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 StarryCod/cogitum --skill mapsgit clone --depth 1 https://github.com/StarryCod/cogitumWrote 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/starrycod/cogitum/maps)<a href="https://agentmods.dev/skills/starrycod/cogitum/maps"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/maps/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/starrycod/cogitum/maps"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/maps.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 | $0.00020 | $0.01769 |
| Opus 5.5 | $0.00008 | $0.00708 |
| Sonnet 5.5 | $0.00004 | $0.00354 |
| Haiku 4.5 | $0.00002 | $0.00177 |
Grade A, and why
maps 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 1mo 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
98% identical to maps — 48 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maps Skill
Location intelligence using free, open data sources. 8 commands, 44 POI categories, zero dependencies (Python stdlib only), no API key required.
Data sources: OpenStreetMap/Nominatim, Overpass API, OSRM, TimeAPI.io.
This skill supersedes the old find-nearby skill — all of find-nearby's
functionality is covered by the nearby command below, with the same
--near "<place>" shortcut and multi-category support.
When to Use
- User sends a Telegram location pin (latitude/longitude in the message) →
nearby - User wants coordinates for a place name →
search - User has coordinates and wants the address →
reverse - User asks for nearby restaurants, hospitals, pharmacies, hotels, etc. →
nearby - User wants driving/walking/cycling distance or travel time →
distance - User wants turn-by-turn directions between two places →
directions - User wants timezone information for a location →
timezone - User wants to search for POIs within a geographic area →
area+bbox
Prerequisites
Python 3.8+ (stdlib only — no pip installs needed).
Script path: ~/.config/cogitum/skills/maps/scripts/maps_client.py
Commands
MAPS=~/.config/cogitum/skills/maps/scripts/maps_client.py
search — Geocode a place name
python3 $MAPS search "Eiffel Tower"
python3 $MAPS search "1600 Pennsylvania Ave, Washington DC"
Returns: lat, lon, display name, type, bounding box, importance score.
reverse — Coordinates to address
python3 $MAPS reverse 48.8584 2.2945
Returns: full address breakdown (street, city, state, country, postcode).
nearby — Find places by category
# By coordinates (from a Telegram location pin, for example)
python3 $MAPS nearby 48.8584 2.2945 restaurant --limit 10
python3 $MAPS nearby 40.7128 -74.0060 hospital --radius 2000
# By address / city / zip / landmark — --near auto-geocodes
python3 $MAPS nearby --near "Times Square, New York" --category cafe
python3 $MAPS nearby --near "90210" --category pharmacy
# Multiple categories merged into one query
python3 $MAPS nearby --near "downtown austin" --category restaurant --category bar --limit 10
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.
- 1mo ago First seen · 196 lines · 20 tokens per session scan A 0fc59314ac5a
maps is a skill published in the GitHub repository StarryCod/cogitum (12 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 1,769 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to maps, differing in 48 lines, and is treated as a copy.
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