| 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
-
Ingest — python3 ~/Dev/dotfiles/skills/session-analytics/scripts/ingest.py
(1-hour TTL). Best-effort.
-
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}"
-
Collect the digests.
-
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.
-
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.
