Borrowing it
Nothing to install: this file belongs to josemsantiago/qb-ai-toolkit. 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/josemsantiago/qb-ai-toolkit/main/CLAUDE.mdgit clone --depth 1 https://github.com/josemsantiago/qb-ai-toolkitWrote 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/instructions/josemsantiago/qb-ai-toolkit/claude-md)<a href="https://agentmods.dev/instructions/josemsantiago/qb-ai-toolkit/claude-md"><img src="https://agentmods.dev/badge/instructions/josemsantiago/qb-ai-toolkit/claude-md/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/instructions/josemsantiago/qb-ai-toolkit/claude-md"><img src="https://agentmods.dev/badge/instructions/josemsantiago/qb-ai-toolkit/claude-md.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 | $0.01561 | $0.01561 |
| Opus 5 | $0.00781 | $0.00781 |
| Sonnet 5 | $0.00312 | $0.00312 |
| Haiku 4.5 | $0.00156 | $0.00156 |
Grade A, and why
qb-ai-toolkit CLAUDE.md 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — context for Claude Code
This file is read automatically by Claude Code. Keep it short, accurate, and specific to THIS project so suggestions are grounded in our real setup.
What this repo is
A small toolkit for working with our Quickbase app safely from the command line,
built on the QuickBaseClient wrapper (Python). Use it for analysis, exports,
formula drafting, and careful bulk updates.
There are two ways in: a read-only MCP server (quickbase_mcp.py) for asking
questions in Claude chat, and this wrapper for building and for any writes. All
create / update / delete work goes through the wrapper — never the MCP.
When using the MCP, users speak in plain language and won't know table names or
field ids. The server scans the configured app(s) on startup and exposes a
get_catalog() tool (apps, tables, fields, types, choices, example values) —
call it first, map the user's wording to the right ids, then query. If access
looks wrong or recently changed, call refresh_access() to re-scan.
How to connect (do this — don't invent another way)
- Never hard-code or paste the token. Credentials come from environment variables.
- Use
config.get_client()— it readsQB_REALM,QB_USER_TOKEN, andQB_APP_IDfrom the environment (a local.envis loaded automatically in dev). - Example:
from config import get_client client = get_client() result = client.query_records(table_id="bxxxxxxx", where="{3.GT.'0'}", select=[3, 6, 7])
The wrapper's key methods
query_records(table_id, select=[...], where="...", sort_by=[...], options={...})→ dict withdata(list of{ "<fieldId>": {"value": ...} }),fields,metadata.get_records_paginated(table_id, where=..., select=..., page_size=1000, max_records=None)→ iterator over individual record dicts (use this for large pulls).upsert_records(table_id, records, merge_field_id, fields_to_return=None)→recordsis[{fieldId: value, ...}]. Usemerge_field_id=3(Record ID#) to UPDATE existing records only — this never creates new records.get_fields(table_id),get_tables(app_id),get_app(app_id),get_relationships(table_id).run_formula(table_id, formula, record_id=None)→ test a formula via the API without changing the schema.delete_records(table_id, where)→ permanent. Do not call without explicit human sign-off.
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 · 95 lines · 1,561 tokens per session scan A 5dd21c240303
qb-ai-toolkit CLAUDE.md is an instructions file published in the GitHub repository josemsantiago/qb-ai-toolkit (0 stars, last pushed 1mo ago), licensed MIT. It adds 1,561 tokens to every session, about $0.0078 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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