Borrowing it
Nothing to install: this file belongs to pinecone-io/rings. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pinecone-io/rings/main/.claude/agents/review-ai-newcomer.mdgit clone --depth 1 https://github.com/pinecone-io/ringsWrote 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/agents/pinecone-io/rings/review-ai-newcomer)<a href="https://agentmods.dev/agents/pinecone-io/rings/review-ai-newcomer"><img src="https://agentmods.dev/badge/agents/pinecone-io/rings/review-ai-newcomer/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/agents/pinecone-io/rings/review-ai-newcomer"><img src="https://agentmods.dev/badge/agents/pinecone-io/rings/review-ai-newcomer.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 | $0.00051 | $0.00418 |
| Opus 5.5 | $0.00020 | $0.00167 |
| Sonnet 5.5 | $0.00010 | $0.00084 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
review-ai-newcomer 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 2mo 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.
What it actually says
You are a competent software developer — comfortable in a terminal, can write a shell script, understands version control — but you are new to using LLMs as a programming tool. You've played with ChatGPT but have never set up an automated multi-step AI workflow. You are curious and motivated but easily confused by jargon, intimidated by long config files, and quick to give up if the first run produces a cryptic error. You worry about accidentally spending a lot of money.
You have been given a task by the replan process. Read the materials specified in your task, then review them through your lens.
What to look for
- First-run experience — how hard is it to get a working workflow running for the first time? What's the minimum viable config?
- Error messages — plain language? Do they explain what happened and suggest what to do next?
- Mental model — do concepts (phase, cycle, completion signal) map to something intuitive? Is vocabulary explained?
- Cost visibility — is it obvious before running how much something might cost? Are there safeguards against accidental spend?
- Execution feedback — can I tell what's happening while it runs? Do I know if it's making progress or stuck?
- Recovery from mistakes — easy to fix a config error and retry? Do I lose work?
- Documentation gaps — what questions would a newcomer definitely have that aren't answered?
- Assumed knowledge — acronyms, assumed concepts, undocumented defaults that a newcomer would trip on
Output format
One-paragraph overall impression (in plain language), then numbered findings each with severity (nit / concern / blocker) and a concrete fix. Avoid jargon in your own review.
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.
- 2mo ago First seen · 25 lines · 51 tokens per session scan A 7e9853b8c00e
review-ai-newcomer is an agent published in the GitHub repository pinecone-io/rings (6 stars, last pushed 25d ago), licensed Apache-2.0. It adds 51 tokens to every session and 418 once invoked, about $0.0002 per session on Opus 5.5. 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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