Anycap Worldcup Predict

AnyCap World Cup Predict

> Read this entire file before starting. It defines a reliable workflow for turning a player name (or a whole squad) into structured, verifiable World Cup pre-match intel using AnyCap, then optionally calling a winner and scoreline for fun.

World Cup match prediction and pre-match intel for serious football fans. Turn player names and lineups into structured, verifiable profiles, stats, and matchup briefings so you can read a World Cup matchup before kickoff -- then, if you want, finish with a for-fun prediction (likely winner + scoreline + confidence). Built for World Cup squads and national-team lineups, and works for any footballer.

This skill is about how to retrieve and verify player data. For CLI command reference (flags, output, jq), read the anycap-cli skill and its references/search.md / references/crawl.md.

> This skill surfaces public information (profiles, form, historical stats) and can finish with an optional for-fun prediction (likely winner + a playful scoreline) derived from that data. It does not set betting odds, output win probabilities as if they were facts, or place wagers. Every prediction must ship with the disclaimer in [predict.md](references/predict.md). Treat everything as informational and form your own judgment.

Prerequisites

• anycap CLI installed and authenticated (anycap status to verify). Read the anycap-cli skill if setup is needed.
• jq for parsing JSON output.

Core Principle: Search Less, Not Faster

Do not crawl the whole web for every player. Narrow the search space first, then retrieve only what matters:

1. Grounding first -- ask for a structured profile in one shot (anycap search --prompt). This is the fastest path to clean data.
2. Disambiguate -- when a name is shared by several players, add context (nationality, position, birth year, club) to lock onto the right one.
3. Verify only when accuracy matters -- cross-check against an authoritative, crawlable source (Wikipedia) instead of trusting a single answer.
4. Prune, don't dump -- in batch mode, fetch one structured profile per player; never crawl every search result.

Workflow

| Goal | Route | Reference | |------|-------|-----------| | One player's basic info | Grounding structured JSON | [lookup.md](references/lookup.md) | | Right player when name is shared | Add context / pre-scan with --query | [lookup.md](references/lookup.md) | | High-accuracy, sourced profile | Cross-check vs Wikipedia | [verify.md](references/verify.md) | | A whole squad / roster | Loop grounding, aggregate to table | [batch.md](references/batch.md) | | Career + current World Cup stats | Grounding stats JSON (with caveats) | [stats.md](references/stats.md) | | Size up a matchup (Team A vs Team B) | Build both squads via batch, compare side by side | [batch.md](references/batch.md) | | Add past World Cup context | Format, group + knockout results, key players, team history, head-to-head | [history.md](references/history.md) | | Call a winner + scoreline (for fun) | Weigh the gathered layers (plus any user-defined dimensions), then predict with a mandatory disclaimer | [predict.md](references/predict.md) |

For a matchup, run the squad workflow for both teams, then layer in historical context (tournament track record + head-to-head from [history.md](references/history.md)). Present three layers side by side -- squad quality, current form, historical context -- and highlight differences in key positions and availability. Then, if a prediction is wanted, finish with an optional for-fun call (likely winner + playful scoreline + confidence) per [predict.md](references/predict.md). Always keep the factual layers clearly separated from the prediction, and always attach the disclaimer.

Fast path (single player)

anycap search --prompt 'Give the basic profile of footballer Jude Bellingham as JSON with fields: full_name, date_of_birth, nationality, height, position, current_club, shirt_number, national_team. Only the JSON.' \
  | jq -r '.data.content'

Returns (verified):

{
  "full_name": "Jude Victor William Bellingham",
  "date_of_birth": "2003-06-29",
  "nationality": "English",
  "height": "1.86 m",
  "position": "Midfielder",
  "current_club": "Real Madrid",
  "shirt_number": 5,
  "national_team": "England"
}

Grounding search costs 5 credits per call. General search (--query) and crawl cost 1 credit each.

Output Schema

Normalize every profile to this schema so single-player and batch results are consistent:

| Field | Type | Notes | |-------|------|-------| | full_name | string | Legal/full name | | date_of_birth | string | YYYY-MM-DD; use null if unknown | | nationality | string | Primary nationality | | height | string | e.g. 1.86 m; null if unknown | | position | string | e.g. Midfielder, Goalkeeper | | current_club | string | Club as of retrieval date | | shirt_number | int/null | Club or national-team number | | national_team | string | National team |

Rules:

• Use null for missing fields. Never invent values.
• current_club and shirt_number change frequently -- treat them as point-in-time and note the retrieval date.
• When a field cannot be confirmed, mark it and (if accuracy matters) verify via [verify.md](references/verify.md).

Important: Source Reliability (verified)

• Wikipedia is reliably crawlable (anycap crawl https://en.wikipedia.org/wiki/ returns full Markdown). Use it as the primary verification source.
• Transfermarkt blocks crawling -- anycap crawl returns a short Human Verification page, not data. Do not rely on crawling it; use grounding search or Wikipedia instead.
• Other stats sites may also bot-block. Always check the crawled title/length before trusting content.

Prediction (for fun)

After the factual layers, you may add an optional, clearly-labeled prediction so the briefing is fun to read. See [predict.md](references/predict.md) for the method and the required disclaimer. Non-negotiable rules:

• Facts first, prediction last. Never blend the guess into the data tables; put it in its own clearly-marked block.
• Always attach the disclaimer from [predict.md](references/predict.md). A prediction without the disclaimer is incomplete output.
• Entertainment, not betting. Do not present win probabilities or odds as facts, do not give wagering advice, and do not tell anyone to bet.
• Show the reasoning. Base the call on the gathered signals (ranking, form, head-to-head, key players), not a coin flip.
• Let the user steer. Users can add, drop, or re-weight prediction dimensions (e.g. home advantage, injuries, rest, weather, "vibes"). Honor their weights, retrieve any missing data first, and list the final dimension set used. See [predict.md](references/predict.md).

Limitations

• Live data (current club, shirt number, latest stats) reflects whatever the web returns at call time and can lag real events.
• Grounding stats numbers are best-effort and can be approximate; verify critical figures against Wikipedia.
• Generic player names need disambiguation context or the wrong person may be returned.
• This skill retrieves data on demand; it is not a stored database of all players.

Quick Reference

| Tool | Purpose | |------|---------| | anycap search --prompt "...JSON..." | Structured profile / stats in one shot (5 cr) | | anycap search --query "..." --no-crawl | Find/disambiguate the right player page (1 cr) | | anycap crawl https://en.wikipedia.org/wiki/ | Authoritative verification source (1 cr) | | anycap status | Check auth before a batch run |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: anycap-ai
• Source: anycap-ai/anycap
• License: MIT
• Homepage: https://anycap.ai

Install and usage instructions live in the source repository linked above.