Communitygithub.com

ZepiGit/league-api-engineering

Evidence-led, Vanguard-policy-safe engineering guidance Skill for League of Legends data integrations. Build compliant tools better.

league-api-engineering 是什麼?

league-api-engineering is a Claude Code agent skill that evidence-led, Vanguard-policy-safe engineering guidance Skill for League of Legends data integrations. Build compliant tools better.

相容平台~Claude Code~Codex CLI~Cursor
npx skills add ZepiGit/league-api-engineering

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

League API Engineering

Select exactly one primary mode. For a mixed request, state the dominant mode and the secondary deliverable before proceeding.

ModeUse forRequired result
implementAdding or changing a data integrationSource decision → adapter contract → sanitized fixtures/tests → implementation plan or code → gates
auditAssessing an existing integrationInventory → evidence labels → risk matrix → prioritized, source-specific fixes
debugA failing or misleading integrationReproduction → failure class → transport/schema/cache/fallback inspection → fix proof
researchChoosing a source or checking current factsDated evidence map → source assessment → decision
policyWhether data use or product behavior is allowedProduct/data review → policy gate → permitted, constrained, or blocked decision

Non-negotiable gates

Reject a design rather than work around a failed gate:

  • Secrets and privacy: never embed, persist, or log Riot keys, LCU credentials, bearer tokens, cookies, PII, or real player histories.
  • Local-client safety: no injection, hooks, process-memory access, memory scanning, anti-cheat bypass, hidden-state extraction, or claims of exclusive-fullscreen control. Loopback certificate relaxation is limited to a client explicitly bound to 127.0.0.1 or localhost; it is never reusable for remote HTTPS.
  • Authority and terms: do not represent an undocumented endpoint or provider as officially supported. Verify applicable terms, attribution, redistribution, authentication, and rate-limit conditions before use.
  • Fallback truthfulness: a fallback must preserve population, scope, freshness, patch, locale, queue, and provenance semantics. Materially different data is a separate result, not a silent substitute.
  • Unsupported requests: do not invent MMR/ELO estimates, infer hidden state, or expose unavailable player information. Return policy_blocked or unsupported_context with the constraint.

Source-decision workflow

  1. Classify the requested data: local session, active match, static/versioned data, remote account/match/ranked data, aggregate trend data, or unsupported/hidden state.
  2. Start with source-decision-tree.md; choose candidates by authority, access, routing, freshness, stability, patch coverage, latency, terms, privacy, attribution, rate limits, and failure behavior.
  3. Apply hard gates before scoring. A source needing an embedded production key, unsafe local access, prohibited data, unreviewed redistribution, or an incompatible fallback is rejected.
  4. Record an evidence class for each material claim:
    • verified-current: directly supported by a dated authoritative source or current reproducible observation;
    • observed-undocumented: reproduced behavior not guaranteed by an authority;
    • historical: formerly documented or observed, not safe to assume current;
    • inference: a reasoned conclusion that needs validation before production.
  5. Write or update an adapter contract using adapter-contract.md, then adopt cache/fallback, testing, and canary controls.

Failure vocabulary

Use one primary error class and retain raw sanitized diagnostics separately: offline, unauthorized, rate_limited, not_found, no_data, unsupported_context, transport, http_status, parse, schema_drift, or policy_blocked.

Never blindly retry unauthorized, policy_blocked, parse, or schema_drift. See caching-and-fallbacks.md.

Audit output contract

An audit emits a dated artifact with:

  1. scope, data classes, and adapter inventory;
  2. source/endpoint map with authority, stability, evidence class, and terms-review date;
  3. credential, privacy, local-access, routing, versioning, and attribution findings;
  4. cache, no-data, retry, drift, and fallback compatibility findings;
  5. a risk matrix: severity, likelihood, affected behavior, evidence, and concrete remediation;
  6. prioritized fixes, blocked items, and verification criteria.

Do not label an assumption as verified. Do not call a source safe merely because it works once.

Progressive disclosure

Run python scripts/validate.py . from the package root before publishing when the validator is present.

相關技能