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 braxtonROSE4/zorro-agent --skill find-nearbygit clone --depth 1 https://github.com/braxtonROSE4/zorro-agentWrote 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/braxtonrose4/zorro-agent/find-nearby)<a href="https://agentmods.dev/skills/braxtonrose4/zorro-agent/find-nearby"><img src="https://agentmods.dev/badge/skills/braxtonrose4/zorro-agent/find-nearby/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/braxtonrose4/zorro-agent/find-nearby"><img src="https://agentmods.dev/badge/skills/braxtonrose4/zorro-agent/find-nearby.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 | $0.00045 | $0.00799 |
| Opus 5 | $0.00023 | $0.00400 |
| Sonnet 5 | $0.00009 | $0.00160 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
find-nearby 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 7d 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.
This is a copy
95% identical to find-nearby — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Find Nearby — Local Place Discovery
Find restaurants, cafes, bars, pharmacies, and other places near any location. Uses OpenStreetMap (free, no API keys). Works with:
- Coordinates from Telegram location pins (latitude/longitude in conversation)
- Addresses ("near 123 Main St, Springfield")
- Cities ("restaurants in downtown Austin")
- Zip codes ("pharmacies near 90210")
- Landmarks ("cafes near Times Square")
Quick Reference
# By coordinates (from Telegram location pin or user-provided)
python3 SKILL_DIR/scripts/find_nearby.py --lat <LAT> --lon <LON> --type restaurant --radius 1500
# By address, city, or landmark (auto-geocoded)
python3 SKILL_DIR/scripts/find_nearby.py --near "Times Square, New York" --type cafe
# Multiple place types
python3 SKILL_DIR/scripts/find_nearby.py --near "downtown austin" --type restaurant --type bar --limit 10
# JSON output
python3 SKILL_DIR/scripts/find_nearby.py --near "90210" --type pharmacy --json
Parameters
| Flag | Description | Default |
|---|---|---|
--lat, --lon |
Exact coordinates | — |
--near |
Address, city, zip, or landmark (geocoded) | — |
--type |
Place type (repeatable for multiple) | restaurant |
--radius |
Search radius in meters | 1500 |
--limit |
Max results | 15 |
--json |
Machine-readable JSON output | off |
Common Place Types
restaurant, cafe, bar, pub, fast_food, pharmacy, hospital, bank, atm, fuel, parking, supermarket, convenience, hotel
Workflow
-
Get the location. Look for coordinates (
latitude: ... / longitude: ...) from a Telegram pin, or ask the user for an address/city/zip. -
Ask for preferences (only if not already stated): place type, how far they're willing to go, any specifics (cuisine, "open now", etc.).
-
Run the script with appropriate flags. Use
--jsonif you need to process results programmatically. -
Present results with names, distances, and Google Maps links. If the user asked about hours or "open now," check the
hoursfield in results — if missing or unclear, verify withweb_search.
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.
- 7d ago First seen · 70 lines · 45 tokens per session scan A fbd482eae4fe
find-nearby is a skill published in the GitHub repository braxtonROSE4/zorro-agent (8 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 799 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to find-nearby, differing in 2 lines, and is treated as a copy.
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