Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add dreamiurg/claude-mountaineering-skills/plugin install mountaineeringWrote 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/commands/dreamiurg/claude-mountaineering-skills/conditions)<a href="https://agentmods.dev/commands/dreamiurg/claude-mountaineering-skills/conditions"><img src="https://agentmods.dev/badge/commands/dreamiurg/claude-mountaineering-skills/conditions/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/commands/dreamiurg/claude-mountaineering-skills/conditions"><img src="https://agentmods.dev/badge/commands/dreamiurg/claude-mountaineering-skills/conditions.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 | $0.00021 | $0.01212 |
| Opus 5 | $0.00010 | $0.00606 |
| Sonnet 5 | $0.00004 | $0.00242 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
conditions 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 12d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick Conditions Check
Fetch current weather, air quality, daylight, and avalanche conditions for a mountain peak. Much faster than a full route research -- no web scraping or agent dispatch needed.
If the user provided a peak name as an argument (e.g., /mountaineering:conditions Mt Baker), use that as the target peak. Otherwise, ask which peak to check.
Phase 1: Peak Identification
-
Search PeakBagger for the peak:
uvx --from "git+https://github.com/dreamiurg/[email protected]" peakbagger peak search "{peak_name}" --format json -
Handle results:
- Multiple matches: Use AskUserQuestion to present options. For each: "[Name] ([Elevation], [Location]) - [PeakBagger URL]". Include "Other" option.
- Single match: Confirm with user: "Found: [Name] ([Elevation], [Location]) - [URL]. Is this correct?"
- No matches: Try variations (Mt/Mount, word order reversal, remove titles). If still nothing, ask user for clarification.
-
Extract peak_id from the selected result.
Phase 2: Peak Details
Fetch peak coordinates and elevation:
uvx --from "git+https://github.com/dreamiurg/[email protected]" peakbagger peak show {peak_id} --format json
Extract: latitude, longitude, elevation_m (elevation in meters), peak_name.
Phase 3: Fetch Conditions
Run the conditions fetcher script:
cd ${CLAUDE_PLUGIN_ROOT}/skills/route-researcher/tools && uv run python fetch_conditions.py \
--coordinates "{latitude},{longitude}" \
--elevation {elevation_m} \
--peak-name "{peak_name}" \
--peak-id {peak_id}
# Optional enrichment flags:
# --trailhead "lat,lon" multi-county path sampling (trailhead→summit)
# --distance-mi N --gain-ft N enables time_estimates
# --start-time HH:MM enables itinerary (requires distance+gain)
# --waypoint "lat,lon" ... enables bearings (2+ waypoints)
This returns JSON with weather, air_quality, daylight, avalanche, peakbagger, counties, nearest_hospital, ranger_station, campgrounds, and (when --distance-mi/--gain-ft provided) time_estimates sections.
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.
- 12d ago First seen · 82 lines · 21 tokens per session scan A 113e68ee4160
conditions is a command published in the GitHub repository dreamiurg/claude-mountaineering-skills (33 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,212 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
learn
Force claude-smart to extract learnings from this session now.
prototype
You are building a proof-of-concept for the current Grainulator sprint. Read CLAUDE.md for sprint context and claims.json for existing research claims.
init
Install the formatters this repository needs, with every command visible before it runs.
brand-generate
Generate an on-brand document from a saved Brand Profile.
genshijin-compress
A command for safely shortening Markdown or text files into the genshijin style.
check-dev
Type-check a Z specification with fuzz.