mortgage-rate-negotiation-anchoring

mortgage-rate-negotiation-anchoring is a skill for Claude Code, Codex from deciqAI/knowledge-skills. It costs 82 tokens per session (745 once invoked), scanned A, original, MIT.

A guide for discussing mortgage rates when a borrower focuses on an advertised or competitor rate. It explains how to compare rates, points, APR, and total loan cost on the same terms.

In plain words
What is it for?
It is for handling questions such as why one rate is higher than an advertisement and explaining rate-and-fee trade-offs fairly.
Why use it?
It helps resolve misleading rate comparisons caused by teaser rates, different assumptions, or hidden costs.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **Industry front door for [anchoring](../anchoring/SKILL.md).** Adds domain triggers, example, packs only. Parent Process unchanged..

Good fit It is for handling questions such as why one rate is higher than an advertisement and explaining rate-and-fee trade-offs fairly.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/deciqAI/knowledge-skills
agentmods
npx agentmods add skills/deciqai/knowledge-skills/mortgage-rate-negotiation-anchoring

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 745 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 $0.00082 $0.00745
Opus 5 $0.00041 $0.00373
Sonnet 5 $0.00016 $0.00149
Haiku 4.5 $0.00008 $0.00075

Measured 9d ago against content hash 0b0ec930d9ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

mortgage-rate-negotiation-anchoring 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 9d 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.

mortgage-rate-negotiation-anchoring/SKILL.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mortgage — Rate & Terms Anchoring (Defense + Framing)

Industry front door for anchoring. Adds domain triggers, example, packs only. Parent Process unchanged. Not legal advice. Follow fair-lending and LO-comp rules; do not use framing to steer for your own benefit.

Activate when: a borrower anchors on a headline/teaser rate they saw; presenting rate+points+APR trade-offs; competitor "lowball" quote; "why is your rate higher than the ad?" Do NOT activate when: simply disclosing final locked terms.

Why this variant

The parent anchoring shows a first number silently biases all judgment. Borrowers anchor on advertised rates that hide points/credit assumptions. The LO's job is to re-anchor on total cost/APR honestly and defend against a misleading competitor anchor.

Domain inputs → parent's Process

  • Detect the anchor: is the borrower's reference rate net of discount points / specific scenario?
  • Re-anchor on the apples-to-apples basis: same points, same lock, APR + total cost over expected tenure.
  • Defend: quantify what the teaser omits (points cost, credit assumptions, PMI).
  • Never counter-anchor with a number you can't deliver (bait-and-switch = compliance + trust failure).

Worked example

Borrower: "Bank X advertised 0.5% lower." → Re-anchor: that quote assumes 2 discount points and a 45-day lock. On equal points and lock, compare APR and 5-year total cost. Honest re-framing wins without a misleading counter-anchor.

Compliance anchors

  • Truthful advertising (Reg N/MAP Rule); fair-lending consistency; LO compensation rule (don't vary comp by terms).

Packs

  • Solo LO: apples-to-apples comparison sheet (points/lock normalized).
  • Brokerage: competitor-quote decode guide.

Red flags

  • Countering with a rate you can't lock (bait-and-switch).
  • Comparing rate without normalizing points/lock.
  • Framing that steers to higher-comp terms.

Verification

  • Borrower's anchor decomposed (points/lock/assumptions)
  • Re-anchored on APR + total cost, equal terms
  • No undeliverable counter-anchor used
  • Fair-lending / LO-comp consistency held

Read the full file on GitHub · 53 lines

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. 9d ago First seen · 53 lines · 82 tokens per session scan A 0b0ec930d9ad

Subscribe to this mod's changes

mortgage-rate-negotiation-anchoring is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 82 tokens to every session and 745 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 2026-09-03.

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