np-core-suggestions-review

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name: np-core-suggestions-review description: Triage the nervepack performance dashboard's accumulated evaluator suggestions — rank the open ones, decide which to implement (with reasoning), then clear the slate so new suggestions accumulate fresh. Use when the user says "review nervepack suggestions", "evaluate the evaluator/dashboard suggestions", "which suggestions should we build", "clear the suggestions", "/np-suggestions", or after a stretch of sessions when the Suggestions panel has filled up. Tool-agnostic: works from any host (Claude Code, Goose, a terminal); the dashboard's served-mode buttons are the GUI shortcut for the same flow.

Reviewing nervepack evaluator suggestions

The performance evaluator appends suggestions[] to every session's metrics record. They pile up in dashboard/data/metrics.jsonl and surface on the dashboard's Suggestions panel. This skill turns "look at the pile, decide what's worth building, wipe it" into a repeatable loop. The deterministic ranking/clearing is a script; the judgment (which to implement) is yours.

The engine (deterministic, no model)

engine/setup/np-suggestions-review.py — reuses the dashboard's own normalization so its view of "open" matches what the dashboard shows.

• List the open, deduped, confidence-ranked suggestions:

  python3 ~/Code/nervepack/engine/setup/np-suggestions-review.py list --top 10
  python3 ~/Code/nervepack/engine/setup/np-suggestions-review.py list --json   # for machine use
  

  "Open" = present in metrics.jsonl AND not already in dashboard/data/resolved-suggestions.txt. Dedup is by normalized text, keeping the max confidence and an occurrence count (recurrence signal).
• Clear — resolve every open suggestion (default ALL) so the panel resets and new ones accumulate; rebuilds metrics.js so the dashboard reflects it immediately:

  python3 ~/Code/nervepack/engine/setup/np-suggestions-review.py clear
  

  Clearing is non-destructive: it appends the texts to the resolved ledger (the metrics records are untouched), so the audit trail survives.

The workflow (what to actually do)

1. List the top-N (default 10) open suggestions.
2. Evaluate each with a one-line verdict — implement or skip — and a brief reason. Judge on: is it real and still true? is it actionable and in-scope for nervepack? does it duplicate an existing skill/feature? what's the cost/benefit? Prefer auto_safe + recurring (×N) + high-confidence items. Cross-check against docs/ARCHITECTURE.md (don't recommend something already built) and docs/ROADMAP.md (something deliberately deferred should stay deferred unless its trigger fired).
3. Present the verdicts to the user — a short table: rank · confidence · target · verdict · reason. Call out the few worth doing now.
4. On the user's approval, clear — run … clear to reset the slate (the user chose "clear all reviewed"; implemented items become real work tracked elsewhere, the rest are dismissed). Never clear without confirmation.
5. Optionally, turn an approved "implement" item into actual work (a plan, a commit) or fold a durable decision into a skill via [[np-core-contribute]].

The GUI shortcut (served dashboard)

When evaluator.dashboard_serve is on (a param, default on — flip off with nervepack-toggle evaluator.dashboard_serve off), the dashboard is served from a local backend (engine/setup/np-dashboard-server.py) and its Suggestions panel gains buttons: Resolve (one item → np-suggestion-resolve.sh), Review (a single Haiku verdict pass over the top-N, via np-llm.sh), and Review & clear (resolve all open). Same engine underneath — the buttons just call /api/resolve|review|clear.

Notes

• The ranking script makes no model call (harness language policy). The only model use is the GUI Review button's single cheap Haiku classify pass, and it degrades gracefully (shows the ranking with no verdicts) if the seam is unavailable.
• "Clear" defaults to ALL open; pass --top N to clear only the top-N.
• Touches the evaluator/dashboard feature — see docs/ARCHITECTURE.md "Dashboard" row.