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 skills add big-emotion/agent-atelier --skill ai-code-tellsgit clone --depth 1 https://github.com/big-emotion/agent-atelierWrote 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/big-emotion/agent-atelier/ai-code-tells)<a href="https://agentmods.dev/skills/big-emotion/agent-atelier/ai-code-tells"><img src="https://agentmods.dev/badge/skills/big-emotion/agent-atelier/ai-code-tells/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/big-emotion/agent-atelier/ai-code-tells"><img src="https://agentmods.dev/badge/skills/big-emotion/agent-atelier/ai-code-tells.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00123 | $0.01244 |
| Opus 5 | $0.00062 | $0.00622 |
| Sonnet 5 | $0.00025 | $0.00249 |
| Haiku 4.5 | $0.00012 | $0.00124 |
Grade A, and why
ai-code-tells 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 9d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Code Tells Review
A focused, read-only review that catches the five consistent signatures of AI code that nobody read before accepting it. It does not fix anything by default — it reports findings so a human keeps the mental model. Apply fixes only when the user asks.
The five tells below are the complete rule set — the skill needs no external configuration. If your user-level or project CLAUDE.md defines equivalent always-on code-quality guardrails, this skill acts as their on-demand review pass; honor any project-level tightening in the repo's own CLAUDE.md (e.g. hard gates).
When to Activate
- The user invokes
/ai-code-tellsor asks to "check for AI tells / AI smells". - After generating a non-trivial chunk of code, before presenting it as done.
- Before opening a PR, as a self-review pass.
Scope — what to review
Resolve the target in this order, stop at the first that applies:
- An explicit path, file list, or PR number the user named.
- Staged changes:
git diff --cached. If empty → - Working-tree changes:
git diff. If empty → - The current branch vs its merge-base with the default branch:
git diff $(git merge-base HEAD origin/main 2>/dev/null || git merge-base HEAD main)...HEAD.
Review only the changed lines and enough surrounding context to judge them. Do not audit the whole repo. If nothing is in scope, say so and stop.
The five tells
For each, scan the diff and record findings. A finding is file:line — <tell> — why it's a tell — concrete fix.
-
Narrating comments. Comments that restate what the code literally does (
// increment counter,// loop over the list and process each item,// return the result). The fix: delete it, or replace it with the why (a decision, constraint, or gotcha) if one exists. A method whose every line has a play-by-play comment is the strongest signal. -
Generic, domain-blind names. Identifiers like
data,result,item,value,obj,temp,process(),handle(),doStuff(),Manager,Helper,Utilwhere a domain word exists. The fix: rename to what it is in the problem domain. Flag only where a more meaningful name is genuinely available — loop indices and trivially-scoped temporaries are fine.
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.
- 9d ago First seen · 66 lines · 123 tokens per session scan A e865ee30ec79
ai-code-tells is a skill published in the GitHub repository big-emotion/agent-atelier (2 stars, last pushed 2d ago), licensed MIT. It adds 123 tokens to every session and 1,244 once invoked, about $0.0006 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 2026-08-31.
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