context-intelligence-evaluation-methodology

name	context-intelligence-evaluation-methodology
version	1.0.0
description	Use when deciding how to measure a context-intelligence tool signal — metric design across quality/efficiency/efficacy axes, artifact-metric avoidance via precursor measurement, A/B and statistical-N discipline, and test-data fidelity.
user-invocable	false
allowed-tools	read_file, glob, grep, delegate, load_skill, todo
model_role	reasoning
license	MIT

Context Intelligence Evaluation Methodology

Mode-only skill for how to measure. It complements — and never restates — context-intelligence-eval-design (which owns scenario mechanics and the two-layer structural/behavioral structure) and digital-twin-universe (which owns the DTU machinery).

Scope

In scope

• Metric design. Choose metrics across three axes: quality (did it detect what the user means?), efficiency (token/tool cost to detect), efficacy (does detection drive the right outcome?).
• Measure the precursor, not only the failure. Prefer leading indicators (e.g. bounded vs climbing context growth) over lagging ones (e.g. a final timeout). The precursor is testable in a short window; the full failure often is not.
• A/B + statistical-N discipline. A single green run is not proof of a behavioral change. Compare a control arm against a treatment arm; require N independent trials and report the pass rate, not a single anecdote.
• Test data fidelity. Validate on real sessions where available, or on faithfully modeled synthetic corpora that preserve the distribution that matters (e.g. heavy-tail file sizes), never on data hand-shaped to pass.

Out of scope (point elsewhere — do not restate)

• Scenario mechanics, the two-layer structure, DTU profile templates / Gitea URL rewrite → context-intelligence-eval-design.
• DTU lifecycle, launch/exec/profiles → the digital-twin-universe skill.
• "DTU-as-default" rationale and the "artifact-as-success" anti-pattern → already authoritative in context-intelligence-eval-design and context-intelligence:context/context-intelligence-primitives-reference.md. Reference them; do not repeat them.

Method

1. Name the user-meaningful outcome the metric must reflect (from domain-concepts.md), not what a detector happens to emit.
2. Pick the cheapest axis that discriminates. If a deterministic efficiency signal (token growth, tool-call count) separates good from bad behavior, prefer it over an LLM-judged quality rubric.
3. Identify the precursor. Ask: what climbs before the failure? Measure that in a bounded window.
4. Design the A/B. Control = current behavior; treatment = the change. Hold everything else identical. Decide N and the pass threshold before running.
5. Choose the corpus. Real sessions if available; otherwise synthetic that preserves the load-bearing distribution. State the fidelity assumption explicitly.

Apply (do not restate) the event-semantics principle: when a metric depends on what an event means, consult the authoritative ecosystem expert agents for that event's semantics — the principle is named once in context-intelligence:context/context-intelligence-strategy.md. Do not restate it here.