acis2llm 0.3.0

Agent-friendly helpers for the xmACIS2Py climate library — station discovery, multi-station fetch, and seasonal/monthly composites. Ships with the acis-weather skill.

acis2llm

Agent-friendly helpers for the xmACIS2Py climate library — plus the acis-weather skill that teaches an LLM how to use them.

acis2llm adds three things on top of xmacis2py:

1. Station discovery — resolve "10001" (ZIP) / "KNYC" (station ID) / "4600 Silver Hill Rd, Washington, DC" (street address) → ACIS station ID
2. Multi-station fetch — comma-aggregate ("KNYC,KJFK") and plus-backfill ("KNYC+OLDER") syntax
3. Composite analyses — seasonal/monthly aggregates and threshold frequencies, computed across many years in one call

The bulk of the value lives in skills/acis-weather/ — a complete agent skill with an agentskills.io-compliant SKILL.md and 5 reference docs covering xmACIS2Py + the helpers + worked recipes.

---

Install

This project uses uv. Install it first if you don't have it.

uv pip install xmacis2py acis2llm

Or for one-off scripts without managing a venv:

uv run --with xmacis2py --with acis2llm python your_script.py

Requires Python 3.10+ and network access to data.rcc-acis.org, geocoding.geo.census.gov, and api.zippopotam.us. No API keys.

---

Use the skill (the agent path)

If you're driving an LLM agent (Claude Code, Cursor, Gemini CLI, OpenCode, etc.), point it at the skill and it will know what to do:

# Copy or symlink skills/acis-weather into your client's skills directory.
# For Claude Code:
ln -s "$PWD/skills/acis-weather" ~/.claude/skills/acis-weather

# For project-scoped use, drop it under .claude/skills/ in your repo:
ln -s "$(pwd)/skills/acis-weather" /path/to/project/.claude/skills/acis-weather

The skill is a single SKILL.md (under 500 lines) plus references/ files the agent loads on demand. See the agentskills.io spec for the format.

---

Use the library directly (the human path)

import xmacis2py
import acis2llm

# 1. Resolve a ZIP / station ID / street address → station metadata
stn = acis2llm.find_best_station("80202")
# {'station_id': 'KDEN', 'name': 'DENVER INTL ARPT, CO', 'data_start': 1948, ...}
# Note: "City, State" alone does not resolve — pass a ZIP, a station ID
# (e.g. "KDEN"), or a full street address.

# 2. Single-station fetch
df = xmacis2py.get_single_station_acis_data(
    stn["station_id"],
    start_date="2023-06-01",
    end_date="2023-08-31",
)

# 3. Multi-station aggregate (comma) or backfill (plus)
df = acis2llm.fetch_stations(
    "KNYC,KBOS,KORD",
    start_date="2024-06-01",
    end_date="2024-06-30",
)

# 4. Cross-year composite — "snowiest winters in Buffalo"
result = acis2llm.seasonal_summary(
    station="KBUF",
    parameter="snow",
    season="winter",
    aggregation="sum",
)
top5 = sorted(result["table"], key=lambda r: r["value"] or 0, reverse=True)[:5]

# 5. Threshold frequency — "100°F+ days each summer at Phoenix"
result = acis2llm.frequency_of_occurrence(
    station="KPHX",
    parameter="tmax",
    threshold=100,
    comparison=">=",          # also accepts "at_or_above"
    season="summer",
    start_year=2015,
    end_year=2023,
)

For the full API surface see skills/acis-weather/references/acis2llm-api.md. For end-to-end recipes see skills/acis-weather/references/recipes.md.

---

Variables

Short	Full xmACIS2Py column	Unit
tmax	Maximum Temperature	°F
tmin	Minimum Temperature	°F
tavg	Average Temperature	°F
tdpa	Average Temperature Departure	°F
prcp	Precipitation	inches
snow	Snowfall	inches
snow_depth	Snow Depth	inches
hdd / cdd / gdd	Heating / Cooling / Growing Degree Days	base 65/65/32°F
awdb	Average Daily Water Balance	inches

acis2llm's composite functions accept short codes; xmACIS2Py's analysis functions take the full column names. acis2llm.VARIABLE_COLUMN_MAP exports the mapping.

---

Development

uv sync --extra dev
uv run pytest

---

Iterating on the skill

We improve acis-weather by driving real LLM agents through real weather queries and capturing what breaks. After a session that exercised the skill, paste the contents of docs/field-reports/DEBRIEF-PROMPT.md into the same chat. The agent will self-debrief and write either a one-line entry to docs/field-reports/log.md or a full structured report file alongside it. Works in any host (Claude Code, opencode, Gemini CLI, pi, etc.) — it's just a pasteable prompt.

The agent never edits the skill or the library; reports are triage input. To act on a report, either fix it directly or kick off a spec under docs/superpowers/specs/, then rename the report file with a RESOLVED- prefix to keep the historical trail.

---

Layout

.
├── src/acis2llm/
│   ├── __init__.py             public API re-exports
│   ├── geocoding.py            find_best_station, geocode_census, is_zip_code
│   ├── composites.py           seasonal_summary, monthly_totals_by_year, ...
│   └── multi_station.py        fetch_stations (comma + plus syntax)
├── skills/acis-weather/
│   ├── SKILL.md                agent-facing playbook (agentskills.io v1)
│   └── references/             vendored xmACIS2Py docs + recipes + API ref
└── tests/
    ├── test_composites.py
    └── test_multi_station.py

---

Credits

• Data: Regional Climate Centers via the Applied Climate Information System.
• Engine: xmACIS2Py by Eric J. Drewitz (@edrewitz) — MIT-licensed. Documentation in skills/acis-weather/references/xmacis2py-*.md is vendored from upstream and reformatted; original copyright preserved.

License: MIT — see LICENSE.