version: "1.0.1" name: alpha-review description: >- Open the alpha review screen to mark judge keepers as gold or miss, read per-miner precision, and print the eval state. Use when tuning a miner's accuracy. Thin launcher over the existing harvest engine and local review server. Triggers: "/alpha-review", "alpha screen", "open the review screen". license: MIT

/alpha-review — the alpha selection screen in one command

The engine is already built (2026-06-18/20). This skill is launch + summary; it duplicates nothing.

Steps

1. Is the server alive? netstat -ano | findstr :8772 (PowerShell) / netstat -ano | grep :8772 (bash).

• Listening → jump to step 3.

1. Start it (a fresh harvest runs inside):

`` cd /e/Obsidian/_imports/alpha && PYTHONIOENCODING=utf-8 python alpha_review_server.py --no-browser ` in the background (run_in_background). Manual alternative for the operator: double-click $IMPORTS_ROOT/alpha/alpha-review.cmd`.

1. Hand over the link: http://127.0.0.1:8772 — open in a browser (local only, nothing leaves the machine).
2. Eval summary (0 LLM tokens):

`` PYTHONIOENCODING=utf-8 python $IMPORTS_ROOT/alpha/alpha_tune.py `` Show the operator: how much is labelled / per-miner precision / what to label first (uncertainty sampling: PARTIAL first). At >=8 labels per miner, tune names a concrete detector fix.

1. Remind them of the loop: labels -> alpha_tune.py -> adjust the detector threshold/filter -> re-harvest -> re-label. Cards carry a batch badge (🆕 = fresh nightly batch).

Pitfalls

• The DB is a cumulative INBOX of every nightly batch (not just the latest judged file) — "extra" items are not junk, they are an unlabelled backlog. Do not "fix" it.
• An empty screen is not the same as no data: first check that the harvest actually ran (alpha_harvest.py prints counters) and that you are looking at the right drive (E:, not C:).
• 🔒 Community-sourced cards are HIGH sensitivity: never screenshot the screen outside, and approach contacts value-first only (standing rule for elite crypto communities: zero cold DMs, value first).

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Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).

Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_

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