Bitterbot is a local-first personal AI agent that runs on a user’s devices, keeps persistent memories, performs tasks, and can exchange reusable skills with other agents. It is intended for people who want a personal assistant that remains available across conversations and activities. The catalogue entries provide instructions and agents for working with Bitterbot.
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 Bitterbot-AI/bitterbot-desktop --skill weathergit clone --depth 1 https://github.com/Bitterbot-AI/bitterbot-desktopWrote 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/bitterbot-ai/bitterbot-desktop/weather)<a href="https://agentmods.dev/skills/bitterbot-ai/bitterbot-desktop/weather"><img src="https://agentmods.dev/badge/skills/bitterbot-ai/bitterbot-desktop/weather/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/bitterbot-ai/bitterbot-desktop/weather"><img src="https://agentmods.dev/badge/skills/bitterbot-ai/bitterbot-desktop/weather.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 | $0.00013 | $0.00398 |
| Opus 5 | $0.00006 | $0.00199 |
| Sonnet 5 | $0.00003 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
weather scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
metadata: { "bitterbot": { "emoji": "🌤️", "requires": { "bins": ["curl"] } } } This is a copy
92% identical to weather — 8 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.
What it actually says
Weather
Two free services, no API keys needed.
wttr.in (primary)
Quick one-liner:
curl -s "wttr.in/London?format=3"
# Output: London: ⛅️ +8°C
Compact format:
curl -s "wttr.in/London?format=%l:+%c+%t+%h+%w"
# Output: London: ⛅️ +8°C 71% ↙5km/h
Full forecast:
curl -s "wttr.in/London?T"
Format codes: %c condition · %t temp · %h humidity · %w wind · %l location · %m moon
Tips:
- URL-encode spaces:
wttr.in/New+York - Airport codes:
wttr.in/JFK - Units:
?m(metric)?u(USCS) - Today only:
?1· Current only:?0 - PNG:
curl -s "wttr.in/Berlin.png" -o /tmp/weather.png
Open-Meteo (fallback, JSON)
Free, no key, good for programmatic use:
curl -s "https://api.open-meteo.com/v1/forecast?latitude=51.5&longitude=-0.12¤t_weather=true"
Find coordinates for a city, then query. Returns JSON with temp, windspeed, weathercode.
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.
- 8d ago First seen · 55 lines · 13 tokens per session scan A 41a9a9ac7337
weather is a skill published in the GitHub repository Bitterbot-AI/bitterbot-desktop (2,462 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 398 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to weather, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
selfhost-emem-guard
Stand up an emem-guard verdict server, verify it against the conformance checks, and point any agent at it. Use when asked to self-host emem-guard, add a grounding gate to an agent on any model or framework, wire a checkpoint into Claude Code or Claude Enterprise or MCP, or run a signed allow/deny server for…
emem-field-tokens
Get a native-resolution raster field over an area from emem, or a field over time, as a signed, verifiable artifact rather than a set of per-cell scalars. Use when the user needs the actual grid of values over an area of interest (a world model input, an NDVI/band drape, change analysis over a scene window, exportable…
emem-a2a-collaboration
Join the agent-to-agent collaboration running on emem's signed ledger — find the standard, verify another agent's message offline (who wrote it, not just that it was stored), announce yourself, and hand facts to other agents as tokens. Use when the user wants agents to coordinate without a shared database or shared…
emem-find-similar
Given a place name or cell64, return the top-K most similar places on Earth by cosine similarity over the 128-D Tessera foundation embedding. Use when the user asks for analogues, look-alikes, or counterparts ("find cities like Bangalore", "where else looks like the Sundarbans", "show me places with a similar urban…
emem-locate-and-recall
Resolve a free-form place name to an emem cell64 and recall signed Earth-observation facts at that location. Use when the user asks about current weather, vegetation index, elevation, soil properties, or any other geospatial measurement at a named place ("what's the temperature in Bengaluru", "how high is Denali"…
emem-recall-polygon
Recall signed Earth-observation facts at every cell inside a user-supplied polygon. Use when the user asks about an extent rather than a point — "what's the average NDVI inside this watershed", "show me precipitation across the Western Ghats", "what's the elevation profile of this region". Accepts a polygon as [lng…