Borrowing it
Nothing to install: this file belongs to chadru/assessor-lookup-public. 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/chadru/assessor-lookup-public/codex/public-release/.agents/skills/onboard-locale/SKILL.mdgit clone --depth 1 https://github.com/chadru/assessor-lookup-publicWrote 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/skills/chadru/assessor-lookup-public/onboard-locale)<a href="https://agentmods.dev/skills/chadru/assessor-lookup-public/onboard-locale"><img src="https://agentmods.dev/badge/skills/chadru/assessor-lookup-public/onboard-locale/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/skills/chadru/assessor-lookup-public/onboard-locale"><img src="https://agentmods.dev/badge/skills/chadru/assessor-lookup-public/onboard-locale.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 | $0.00053 | $0.00495 |
| Opus 5.5 | $0.00021 | $0.00198 |
| Sonnet 5.5 | $0.00011 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
onboard-locale 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
Onboard a locale
Goal: take the counties an appraiser works in and configure each one for repeated use — cached source + a pinned golden record — reporting honestly which are usable for the full discrepancy check.
Steps
-
Gather inputs. Get the county list and one sample property per county (address or parcel). If the user has an MLS export, pull a sample from it; otherwise ask for one per county — coverage can't be measured without a real property. Delegate the run to the county-onboarder agent if you want it handled end-to-end.
-
Probe each county with the MCP tool
probe_county(county, state, address|parcel)(orpython tests/harness.py --probe "<County>" --address "..."). Readcheck_ready:- true → GLA/beds/baths/year come through; full discrepancy check works.
- false → only owner/legal/value available; the county's public data is thinner. Say so plainly — it is the source's data, not a defect.
-
Onboard the keepers with
onboard_county(...)(orpython tests/harness.py --onboard "<County>" --parcel ...). This caches the assessor source and pins a golden record to the user config (~/.config/assessor-lookup/), so the county is re-checked on every run. -
If a county doesn't resolve at all, switch to the
add-countyskill. -
Verify and report. Run
run_regression(orpython tests/harness.py) to show every onboarded county reacting. Present a table: county | platform | check-ready | latency | notes. Then tell the user they can point MLS exports at theappraisal-checkskill.
Principle
API-first, and honesty about coverage. Never present a not-check-ready county's blanks as if the check succeeded.
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 · 41 lines · 53 tokens per session scan A 263b57fcac65
onboard-locale is a skill published in the GitHub repository chadru/assessor-lookup-public (6 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 495 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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