prompt-analytics

name	prompt-analytics
model	opus
effort	high
description	Analyze how prompts and skill routing behave across coding-agent sessions and produce calibrated recommendations — prompt-pattern analysis, routing accuracy, and knowledge gaps. Use when the user says "analyze my prompts", "prompt patterns", "is routing working", "which skill should have fired", "knowledge gaps", "what do I keep asking", or invokes /prompt-analytics. Do NOT use for auditing a single skill/agent definition (that is /skill-improver), tool/MCP efficiency (that is /tool-efficiency), or one-off interactive log queries (that is /session-analytics).
allowed-tools	Read, Agent, Bash

prompts

Analyze prompt and routing behavior across sessions, then produce calibrated recommendations. Judgment skill — scores findings with the shared confidence × severity model.

Input

A target framing for the analysis: a skill/router name for routing accuracy, a keyword/topic for knowledge gaps, or all for a broad prompt-pattern sweep. If unclear, ask. Optional harness filter (all default).

Owned domains

Three analytics packs under references/:

Domain	Pack	What it surfaces
prompt-analysis	prompt-analysis.md	Recurring user-prompt shapes, repeated asks, session openers
routing-accuracy	routing-accuracy.md	Did the right skill fire? (correlational — no ground truth)
knowledge-gaps	knowledge-gaps.md	Topics that recur without a resolving skill/tool (medium signal)

Protocol

1. Ingest — python3 ~/Dev/dotfiles/skills/session-analytics/scripts/ingest.py (1-hour TTL). Best-effort.

2. Fan out — spawn one parallel duckdb-expert per relevant domain (one-domain-per-spawn):

   spawn duckdb-expert "Run analytics pack prompt-analytics/references/<domain>.md for target {TARGET}. harness={HARNESS}"
   

3. Collect the digests.

4. Calibrate with ../session-analytics/references/calibration.md. Two of these domains are explicitly weaker signal — honor that: routing-accuracy has no intent ground-truth (correlational at best) and knowledge-gaps is medium-signal. Findings from them lean <speculative>; demote to <don't know> when the data is thin.

5. Report (below).

Report

## Prompt & Routing Report: {TARGET}

### Summary
- Target: <skill/topic/all>  ·  Harness: <filter>
- Domains run: <list>
- Findings: N surfaced, N below the bar

### Recommendations (surfaced)
| # | Severity | Confidence | Domain | Issue | Recommendation |
|---|----------|------------|--------|-------|----------------|

### Detail
For each surfaced finding: What / Why (with the metric) / How.

### Below the Bar
N findings were `<don't know>` or insufficient-signal (not shown).

What this skill never does

• Score with a 0-100 number — uses the shared qualitative model.
• Treat correlation as causation: routing-accuracy lacks ground truth and must say so.
• Run more than one domain per duckdb-expert spawn.
• Rewrite prompts or skill descriptions — it recommends; the human decides.

Gotchas

• routing-accuracy infers intent from what fired next; it cannot prove the right skill fired. Always tag its findings <speculative> at most.
• knowledge-gaps is medium-signal — a recurring topic isn't proof of a missing skill. Degrade to "insufficient signal" rather than over-claim.