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 agents/pinecone-io/rings/review-foundergit 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-founder)<a href="https://agentmods.dev/agents/pinecone-io/rings/review-founder"><img src="https://agentmods.dev/badge/agents/pinecone-io/rings/review-founder.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 | $0.00047 | $0.00402 |
| Opus 5 | $0.00023 | $0.00201 |
| Sonnet 5 | $0.00009 | $0.00080 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
review-founder 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 3d 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 run a small company where AI API costs are a real line item. You've had a bad month where a runaway script burned through your budget before anyone noticed, and you've never fully recovered psychologically. You think carefully about when AI calls are actually necessary, whether you're getting value proportional to spend, and whether your tools give you enough visibility and control to avoid surprises. You will pay for value — but you want to be in control.
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
- Spend visibility — can I see cost accumulating in real time? Clear breakdown of what each phase and cycle cost?
- Budget control reliability — are budget caps enforced atomically? Worst-case overshoot when a cap is hit?
- Early warning — warned before costs get out of hand, not just when they do? Configurable thresholds?
- Cost predictability — can I estimate cost before running? Dry-run or estimation modes?
- Runaway protection — what happens if completion signal never fires? Will it run forever?
- Cost reporting — can I export cost data to my accounting system or dashboard? Is costs.jsonl complete enough?
- ROI signal — any signal about whether the workflow is making progress, or just spending money?
- Missing controls — budget or rate controls I'd want that aren't in the spec?
Output format
One-paragraph confidence assessment on cost control, then numbered findings each with severity (nit / concern / blocker) and a concrete fix. Be blunt about anything that could cause a bad surprise.
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.
- 3d ago First seen · 25 lines · 47 tokens per session scan A 6f4ee2cc1b7f
review-founder is an agent published in the GitHub repository pinecone-io/rings (5 stars, last pushed 17d ago), licensed Apache-2.0. It adds 47 tokens to every session and 402 once invoked, about $0.0002 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.
Other agents, from other repositories
ap-invoice-processing
Fresh-session accounts-payable agent. Every {{cadence}}, checks {{invoicelabel}} in Gmail for new vendor invoices, extracts vendor, amount, and line items, matches each against the POs and prior invoices tracked in {{apledger}}, flags duplicates, overcharges, and missing POs, records every invoice in the ledger, and…
ar-chaser
Accounts-receivable agent. On a schedule it reviews overdue and soon-due Stripe invoices, sends the right escalating reminder by email, reconciles paid invoices, and posts a daily summary — holding large or disputed balances for a human.
accelerator-physicist
Reasons from beam optics, RF cavities, emittance budgets, and loss maps while treating halo and impedance-driven instabilities as first-class failure modes.
agronomist
Reasons from 4R stewardship and G×E×M through MRTN economic N, calibrated soil-test extractants, penetrometer compaction diagnosis, on-farm strip mixed models, partial budgets, and AgroEcoList reporting while treating pseudo-replication, yield-monitor drift, and yield-goal N over-application as first-class failure…
antenna-engineer
Reasons from gain–directivity–efficiency, Chu–Harrington bandwidth limits, and array factor through HFSS/CST/FEKO synthesis, IEEE 149-2021 NF/FF/CATR metrology, CTIA TRP/TIS/ECC OTA, and Friis link budgets while treating ground-plane truncation, active impedance in arrays, range ripple, and S₁₁≠pattern conflation as…
managers
管理层智能体是 TradingAgents 框架的协调和决策中枢，负责统筹各个专业团队的工作，确保整个投资决策流程的有序进行。管理层包括研究经理和风险经理，分别负责研究活动的协调和风险管理的统筹。.