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 underwriting-researchgit 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/underwriting-research)<a href="https://agentmods.dev/skills/fusion-data-company/bristol-os/underwriting-research"><img src="https://agentmods.dev/badge/skills/fusion-data-company/bristol-os/underwriting-research/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/underwriting-research"><img src="https://agentmods.dev/badge/skills/fusion-data-company/bristol-os/underwriting-research.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 | $0.00086 | $0.00626 |
| Opus 5 | $0.00043 | $0.00313 |
| Sonnet 5 | $0.00017 | $0.00125 |
| Haiku 4.5 | $0.00009 | $0.00063 |
Grade A, and why
underwriting-research 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Underwriting Research
Bristol builds its own financial models. This play researches and pressure-tests the assumptions that feed the model so they're grounded in market reality and sourced.
When to use
"What rents can we underwrite?", "research property taxes/insurance in [market]," "what are construction costs running?", "are these expense assumptions reasonable?", "any incentives available here?"
Assumption areas (research + cite each)
- Revenue — achievable market rents by unit type (pull from market-comp-analysis), realistic stabilized occupancy, loss-to-lease/concessions, other income (parking, pet, fees).
- Operating expenses — submarket OpEx benchmarks per unit; payroll, R&M, marketing, management fee, utilities, G&A.
- Property taxes — local rate/assessment methodology; how new construction is assessed; reassessment risk at stabilization. (Often a major swing factor — research carefully.)
- Insurance — current multifamily insurance cost trend in the market (rising fast in many regions; flag it).
- Construction / hard costs — current $/SF or $/unit ranges for the product type and region; cost trend; long-lead items.
- Soft costs & timing — typical soft-cost load, entitlement/permitting timeline, construction duration, lease-up pace.
- Capital markets context — prevailing construction debt terms and market exit cap rates for the product/market (for the yield-on-cost spread).
- Incentives — TIF, PILOT, tax abatement, opportunity zones, grants, or programs available at the site; eligibility and rough value.
Method
- For each assumption: give a researched range (low / base / high) with sources and dates, not a single guess.
- Compare to any numbers the user already has in their model; flag where their assumption looks aggressive or conservative versus the market, and why.
- Call out the 2–3 assumptions the deal is most sensitive to.
Output
- An assumptions memo saved to the deal folder: each input, the researched range, the source, and a flag (supports / stretch / risk).
- A short "watch these" list of the highest-sensitivity inputs.
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 · 36 lines · 86 tokens per session scan A ae261275d825
underwriting-research is a skill published in the GitHub repository Fusion-Data-Company/bristol-os (1 stars, last pushed 2mo ago), licensed MIT. It adds 86 tokens to every session and 626 once invoked, about $0.0004 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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