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/crustdata/skillsWrote 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/rules/crustdata/skills/crustdata-tool-selection)<a href="https://agentmods.dev/rules/crustdata/skills/crustdata-tool-selection"><img src="https://agentmods.dev/badge/rules/crustdata/skills/crustdata-tool-selection/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/rules/crustdata/skills/crustdata-tool-selection"><img src="https://agentmods.dev/badge/rules/crustdata/skills/crustdata-tool-selection.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 | $0.00037 | $0.01077 |
| Opus 5.5 | $0.00015 | $0.00431 |
| Sonnet 5.5 | $0.00007 | $0.00215 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
crustdata-tool-selection 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 15d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Crustdata MCP server is Code Mode. It exposes list_tools, get_schema and execute, plus feedback (for reporting a problem with Crustdata's own data or tools) when it's listed. Every data operation happens inside an execute script:
// user query: <the user's request, in their words>
const r = await callTool('person_search', { filters, limit: 25 });
// r = { ok: true, data, credits_used? } | { ok: false, status, errorType, message }
- Start every
executescript with a// user query: <the user's request>line, or// model query: <what this run does>for your own follow-up step. The server rejects a script without one before it runs, at no charge. list_tools,get_schema,executeandfeedback(when listed) are MCP tools: call them directly, never throughcallTool. Inside a script,callTool('list_tools')orcallTool('get_schema')is rejected as an unknown tool.callToolonly takes the nameslist_toolsreturns.- Write plain JavaScript for
execute— any TypeScript syntax (: Type,as, generics) is a parse error that fails the whole run before anything is billed. - Call
get_schema({ names: ["<tool>"] })before building filters: it returns filterable columns and value formats; adddetail: "detailed"for the full column list and a response example. in_,nin_,any_ofandhas_alltake an array. Wrap a single value, as inin_(field, ['OpenAI']); a bare string throws.- A failed
callToolreturns{ ok: false, ... }; it does not throw. Checkokbefore usingdata.
Order of preference (cheapest correct path first):
- Company lookup:
company_identify(FREE) →company_enrichbycrustdata_company_id(2 credits per returned match; enriching by domain/name can fuzzy-match several companies and multiply the charge) - People search:
person_search(indexed DB, ~0.03 credits/result) → onlyperson_search_live(2 credits/result) when the DB returns 0 results or realtime data is required - Company search:
company_search(~0.03 credits/result) →company_search_live(2 credits/result) only for realtime - Contact info (emails/phones):
person_contact_enrichis THE tool — billed per contact type returned, no base fee, up to 25 profile URLs per call; narrowfields(e.g.contact.business_emails) to cap spend. Useperson_enrichonly for full profile data (work history, education, skills); itspreview: trueis free - Job listings:
job_search(filters/sorts/aggregations, ~0.03 credits/result) orjob_search_live(real-time retrieval by company, 2 credits/result) - Social posts:
social_post_list_live(posts by one person or company) orsocial_post_search_live(keyword search across professional-network posts only); for X/Twitter, Reddit or any other network useweb_search_live - Web:
web_search_live(1 credit/query) andweb_enrich_live(fetch a page by URL, 1 credit/page) - Credits:
account_credits(FREE) for the balance; a data-API tool's result carriescredits_usedfor the exact charge of that call (other tools, such asaccount_credits, return none) - Discover valid filter values:
company_autocomplete,person_autocomplete,job_autocomplete(FREE) - Bulk lists (thousands of rows): the async
batch_*twins (batch_person_enrich,batch_company_search, …) — submit, pollbatch_job_get, read viabatch_results - Alerts:
watch_create/watch_discovery_create. Send the whole body withpreview: truefirst (free, creates nothing), show the user what it found and what it will cost, then create it in a laterexecuterun. The preview run returnswatch_preview_tokens, a list of{ tool, dataset, token, reported }; pass thetokenof the entry whosereportedmatches the preview you showed aspreview_token. The server refuses a create without that token, and refuses it inside the run that minted it. If you change the body after previewing, preview again.watch_previewonly shows a sample notification and does not count.
list_tools returns the full annotated tool list with per-tool pricing; trust it over this summary when they disagree.
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.
- 15d ago Changed d82abefc4712
- 20d ago Changed · +4 lines 097d1ab7bf9b
- 1mo ago First seen · 32 lines · 37 tokens per session scan A 7b345f68abb3
crustdata-tool-selection is a cursor rule published in the GitHub repository crustdata/skills (11 stars, last pushed 14d ago), licensed MIT. It adds 37 tokens to every session and 1,077 once invoked, about $0.0001 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 2026-09-05.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).