyclaudecode/remove-ai-slops.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00182 | $0.04601 |
| Opus 5 | $0.00091 | $0.02301 |
| Sonnet 5 | $0.00036 | $0.00920 |
| Haiku 4.5 | $0.00018 | $0.00460 |
Grade A, and why
remove-ai-slops 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.
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude Code Harness Tool Compatibility
This skill may include examples copied from the OpenCode or Codex harness. In Claude Code, do not call OpenCode/Codex-only tools such as task(...), call_omo_agent(...), spawn_agent(...), background_output(...), wait_agent(...), team_*(...), send_message(...), followup_task(...), or close_agent(...) literally. Translate those examples to Claude Code native tools:
| OpenCode / Codex example | Claude Code tool to use |
|---|---|
task(subagent_type="explore", ...) / call_omo_agent(...) / spawn_agent(agent_type="explorer", ...) |
the Task tool (spawn a subagent of the matching type) |
task(subagent_type="plan"/"oracle", ...) / spawn_agent(agent_type="plan"/"reviewer", ...) |
the Task tool with the planner/reviewer subagent, or the Skill tool |
task(category="...", ...) |
the Task tool (general-purpose subagent) or run the work inline |
background_output(...) / wait_agent(...) |
await the subagent's return value / the system completion notification |
team_*(...) / send_message/followup_task/close_agent |
run multiple Task subagents and synthesize their results |
When translating load_skills=[...], invoke the requested skills with the Skill tool or pass their names in the spawned subagent's prompt. If a code block below conflicts with this section, this section wins.
Remove AI Slops Skill
Inputs
- Default scope: branch diff vs
merge-base main(no arguments needed) - Optional scope: explicit file list passed by the caller (e.g., a Ralph workflow's changed-files set)
What this skill does
Cleans AI-generated slop from a bounded set of changed files while strictly preserving behavior. Locks behavior with regression tests first, then runs a categorized multi-pass cleanup, then verifies with quality gates and a critical review. Reverts and direct-edits when verification fails.
The core safety invariant: behavior is locked by green tests before a single line is removed. A checklist alone is not safety; a passing regression test is.
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.
- 5d ago First seen · 332 lines · 182 tokens per session scan A 69c7b1149835
remove-ai-slops is a skill published in the GitHub repository code-yeongyu/lazyclaudecode (18 stars, last pushed 3mo ago), licensed MIT. It adds 182 tokens to every session and 4,601 once invoked, about $0.0009 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.
Other skills, from other repositories
craft-style
Builds a personal output style on hush's frame — the user's voice on the surface, hush's silence-and-structure mechanics copied verbatim underneath. Manages its own creations: lists them alongside stock Hush and edits them. A mechanical verifier confirms every invariant survived. Activation is hush:pick-style's job …
checkpoint
Maintain a thirty-second return point (CHECKPOINT.md) and a single-use handoff (HANDOFF.md) so any session can resume mid-goal without archaeology. Use when a large unit of work finishes, a session enters its closing stretch, when leaving instructions for the next session, or when resuming and the user asks where…
verify-gate
Treat research results and model knowledge as drafts until verified. Use when researching, quoting numbers or sources, recording knowledge as fact, or before declaring a task successful.
goal
Run a large or unfamiliar goal through the full ballast pipeline — mobilize what you already hold, terrain scan, full skeleton, atomic foundation learning with verification, then build from bedrock to a verified done. Use when the user hands over a big goal, enters a new field, or asks to learn X in order to achieve Y.
proof-standard
Never make an external-facing claim about a product without evidence from a truth file. Use when writing marketing copy, announcements, docs, landing pages, investor material, or answering "can our product do X".
rehearsal
Test a deliverable on a zero-context reader before it ships. Use before shipping any document, guide, kit, or handoff meant to work without you, and before calling a deliverable done in goal Phase 5.