How do I install the Alpha Review skill?

Run npx skillmds@latest add tonydzi/alpha-review in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Alpha Review safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Alpha Review?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Alpha Review free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Alpha Review?

tonydzi (@tonydzi) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Alpha Review by tonydzi — Agent Skill

Name: Alpha Review
Author: tonydzi

Alpha Review

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".

tonydzi 9eaa328 3.2 KB Updated Sep 17, 2026

File contents

/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

Is the server alive? netstat -ano | findstr :8772 (PowerShell) / netstat -ano | grep :8772 (bash).
- Listening → jump to step 3.
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.
Hand over the link: http://127.0.0.1:8772 — open in a browser (local only, nothing leaves the machine).
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.
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).

Like this skill? It is one of 100 in second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.

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_

Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.

---name: alpha-reviewdescription: 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 commandThe engine is already built (2026-06-18/20). This skill is launch + summary; it duplicates nothing.## Steps1. **Is the server alive?** `netstat -ano | findstr :8772` (PowerShell) / `netstat -ano | grep :8772` (bash). - Listening → jump to step 3.2. **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`.3. **Hand over the link**: http://127.0.0.1:8772 — open in a browser (local only, nothing leaves the machine).4. **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.5. **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).------**Like this skill?** It is one of 100 in [second-brain-starter-kit](https://github.com/tonydzi/second-brain-starter-kit): the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with `npx skills add tonydzi/second-brain-starter-kit`. Everything is open source and free, so take what you need.Flagships worth a look on their own: [secondop-panel](https://github.com/tonydzi/secondop-panel) (a second opinion from a panel of external models), [claude-memory-tidy](https://github.com/tonydzi/claude-memory-tidy) (stop your agent's memory from rotting), [telegram-mcp-kit](https://github.com/tonydzi/telegram-mcp-kit) (your own Telegram over MCP in about 15 minutes).Author: **Anton Dziatkovskii**, Palo Alto AI Research Lab. Telegram [@tonydzi](https://t.me/tonydzi) - WhatsApp [+1 341 222 9178](https://wa.me/13412229178) - X [@Tony_Stef_](https://x.com/Tony_Stef_)**Engineers: want to test-drive this setup?** Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.

Frequently asked questions