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CD anti-slop

Sovereign Anti-AI Slop Directive & Enforcement Engine. Absolute zero-tolerance standard for lazy placeholders, conversational fluff, syntax narration, speculative over-engineering, hallucinated code, and generic UI clichés / Doktrin dan mesin penegakan anti-AI slop mutlak. Standar nol toleransi terhadap placeholder malas, basa-basi, komentar sintaksis, over-engineering, dan klise visual UI.

roedyrustam/vibes-plug Agent Skills author: roedyrustam MIT 2 files · 1 script body ≈ 8 312 tokens Open the sourcegithub.com↗ analyzed 4 h ago

Sovereign Anti-AI Slop Directive & Enforcement Engine.

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
58
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 8312 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 19): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 48/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 30Running it twice. 4 mutating operations with no state check
  • 40Execution cost. Instruction body is 8312 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 198 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 12 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -276 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 393: enough signal without eating the budget
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 198 items
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.