Fpl Copilot logo

Fpl Copilot

Community
sugarforever
fpl-copilot

Use when the user asks about Fantasy Premier League player stats, transfer advice, captain picks, fixture difficulty, gameweek strategy, or saved squad management.

Overview

Publishersugarforever
Repository01coder-agent-skills
Skill namefpl-copilot
Stars
139
Forks
37
Bundled files
12
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.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Fpl Copilot 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/sugarforever/01coder-agent-skills/tree/main/skills/fpl-copilot
  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/sugarforever/01coder-agent-skills.git /tmp/01coder-agent-skills
mkdir -p .claude/skills
cp -r /tmp/01coder-agent-skills/skills/fpl-copilot .claude/skills/fpl-copilot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fpl Copilot 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 Fpl Copilot 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 Fpl Copilot 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.

FPL Copilot

Analyze current FPL data, maintain saved squads, and produce self-contained reports. This skill specifies operations and completion criteria; choose suitable tools or commands from the actual environment.

Workflow

1. Establish capabilities and storage

Inspect the available tools and locate this skill’s resources relative to its installed location. Identify capabilities for HTTP requests, JSON processing, SQLite 3 queries and writes, document persistence, and report delivery. On a shell-equipped host, infer suitable installed commands or scripting libraries; on other hosts, use exposed native tools. Tool names and executables are implementation choices.

Resolve storage once using the rule below, then reuse the resulting mapping for every reference and operation. Keep FPL data separate from the agent application's internal storage.

Storage resolution (in precedence order):

  1. Use an explicit user-selected FPL location or a host-provided binding for the stable namespace fpl-copilot. If these disagree, ask which to use before writing.
  2. Without an explicit selection or host binding, use the desktop root ~/.fplcopilot/, expanded to an absolute path through the current user's home directory. Reuse this root even when the working directory or conversation changes.
  3. On a host without a desktop home, request persistent storage for namespace fpl-copilot from its storage capability. Reopen its existing binding; if the host cannot provide a stable binding, ask the user to select a durable location before creating state. Conversation-local storage must be identified as such and selected explicitly.

Derive fplcopilot.db, squads/, reports/, and player-history-sync.json from that one root. If the host uses separate resource handles, persist their mapping under the same namespace through the host's storage configuration. Verify that mapping can be reopened before claiming cross-conversation persistence. Every mention of a “resolved” location in the references means this mapping, never a fresh location choice.

Inspect the selected location before initializing it. When competing existing mappings are discovered, ask which is authoritative; preserve existing data until the user selects a migration. Report the chosen root or handles and persistence scope when first establishing the mapping.

SQLite 3 is required for structured FPL storage; the sqlite3 executable is one possible interface, not a dependency. Use an available SQLite interface: the CLI, a scripting-language library, or a native primitive/tool backed by SQLite (such as Expo SQLite). Discover which interface is available and use it consistently. Initialize a new database from references/schema.sql; execute reference SQL through the selected interface, preserving the schema and calculation semantics. If a required capability is missing, identify the blocked operation and complete only the supported parts; distinguish unsaved output and unavailable live data explicitly.

Complete when: the agent has identified working operations and persistent locations for the requested task, or reported a specific missing capability.

2. Establish data freshness

Before data analysis, read references/sync.md and the relevant mappings in references/api.md. Inspect successful-refresh metadata and refresh missing or stale datasets according to that contract. Fetch only the player histories required for the requested calculation; a full-player batch requires an explicit user request.

Complete when: required datasets have successful refresh timestamps and verified persisted records, or the answer can identify the age and limitations of a usable cached snapshot. An unsuccessful refresh is not evidence of fresh data.

3. Load squads and analyze

For “my squad/team,” check saved squads before asking the user to share again. Read references/squad.md for selection, persistence, and validation. Read references/analysis.md for metrics and decision frameworks; use the documented formulas with the available query/calculation capabilities. Resolve player IDs by stored names, checking ambiguity before selecting a player.

Save newly shared squads and confirmed changes using the squad reference. Keep hypothetical transfers in draft plans; recording a change locally does not submit it to the user's FPL account. After squad analysis, persist key takeaways in Notes.

Complete when: recommendations are backed by retrieved data and documented calculations, and every required squad write is verified or explicitly reported as unsaved.

4. Deliver the result

Use Markdown for quick lookups, short reasoning, deadlines, and small results. For the spatial or comparative outputs below, read references/html-output.md and adapt the matching template with actual data.

OutputTemplate
Squad formation, bench, totalstemplates/squad-view.html
Fixture difficulty matrixtemplates/fixture-matrix.html
Transfer comparisontemplates/transfer-comparison.html
Captain ranking with reasoningtemplates/captain-ranking.html
Gameweek strategytemplates/gameweek-report.html

Create one self-contained HTML report with inline CSS, JavaScript, and SVG; use vanilla HTML/CSS/JS and responsive layouts. Store it under the resolved reports/ location as {YYYY-MM-DD}-{slug}.html. Provide the supported preview, attachment, share link, or accessible file path. Infer an appropriate viewing operation when available. When report creation or delivery is unavailable, provide the substantive result in chat and identify the missing report capability.

Complete when: the user has a substantive answer and an accessible report reference where supported, with data freshness and any limitations stated.

Reference routing

Read each resource before performing its operation:

ResourceOperation
references/sync.mdRefresh, normalize, persist, and verify FPL datasets
references/api.mdResolve endpoints and API-to-storage field mappings
references/schema.sqlInitialize storage or resolve tables, types, and keys
references/analysis.mdCompute metrics and recommendations
references/squad.mdLoad, validate, create, or update squads
references/html-output.mdGenerate and deliver reports

Data conventions

  • Resolve names against web_name, then partial display names and full names; clarify ambiguous matches.
  • Prices use 0.1m units: 130 means £13.0m. Display as £X.Xm; normalize by cost in millions for value comparisons.
  • Positions are GKP, DEF, MID, FWD, mapped from API codes 1–4.
  • Availability codes: a available, d doubtful, i injured, s suspended, u unavailable.
  • FDR runs from 1 (very easy) to 5 (very hard).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Fpl Copilot AI skill do?

Use when the user asks about Fantasy Premier League player stats, transfer advice, captain picks, fixture difficulty, gameweek strategy, or saved squad management.

Why use Fpl Copilot on TypingMind?

Because you install it once and use it with any model. Fpl Copilot 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 Fpl Copilot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sugarforever/01coder-agent-skills/tree/main/skills/fpl-copilot. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Fpl Copilot?

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 Fpl Copilot?

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

Is the Fpl Copilot AI skill free?

Yes. It is published on GitHub by sugarforever 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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