assessor-lookup-public: Skill for Claude Code

.agents/skills/onboard-locale/SKILL.md

onboard-locale is a skill for Claude Code, Codex from chadru/assessor-lookup-public. It costs 53 tokens per session (495 once invoked), scanned A, original, MIT.

A setup guide for adding the counties an appraiser works in to an assessor-lookup tool. It tests each county with a sample property, then saves the working source and a reference record for reuse.

In plain words
What is it for?
Use it when adding local counties, giving each county an address or parcel number, checking available fields, and configuring reusable county sources.
Why use it?
It shows whether a county's public records include the building details needed for comparisons, instead of assuming every county provides the same data.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is chadru/assessor-lookup-public's own configuration. It tells Claude Code and Codex how to work on assessor-lookup-public itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything assessor-lookup-public configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/chadru/assessor-lookup-public/codex/public-release/.agents/skills/onboard-locale/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/chadru/assessor-lookup-public

Made for: Claude Code, Codex.

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agentmods badge for onboard-locale

README.md
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agentmods 80×15 button for onboard-locale

Your own site · 80×15
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Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 495 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.00495
Opus 5 $0.00026 $0.00247
Sonnet 5 $0.00011 $0.00099
Haiku 4.5 $0.00005 $0.00049

Measured 12d ago against content hash 263b57fcac65, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 12d 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.

.agents/skills/onboard-locale/SKILL.md · 41 lines

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

  1. 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.

  2. Probe each county with the MCP tool probe_county(county, state, address|parcel) (or python tests/harness.py --probe "<County>" --address "..."). Read check_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.
  3. Onboard the keepers with onboard_county(...) (or python 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.

  4. If a county doesn't resolve at all, switch to the add-county skill.

  5. Verify and report. Run run_regression (or python 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 the appraisal-check skill.

Principle

API-first, and honesty about coverage. Never present a not-check-ready county's blanks as if the check succeeded.

Changes

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.

  1. 12d ago First seen · 41 lines · 53 tokens per session scan A 263b57fcac65

Subscribe to this mod's changes

onboard-locale is a skill published in the GitHub repository chadru/assessor-lookup-public (6 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 495 once invoked, about $0.0003 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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