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.
npx skills add Fusion-Data-Company/bristol-os --skill quarry-parcelsgit clone --depth 1 https://github.com/Fusion-Data-Company/bristol-osWrote 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/fusion-data-company/bristol-os/quarry-parcels)<a href="https://agentmods.dev/skills/fusion-data-company/bristol-os/quarry-parcels"><img src="https://agentmods.dev/badge/skills/fusion-data-company/bristol-os/quarry-parcels/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/fusion-data-company/bristol-os/quarry-parcels"><img src="https://agentmods.dev/badge/skills/fusion-data-company/bristol-os/quarry-parcels.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 | $0.00099 | $0.00698 |
| Opus 5 | $0.00049 | $0.00349 |
| Sonnet 5 | $0.00020 | $0.00140 |
| Haiku 4.5 | $0.00010 | $0.00070 |
Grade A, and why
quarry-parcels 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 11d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quarry — Parcel Data
Bristol's own parcel engine. When anyone asks about a property, this is where the answer comes from — not a paid lookup site, not a map they have to click. Claude queries it and returns the facts.
HARD LAW
If they ask for parcel data, just get it and give it to them. Don't open a map, don't explain how, don't ask them to do anything. Run the query, read back the answer, and put it in the one-pager/deal folder.
How (Claude runs this; user does nothing, no key needed)
# by address (auto-geocodes, free)
python bristol-os/skills/quarry-parcels/quarry_lookup.py --address "381 Mallory Station Rd, Franklin TN"
# by coordinates
python bristol-os/skills/quarry-parcels/quarry_lookup.py --lat 36.16 --lng -86.78
# everything in a map area (returns a list — no map shown)
python bristol-os/skills/quarry-parcels/quarry_lookup.py --bbox "-86.90,35.90,-86.80,35.97" --limit 200
The base URL is baked in; parcel lookups need no key and cost nothing. It returns JSON — owner, mailing address, absentee flag, county, APN, acreage, lot sqft, zoning, year built, building sqft, units, and value.
What to do with the result
- Read it back in plain English ("This parcel is owned by 616 Church LLC — an absentee owner mailing to Cleveland, OH; 0.09 acres, zoned Downtown Code, appraised at $X").
- Drop it straight into the site one-pager (
bristol-os/templates/site-one-pager.md) owner/zoning/value fields, and save to the deal folder. - Cite "Quarry" + the date.
- If it returns null, say the parcel wasn't found — never invent it.
Owner contact (skip-trace, optional)
Quarry can also return the owner's phone/email. Run with --skiptrace:
python bristol-os/skills/quarry-parcels/quarry_lookup.py --skiptrace --address "123 Main St, Nashville TN"
python bristol-os/skills/quarry-parcels/quarry_lookup.py --skiptrace --apn 0123456789
Returns up to 2 phones + 2 emails with confidence. This draws on Bristol's Quarry account.
Feeds
- site-selection / one-pager: owner, zoning, units, acreage, value.
- investor-sourcing / owner outreach: owner + mailing address (+ contact via
--skiptrace). - market-comp-analysis:
--bboxto inventory nearby parcels.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 42 lines · 99 tokens per session scan A 8ab0f322a12f
quarry-parcels is a skill published in the GitHub repository Fusion-Data-Company/bristol-os (1 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 698 once invoked, about $0.0005 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
synthesize
Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges…
trade-idea-generation
Systematic trade idea generation framework, fundamental quantitative and qualitative screening pipeline, catalyst-driven trade structuring, macro-to-micro idea translation, 20-60 day long-short portfolio management process.
chart-patterns
Chart pattern recognition, price action patterns, candlestick signal bars, pullback bar counting H1/H2/H3/H4, trend channels, micro channels, trading ranges, breakouts, major trend reversals 5-step sequence, wedges, double tops and bottoms, triangles, head and shoulders, climaxes, measured moves, support and…
trade-execution
Trade execution using price action setups, major trend reversal trading top and bottom, MTR failure continuation, strong bull and bear breakout trading, strong and weak channel trading strategies, trading range strategies, opening range swings, integration with gold.technicalsetups for setup matching and execution…
challenge
Adversarial verification of research theses — cross-run/cross-thesis contradiction via the entity index, pre-mortem (Klarman/Kahneman: assume the loss already happened, reverse the path), inversion (Munger: invert, always invert), and wrongif falsifiability review. ≈50-finding cap sorted by severity; content-derived…
converge
Append-only gap closure and the cadence engine for research theses. Evaluates artifact CURRENT state (never git history, never task checkboxes) against current pins and wrongif falsifiers, then appends a Convergence section to tasks.md re-proposing the gaps. A clean run leaves tasks.md byte-identical. Deterministic…