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codex-session-benchmark-maintainer

Benchmark a Codex session against a local cohort using proxy metrics.

280|30|Updated Mar 30, 2026
Search Tags:#benchmarking#proxy metrics#failure classification#token cost#workflow evaluation#codex sessions#follow-up analysis
One-click install
npx skills add https://github.com/Undertone0809/rudder --skill codex-session-benchmark-maintainer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codex-session-benchmark-maintainer
Source: https://github.com/Undertone0809/rudder/tree/main/.agents/skills/maintainer/codex-session-benchmark-maintainer
Command: npx skills add https://github.com/Undertone0809/rudder --skill codex-session-benchmark-maintainer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you evaluate how efficiently and effectively a specific Codex session performed by benchmarking it against a recent local cohort and turning log evidence into actionable workflow guidance.

Core Features & Use Cases

  • Target-vs-cohort proxy benchmarking: Compares a named Codex session against a deduped recent window to estimate efficiency, rework, follow-up, interruptions, and handoff quality without assuming ground-truth success.
  • Evidence-first metric extraction: Computes or labels metrics as direct, derived, proxy, or missing using local JSONL/SQLite sources, including validation/commit/PR handoff indicators when present.
  • Reusable failure-class outcomes: Classifies the session outcome (e.g., clean handoff, completed with rework, partial/blocked, diagnosis only, ambiguous) and maps underperformance to reusable workflow failure classes, then recommends next skill/workflow changes (often by handing off to skill-optimizer).

Example use cases:

  • Assess whether a target Codex session (given by id or prefix) was notably worse or better than the surrounding 30–100 session cohort for the same Rudder development work context.
  • Diagnose whether a session’s low effectiveness came from wrong skill routing, weak source-of-truth checks, validation gaps, wrong worktree/runtime, tooling/permission blockers, or unclear user-intent extraction.

Quick Start

Compare the Codex session id 019e3f7c-d54b-71b3-ba08-2ee20ce6be27 against the most recent 100 local Rudder-related Codex sessions and report which proxy metrics and failure classes explain any performance gap.

Frequently Asked Questions about codex-session-benchmark-maintainer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark a Codex session against previous sessions for proxy metrics?▼

To benchmark a Codex session, compare the target session ID against a deduped recent local cohort to evaluate efficiency, rework, follow-up, interruptions, and handoff quality using evidence-first proxy metrics.

What are defensible proxy metrics for evaluating AI coding session workflow performance?▼

Defensible proxy metrics include efficiency, follow-up frequency, interruption counts, validation depth, and handoff completeness, extracted from local JSONL or SQLite sources with explicit missing-field labeling.

How to classify Codex session failures into reusable workflow categories?▼

Classify Codex session failures by mapping underperformance to reusable workflow failure classes like wrong skill routing, validation gaps, wrong worktree, tooling blockers, or unclear user-intent extraction.

Can I compare token cost and validation depth across multiple Codex sessions?▼

Yes, you can compare token cost and validation depth across selected task categories by benchmarking a target session against a local cohort window of 30 to 100 sessions using local log data.

Does Codex session benchmarking require ground-truth success labels to work?▼

No, Codex session benchmarking does not assume ground-truth success, instead relying on cautious outcome classification and evidence-first proxy metric extraction from local session logs.

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