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/appraisal-check/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/appraisal-check)<a href="https://agentmods.dev/skills/chadru/assessor-lookup-public/appraisal-check"><img src="https://agentmods.dev/badge/skills/chadru/assessor-lookup-public/appraisal-check/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/appraisal-check"><img src="https://agentmods.dev/badge/skills/chadru/assessor-lookup-public/appraisal-check.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 | $0.00054 | $0.00461 |
| Opus 5 | $0.00027 | $0.00230 |
| Sonnet 5 | $0.00011 | $0.00092 |
| Haiku 4.5 | $0.00005 | $0.00046 |
Grade A, and why
appraisal-check 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.
What it actually says
Appraisal public-records check
Goal: turn an MLS export into a field-by-field discrepancy report against the county's public records, so the appraiser fixes mismatches before the report ships.
Steps
-
Identify the county. It is auto-detected from the CSV
Countycolumn; if absent, ask or infer. Confirm it is covered withlist_counties. If it is not, run theonboard-localeskill first (a single county is fine). -
Run the check with the MCP tool
check_mls_csv(subject_csv, comps_csv, county, state). The subject CSV's first row is the subject; the comps CSV holds comparables. Standard PPMLS and RESO/REColorado column names are both understood. (CLI equivalent:assessor-lookup check subject.csv comps.csv --county "<County>".) -
Report the discrepancies. For each property, show the flagged fields. Focus on the stable fields — GLA, beds, baths, year built, basement — where
flagis true; those are the real discrepancies to reconcile. Values, owner, and taxes drift legitimately year to year, so don't alarm on those. Include theassessor_urlfor any flagged property so the appraiser can verify the record. -
Explain the common ones. Multi-level homes (tri-level, 4-level) routinely show a large GLA diff because MLS counts the lower level as basement while the assessor counts it above-grade — flag it as expected, not an error. Bath counts often differ (assessor total vs MLS above-grade split).
Principle
Report what the records actually say. Note any property that came back
not_found or timeout so the appraiser checks it manually — don't silently
drop it.
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.
- 12d ago First seen · 39 lines · 54 tokens per session scan A dce71f5c8c11
appraisal-check is a skill published in the GitHub repository chadru/assessor-lookup-public (6 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 461 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.
Other skills, from other repositories
csv-to-sif
Export a project's FF&E product-library CSV as dealer-system SIF. Use to produce a .sif schedule; use sif-to-csv for the reverse direction.
product-data-cleanup
Clean a local FF&E CSV schedule by normalizing casing, dimensions, units, language, materials, and formatting. Use when asked to clean, fix, or standardize product data.
product-data-import
Generate a formatted FF&E specification schedule from notes, CSV, or pasted lists and optionally save it to the project's 33-column CSV library. Use when asked to import products or build a schedule.
sif-to-csv
Parse a SIF (Standard Interchange Format) input into a readable preview and optionally append canonical product rows to the project's CSV library. Use when asked to convert or inspect SIF dealer data. For the reverse direction use /as:csv-to-sif.
dcf-model
Build discounted cash flow valuation workbooks in Excel.
audit-xls
Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model (including financial-model integrity checks like BS balance, cash tie-out, and logic sanity). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this…