Generic Constructor-Pattern agent kit for Claude Code. Zero personal data, fully English, MIT-licensed. Contents: - 34 reusable blocks (baseline, rules, stack/deploy/domain/api/scraper) - 14 cross-project agent manifests (code/ml/infra/researcher/critic/...) - 6 portable skills (/new-agent, /research, /test-gen, /debug-deep, /pr-review, /refactor) - Rust assembler (single binary, ~500 KB) - 3 hooks (auto-reassemble, pre-commit validate, no-hand-edit) - install.sh (idempotent, cargo-builds on first run) - MIT LICENSE All 6 sanity greps pass: 0 Russian text, 0 specific project names, 0 incident numbers, 0 user paths, 0 hardcoded IPs, 0 API keys. cargo check + assemble --validate: both pass on 14 manifests. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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API — Anthropic (Claude)
Full text: Anthropic docs (WebFetch https://docs.anthropic.com/en/api before any new feature). Claude API skill trigger: code imports anthropic / @anthropic-ai/sdk.
Model IDs (from env, never hard-code):
- Opus tier — max effort, 1M input tokens on the
[1m]variant - Sonnet tier — balanced cost / capability
- Haiku tier — cheapest, latency-critical
- Keep ID in env var (
ANTHROPIC_MODEL) — swapping Opus→Sonnet should be 0 code changes.
Prompt caching (up to ~90% cost reduction + latency drop on cache hit):
- 4 cache breakpoints per request (
cache_control: {type: "ephemeral"}) - Two TTLs: default 5-min (cheap writes) and 1-hour (premium writes, higher $/token)
- Same prefix sent >N times → MUST
cache_control— missing caching on a long system prompt is free money left on the table - Log cache_read_input_tokens vs cache_creation_input_tokens every call — if read is zero across N calls, cache is mis-wired
Tool use:
- Fine-grained tool streaming supported (parse tool_use deltas, don't wait for full turn)
tool_choice: "auto" | "any" | {type: "tool", name}— pickanywhen you need some tool but don't care which- Cap turn loop with
max_iterations(default 10) — infinite loop on broken tool = infinite cost - Every tool_use MUST have matching tool_result — orphan tool_use errors mid-turn
Batch API: 50% discount, 24h window. Use for offline eval / bulk-ingest / non-interactive tasks. Polling via batch ID.
Extended thinking: thinking: {type: "enabled", budget_tokens: N}. Higher budget → deeper reasoning. Visible thinking is billed; hidden is not streamed but still billed.
Cost tracking (mandatory per-call log): input_tokens, output_tokens, cache_read_input_tokens, cache_creation_input_tokens → memory/{project}.md. Rates change — WebFetch https://www.anthropic.com/pricing before any budgeted run [VERIFY: live pricing page].
Forbidden: hard-coding model strings in source (use env var); using deprecated IDs without a migration note citing the replacement; sending the same >2K-token prefix >3 times without cache_control; skipping per-call cost log (no data → no decisions).