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.
git clone --depth 1 https://github.com/deciqAI/knowledge-skillsnpx agentmods add skills/deciqai/knowledge-skills/realtor-price-reduction-decisionWrote 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/deciqai/knowledge-skills/realtor-price-reduction-decision)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/realtor-price-reduction-decision"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/realtor-price-reduction-decision/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/deciqai/knowledge-skills/realtor-price-reduction-decision"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/realtor-price-reduction-decision.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 | $0.00083 | $0.00670 |
| Opus 5 | $0.00042 | $0.00335 |
| Sonnet 5 | $0.00017 | $0.00134 |
| Haiku 4.5 | $0.00008 | $0.00067 |
Grade A, and why
realtor-price-reduction-decision 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.
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Real Estate — Pricing & Price-Reduction Decision
Industry front door for decision-tree. Adds domain triggers, example, packs. Parent Process unchanged. Not appraisal advice.
Activate when: setting a list price; a listing stalls (DOM up, showings without offers); deciding reduction timing/size; managing seller expectations. Do NOT activate when: priced correctly with active offers.
Why this variant
The parent decision-tree maps sequential choices under uncertainty. Pricing and reductions are a decision tree: hold vs reduce, by how much, when — against showing/offer feedback, carrying cost, and market trend, rolling back to expected net proceeds and time-to-sell.
Domain inputs → the tree
- Read the signals: showings-to-offer ratio, DOM vs market median, feedback themes, comparable adjustments.
- Branch: hold (if fresh/undersampled), small reduction (nudge into a search bracket), meaningful reduction (reset if far off).
- Value the branches by expected net proceeds × probability × carrying cost of extra DOM. Gate: reductions below a portal price bracket (e.g. $505k→$499k) capture a new buyer pool — size to brackets, not round guesses.
Worked example
30 showings, no offers, DOM 2× median, feedback "overpriced vs the one down the street." → Tree: this is a pricing (not marketing/condition) problem; a token cut won't fix a bracket miss. Reduce into the correct search bracket in one decisive move; slow drip prolongs DOM and signals weakness.
Packs
- Solo agent: showings-to-offer + DOM decision card; bracket-aware reduction sizing.
- Team: weekly stale-listing review triggering the decision.
Red flags
- Blaming marketing when the data says price.
- Tiny drip reductions that prolong DOM.
- Reductions not aligned to portal search brackets.
Verification
- Showing/offer + DOM signals reviewed vs comps
- Problem diagnosed (price vs condition vs marketing)
- Reduction sized to search brackets, not round numbers
- Expected net proceeds vs carrying cost weighed
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.
- 9d ago First seen · 45 lines · 83 tokens per session scan A e768af53d384
realtor-price-reduction-decision is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 83 tokens to every session and 670 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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