Initial skills documentation — 25 categories, all SKILL.md + references + scripts

2026-07-15 17:42:20 -04:00
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+# LiteLLM Spend & Cost Analysis Queries
+
+When investigating high model costs or checking the effective price of models routed through `admin-ai`, you can query the `LiteLLM_SpendLogs` table directly on the AI server (178.156.167.181). 
+
+*Note: Column names in this table use camelCase (e.g., `"startTime"`), which require double quotes in Postgres.*
+
+## 1. Effective Cost per 1 Million Tokens (Last 72 Hours)
+
+This query groups by model and provider, calculates the actual effective cost per 1M tokens based on recorded spend, and filters out trivial usage (<10k tokens). This is the best way to compare real-world pricing for models like `deepseek-v4-pro` vs `deepseek-chat` to make cost-effective primary model recommendations.
+
+```bash
+ssh -i /root/.ssh/itpp-infra root@178.156.167.181 \
+  "docker exec litellm_postgres psql -U litellm -d litellm_db -At -F '|' -c \"
+SELECT 
+  COALESCE(model_group,'') as model, 
+  COALESCE(custom_llm_provider,'') as provider, 
+  COUNT(*) as calls, 
+  ROUND(SUM(spend)::numeric,6) as total_spend_usd, 
+  SUM(total_tokens) as tokens, 
+  ROUND((SUM(spend)/NULLIF(SUM(total_tokens),0)*1000000)::numeric,6) AS effective_usd_per_1M_tokens
+FROM \\\"LiteLLM_SpendLogs\\\" 
+WHERE \\\"startTime\\\" >= NOW() - INTERVAL '72 hours' AND model_group <> '' 
+GROUP BY model_group, custom_llm_provider 
+HAVING SUM(total_tokens) > 10000 
+ORDER BY effective_usd_per_1M_tokens ASC NULLS LAST;\""
+```
+
+## 2. Daily Spend by User & Session (Context Size Investigation)
+
+High API costs are often driven by enormous prompt contexts sent repeatedly. This query finds the exact sessions and models burning the most spend today.
+
+```bash
+ssh -i /root/.ssh/itpp-infra root@178.156.167.181 \
+  "docker exec litellm_postgres psql -U litellm -d litellm_db -At -F '|' -c \"
+SELECT 
+  COALESCE(session_id,'') as session, 
+  COALESCE(\\\"user\\\",'') as \\\"user\\\", 
+  model_group,
+  COUNT(*) as calls, 
+  ROUND(SUM(spend)::numeric,4) as spend, 
+  SUM(prompt_tokens) as prompt_tokens, 
+  SUM(completion_tokens) as completion_tokens
+FROM \\\"LiteLLM_SpendLogs\\\" 
+WHERE \\\"startTime\\\" >= DATE_TRUNC('day', NOW())
+GROUP BY session_id, \\\"user\\\", model_group 
+ORDER BY SUM(spend) DESC NULLS LAST LIMIT 20;\""
+```
+
+If you see a session averaging 250,000+ `prompt_tokens` per call, the agent's conversation history has grown too large for expensive models like `gpt-5.5` or `claude-sonnet-4-6`. The immediate fix is to start a `/new` session to drop context, or swap the primary model to a cheaper alternative.