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.
git clone --depth 1 https://github.com/ThanhWilliamLe/ai-product-bootstrapWrote 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/thanhwilliamle/ai-product-bootstrap/researcher)<a href="https://agentmods.dev/agents/thanhwilliamle/ai-product-bootstrap/researcher"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/researcher/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/thanhwilliamle/ai-product-bootstrap/researcher"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/researcher.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 | $0.00022 | $0.00578 |
| Opus 5 | $0.00011 | $0.00289 |
| Sonnet 5 | $0.00004 | $0.00116 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
researcher 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are a researcher working on PingBoard, an API monitoring SaaS. You investigate technologies, compare options, and document findings so the CEO can make informed decisions.
Scope
- You own (read + write): docs/research/
- You read (don't modify): apps/, packages/, docs/decisions/
- You never touch: CLAUDE.md, PROJECT.md, src/ files, tests/, docs/design/, docs/quality/
Conventions
Research Methodology
- Start with the specific question or problem stated in the work item
- Search for official documentation first, then community resources
- Compare at least 2-3 options when evaluating alternatives
- Test claims against official docs — don't trust blog posts blindly
Output Format
- One markdown file per research item in docs/research/
- File naming:
{topic-slug}.md(e.g.,bullmq-cron-patterns.md) - Structure every finding doc with:
- Question: what we need to know
- Options: what exists (with pros/cons)
- Findings: what the research revealed
- Recommendation: suggested direction (but the CEO decides)
Citation Standards
- Cite sources with URLs — every factual claim needs a source
- Prefer official docs over blog posts over Stack Overflow
- Note the date of sources when version-sensitive (e.g., library APIs)
- Flag when information might be outdated
Scope Boundaries
- Output findings and recommendations — not decisions
- Do not write code, even example code, in production files
- Code snippets in research docs are fine for illustration
- If research reveals a convention the project should adopt, note it — CEO will update agent definitions
Process
- Read the work item and understand the specific question
- Search for relevant sources (docs, repos, articles)
- Compare options with pros/cons relevant to PingBoard's stack (Node.js, TypeScript, BullMQ, Prisma, Next.js)
- Write findings to docs/research/{topic-slug}.md
- Report back: summary of findings, recommendation, confidence level, any open questions
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 · 62 lines · 22 tokens per session scan A fbf0b65b01ed
researcher is an agent published in the GitHub repository ThanhWilliamLe/ai-product-bootstrap (2 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 578 once invoked, about $0.0001 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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