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 agentmods add rules/seanlf/weather-mcp/root-problem-identificationgit clone --depth 1 https://github.com/SeanLF/weather-mcpWhat 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 | $0.00372 | $0.00372 |
| Opus 5 | $0.00186 | $0.00186 |
| Sonnet 5 | $0.00074 | $0.00074 |
| Haiku 4.5 | $0.00037 | $0.00037 |
Grade A, and why
root-problem-identification scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
filters:
- type: event pattern: "ai_suggestion_request"
- type: content pattern: "(?i)(fix|implement|change|update|create)"
actions:
-
type: transform pattern: "^.*$" replacement: |
Problem Analysis
Business Context
{infer_business_context_from_request}
Underlying Problem
{extract_root_problem}
Requirements
{explicit_and_implicit_requirements}
Implementation Plan
{original_content}
-
type: suggest message: | Before implementing a solution:
- Identify the underlying business problem
- Clarify any unstated requirements
- Consider alternative approaches
- Choose the solution with highest business value and lowest complexity
examples:
-
input: | Can you optimize this database query? It's running slowly. output: |
Problem Analysis
Business Context
This query appears to be affecting application performance, potentially impacting user experience or system reliability.
Underlying Problem
The database query is performing inefficiently, likely due to missing indexes, suboptimal join conditions, or retrieving unnecessary data.
Requirements
- Improve query performance
- Maintain exact same result set
- No schema changes unless absolutely necessary
Implementation Plan
Let me analyze this query to find optimization opportunities...
metadata: priority: critical version: 1.0
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.
- 2d ago First seen · 70 lines · 372 tokens per session scan A 5910ab8e2654
root-problem-identification is a cursor rule published in the GitHub repository SeanLF/weather-mcp (2 stars, last pushed 1y ago), licensed MIT. It adds 372 tokens to every session, about $0.0019 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 2026-08-31.
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