mirror of
https://github.com/hanzoai/insights.git
synced 2026-11-02 09:24:48 +00:00
zeekayandHanzo Dev
841ea1c0da
716 files rewritten, 30 renamed, same family mapping as the frontend pass.
Held back, and each for a reason rather than by oversight:
db_table values insights_hogfunction, insights_hogflow,
insights_hogfunctiontemplate, hogflow_templates.
Renaming a table is ALTER TABLE, so it ships as a
migration with the raw SQL in nodejs that names it.
hog_invocation_results a Datastore table with Kafka engine tables on it
products/review_hog the module path is the Django app path; moving it
moves app_label and every migration is recorded under
(app_label, name). That is the same history break as
renaming a migration file, one level up.
*/migrations/* five files, untouched
whogho an integration vendor
Checked: 395 of 397 changed Python files compile. The two that do not —
insightsql/database/database.py and temporal/schedule.py — fail identically at
origin/main under the same interpreter, because they use `type X = ...` and
`except*`, which need the 3.13 this repo pins and not the 3.9 doing the check.
Running the check against the original is what told those apart from a real
break.
Co-authored-by: Hanzo Dev <[email protected]>
AI observability skills
Agent skills for the AI observability product.
Built by insightscli build:skills and installed into sandbox containers for background agents.
Also available to Claude Code / Codex via insightscli sync:skill.
Skills
- exploring-llm-traces — how to query, inspect, and debug LLM traces via MCP tools.
Covers the
$ai_*event schema, where message content lives (eventsvs theai_eventstable), content detail levels, and step-by-step debugging workflows. - exploring-llm-clusters — how to investigate AI observability clustering results, compare cluster behavior, compute metrics, and drill into individual traces.
- exploring-llm-costs — how to investigate LLM spend: total cost, breakdowns by model/provider/user/trace/custom dimension, token and cache economics, cost regressions, and materializing cost insights, dashboards, and alerts.
- analyzing-expensive-users — how to analyze the most expensive users in AI observability, compare them against baseline usage, inspect trace/model/token/cache patterns, and explain what drives their spend.
- exploring-llm-evaluations — how to manage and investigate AI observability
evaluations (
script,llm_judge, andsentiment), run them on specific generations, query individual results, and generate AI-powered summaries of pass/fail/N/A patterns for boolean evaluations.
Skills for managing skills themselves (skills-store, working-with-skills) now live in
the standalone Skills product — see products/skills/skills/.
Adding a new skill
insightscli init:skill -- --product ai_observability --name my-new-skill
See products/insights_ai/scripts/build_skills.py for the build pipeline
and AGENTS.md for conventions.
Local testing
insightscli sync:skill -- --name exploring-llm-traces
This copies the built skill to .agents/skills/ for Claude Code to discover.