Use llms.txt to Orient a Football AI Coding Workflow

Use llms.txt as a map, not the territory

A concise llms.txt file can tell a coding agent what the product is, where the canonical OpenAPI document lives, how authentication works, which capabilities are available, and which safety rules matter. This reduces the chance that the first prompt begins from generic training data or a similarly named API.

The file should point to authoritative artifacts rather than restating every field. OpenAPI remains the machine-readable contract for operations, parameters, and schemas. Human documentation explains behavior and examples. Coverage pages describe real datasets. If those layers conflict, the agent should stop and surface the mismatch instead of selecting whichever version makes its code easier.

Give every coding agent the same reading sequence

Begin with llms.txt, follow its canonical links, pin the OpenAPI revision, and write a short implementation brief before code. The brief should name required operations, data fields, errors, secret location, pagination, cache behavior, and acceptance tests. This makes handoffs between ChatGPT, Codex, Claude Code, Cursor, or another tool much easier to review.

After generation, compare the implementation with the brief and run contract tests. Store the brief in the repository so a later agent can understand why the integration exposes only a subset of operations. A well-maintained context file reduces repeated discovery, but it never removes the need to inspect the current contract before a material change.

/llms.txt
  → /openapi.json
  → /docs/authentication
  → /docs/errors
  → /coverage
  → implementation brief
  → tests
  → code

A handoff your agent can actually follow

Treat an AI agent as a planner and transformer, not as the database. Give it a narrow task, the exact resources it may call, the response fields it may quote, and a stop condition for missing data. Keep bearer credentials in the server-side tool implementation rather than in the prompt, transcript, browser, or generated source file.

The handoff below is deliberately operational. It asks for evidence before prose, makes uncertainty visible, and keeps the model inside the current football API contract. Adapt the output format to your product, but preserve the rules about stable IDs, UTC timestamps, freshness, and error handling.

Read /llms.txt for scope, authentication, availability, and links.
Then read /openapi.json as the authoritative operation and schema contract.
List any conflict instead of choosing silently.
Generate code only for currently available operations and add contract tests.

What the human reviewer still owns

Automation can verify schemas and repeatable checks, but publication and product decisions still need a person. Review the selected competition, season, team, and match IDs; confirm that the time window matches the user’s question; and read the final answer against the retrieved JSON. A fluent explanation is not evidence that the underlying call was correct.

For time-sensitive football AI, record when the source was ingested and when the agent retrieved it. If the workflow cannot establish those timestamps, qualify the result instead of presenting it as current. The same rule applies to unavailable capabilities: do not quietly substitute fixtures or results for lineups, player statistics, odds, expected goals, injuries, or live events.

• Confirm every quoted fact appears in the retained API response.
• Exercise the empty, 401, 404, 429, and 503 paths before launch.
• Keep model interpretation separate from source facts in logs and user-facing output.
• Confirm the agent did not turn a prose roadmap item in llms.txt or a linked page into an executable endpoint.

Continue with the contract, not a guess

Start with the public contract and coverage ledger, then move into implementation only when the capability you need is marked available. The related guide gives your next agent-first pattern without requiring an undocumented endpoint.