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Picks Tracker

Organization
aeonfun
picks-tracker

Retrospective on past token and prediction market picks - what hit, what flopped, what the score is

Overview

Publisheraeonfun
Repositoryaeon
Skill namepicks-tracker
Stars
771
Forks
280
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aeonfun on GitHub. Read the source before you install it.

Installation

Install the Picks Tracker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aeonfun/aeon/tree/main/skills/picks-tracker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/picks-tracker .claude/skills/picks-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Picks Tracker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Picks Tracker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Picks Tracker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Today is ${today}. Your task is to audit the last 30 days of token picks and score them against current prices.

Read memory/MEMORY.md for context.

Network note

curl works — there is no network sandbox. For every curl call, if it fails or returns empty/error, use WebFetch for the same URL as a fallback for a flaky public GET.

Steps

1. Extract token picks from logs

Scan the last 30 days of memory/logs/ for token picks. For each log file, grep for lines matching **Token:**. Extract:

  • Symbol (e.g., XMR, HYPE, TAO)
  • Date (from the filename, e.g., 2026-04-19)
  • Pick price (the number after $ on the same line, ignoring ~ approximations)

Also extract prediction market picks from lines matching **Market:**:

  • The question text (in quotes)
  • The position taken (YES or NO)
  • The price/odds at pick time

Expected log format (written by token-pick and monitor-polymarket):

- **Token:** XMR — $350.52 (+1.17% 24h, +2.55% 7d)
- **Market:** "US x Iran permanent peace deal by April 30?" — NO $0.615 (YES 38.5%)

Focus on the last 30 days of logs. If a token was picked multiple times, record each instance separately.

If zero picks are found in the window, log PICKS_TRACKER_SKIP: no picks in last 30 days — enable token-pick / monitor-polymarket and stop (no notification).

2. Fetch current token prices

For each unique token symbol, fetch the current price from CoinGecko.

First, try the search endpoint to get the coin ID. The optional demo key rides ./secretcurl's {COINGECKO_API_KEY} placeholder (never a bare $SECRET on the line); the header is only sent when a key is set:

bash
CG_HDR=(); [ -n "${COINGECKO_API_KEY:+x}" ] && CG_HDR=(-H "x-cg-demo-api-key: {COINGECKO_API_KEY}")
./secretcurl -s "${CG_HDR[@]}" "https://api.coingecko.com/api/v3/search?query=SYMBOL"

Then fetch the price:

bash
./secretcurl -s "${CG_HDR[@]}" "https://api.coingecko.com/api/v3/simple/price?ids=COIN_ID&vs_currencies=usd&include_24hr_change=true"

Fallback: If curl fails, use WebFetch for the same URL (drop the API key header).

Common ID mappings (use directly, skip search step):

  • HYPE → hyperliquid
  • XMR → monero
  • TAO → bittensor
  • ENJ → enjincoin
  • DASH → dash
  • ORDI → ordinals
  • MORPHO → morpho
  • RENDER → render-token
  • JUP → jupiter-exchange-solana
  • KAS → kaspa
  • ENA → ethena
  • ALGO → algorand
  • APT → aptos
  • FET → fetch-ai
  • ZEC → zcash
  • ETHFI → ether-fi

For tokens not in the list, use the search endpoint. If a symbol is ambiguous or the token isn't found, mark as N/A (not found).

3. Calculate performance

For each pick:

performance % = ((current_price - pick_price) / pick_price) * 100

Round to 1 decimal place. Use a ~ prefix if the pick price was approximate.

If the same token was picked multiple times, calculate performance from each individual pick date.

Classify each pick:

  • 🟢 Win — +10% or better
  • 🟡 Hold — between -10% and +10%
  • 🔴 Loss — -10% or worse

4. Check prediction market pick resolution

For each prediction market pick, search the Polymarket API to check if the market resolved:

bash
# Search by question text
curl -s "https://gamma-api.polymarket.com/markets?closed=true&limit=10&q=KEYWORDS_FROM_QUESTION"

If WebFetch is needed: https://gamma-api.polymarket.com/markets?closed=true&limit=10&q=KEYWORDS_FROM_QUESTION

For each market pick:

  • If resolved: was the position correct? Mark ✅ (correct) or ❌ (wrong) or ⏳ (still open)
  • Note the resolution price/outcome if available

Markets with no Polymarket equivalent (internal or experimental markets) — mark as ⏳ or skip.

5. Score summary

Tally the token picks:

  • Total picks in window
  • Wins / Holds / Losses count
  • Average return across all picks (equally weighted)
  • Best pick (highest % gain)
  • Worst pick (biggest % loss)
  • Hit rate: (wins / total_picks) * 100

Keep it honest. No cherry-picking dates.

6. Format and send notification

Send via ./notify (inline multi-line literal — do NOT pipe or use $(cat)):

*picks scorecard — [START_DATE] → [TODAY]*

*token picks ([N] total)*
[SYMBOL] [DATE] — picked $[PICK] → now $[CURRENT] ([PERF]%) [EMOJI]
...sorted best to worst...

*score: [WINS]W [HOLDS]H [LOSSES]L | avg [AVG]% | hit rate [HIT_RATE]%*
*best: [SYMBOL] +[BEST]% | worst: [SYMBOL] [WORST]%*

*market picks*
[QUESTION_SNIPPET] — [POSITION] [STATUS_EMOJI]
...

no financial advice. just tracking the record.

read it: https://github.com/aeonfun/aeon/blob/main/output/articles/picks-scorecard-${today}.md

Keep the message under 3000 chars. If too long, truncate to the most recent 10 picks.

7. Save scorecard

Write a brief scorecard to output/articles/picks-scorecard-${today}.md:

markdown
# Picks Scorecard — [DATE]

## Token Performance
| Symbol | Picked | Pick Price | Current | Change | Result |
|--------|--------|-----------|---------|--------|--------|
...

## Summary
- Window: last 30 days
- Total picks: N
- Wins / Holds / Losses: X / Y / Z
- Average return: X%
- Hit rate: X%

## Market Picks
| Question | Position | Status |
...

8. Log to memory

Append to memory/logs/${today}.md:

### picks-tracker
- **Window:** last 30 days (N picks)
- **Score:** [WINS]W [HOLDS]H [LOSSES]L | avg [AVG]% | hit rate [HIT_RATE]%
- **Best:** [SYMBOL] +[BEST]%
- **Worst:** [SYMBOL] [WORST]%
- **Notification sent:** yes
- PICKS_TRACKER_OK

Environment Variables

  • COINGECKO_API_KEY — optional, increases rate limits. Skill works without it via free tier + WebFetch fallback.

Frequently asked questions

What does the Picks Tracker AI skill do?

Retrospective on past token and prediction market picks - what hit, what flopped, what the score is

Why use Picks Tracker on TypingMind?

Because you install it once and use it with any model. Picks Tracker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Picks Tracker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aeonfun/aeon/tree/main/skills/picks-tracker. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Picks Tracker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Picks Tracker?

As many as you like. As long as a model supports skills, you can use Picks Tracker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Picks Tracker AI skill free?

Yes. It is published on GitHub by aeonfun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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