Claude API

First-party harness for building, debugging, and optimizing applications on the Claude API / Anthropic SDK. Owns model routing, prompt caching (on by default), tool-use design, batch/streaming patterns, and version-migration playbooks. Apps built or touched through this skill ship prompt caching from the first commit and carry token-ceiling guardrails per phase.

This is the Claude-in-the-loop counterpart to the rest of the pipeline. If you’re building an LLM feature — a chatbot, an extraction pipeline, an agent, an LLM judge — this skill owns the request layer: which model, how it’s cached, how tools are wired, and how the code moves when a model version bumps. It does not own agent topology or harness loops (that’s oc-agent-forge), retrieval (oc-rag-forge), or prompt versioning + eval datasets (oc-prompt-ops). It owns the Claude API surface those skills build on.

Accuracy contract. Model IDs, prices, parameters, and breaking changes in this skill and its reference docs are sourced from the bundled claude-api skill, not from memory. When in doubt about a model ID, a price, a beta header, or a deprecated parameter, invoke claude-api (or read its shared/ files) rather than inventing one. The most recent models are Fable 5 (claude-fable-5), Opus 4.8 (claude-opus-4-8), Sonnet 4.6 (claude-sonnet-4-6), and Haiku 4.5 (claude-haiku-4-5).

How This Skill Fits the Build Pipeline

APP-ARCHITECT (planning)                 TRI-DEV (building)
  Phase 2: Spec ──"AI app?"──▶ oc-claude-api model-routing decision tree
                                         │  writes 05-llm-design.md
  Phase 5: Scaffold ──auto-calls──▶ oc-claude-api request-layer scaffold
                                         │  (caching + token ceilings baked in)
                                         ▼
                          oc-agent-forge (topology/loop) ──reads model routing──┐
                          oc-rag-forge   (retrieval)      ──reads model routing──┤
                          oc-prompt-ops  (eval/versioning)──reads model routing──┘

App-architect auto-invokes oc-claude-api in Phase 2 when the discovery interview flags an AI app — triggers are “AI app”, “agent”, “chatbot”, “LLM in the loop”, “summarize/extract/classify with a model”, “Claude”, “Anthropic”. The decision tree runs, picks the per-phase model, and records the request-layer plan in 05-llm-design.md. The user doesn’t call /oc-claude-api separately for new projects — but they can invoke it directly to migrate, audit caching, design tools, or set cost guardrails on existing code.

Model routing is owned here and read by siblings. oc-agent-forge decides subagent topology and tool budgets but reads this skill’s per-task model choice; oc-rag-forge reads it for the answer-synthesis model; oc-prompt-ops reads it to pin the model its eval datasets run against.

/oc-claude-api — Command Reference

CLAUDE API COMMANDS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  BUILD / DEBUG
  /oc-claude-api          Design or debug a Claude API integration
  /oc-claude-api tool-use Schema-first tool-use patterns (defer-load, parallel)

  OPTIMIZE
  /oc-claude-api cache-audit  Audit prompt-cache hit rate (target ≥ 60%)
  /oc-claude-api cost         Token-ceiling + per-phase cost guardrails

  LIFECYCLE
  /oc-claude-api migrate  Migrate code across model versions → diff PR
  /checkpoint             Show checkpoint status

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Type any command to begin. /oc-claude-api to see this again.

Model-Routing Decision Tree

The first decision on any Claude API task is which model per phase. Default toward capability, drop to cheaper tiers only when the task is genuinely simple or high-volume. Read references/model-routing.md for the full table; the shape:

What is this call doing?
│
├─ Spec authoring, architecture, code audit, migration, hard reasoning,
│  long-horizon agentic work
│     └─▶ claude-opus-4-8         (the default for intelligence-sensitive work)
│        └─ most demanding / overnight autonomous runs → claude-fable-5
│
├─ Build, reverse-spec, codegen, balanced agentic loops, tool-heavy work
│     └─▶ claude-sonnet-4-6       (best speed/intelligence balance)
│
└─ Cheap repetitive phases: classification, routing, extraction, label-gen,
   high-volume summarization
      └─▶ claude-haiku-4-5        (fastest, cheapest; 200K context)

Routing principles

• Don’t downgrade silently for cost. The model choice is the caller’s; default to claude-opus-4-8 for anything intelligence-sensitive and surface a cheaper option, don’t impose it.
• One model per cached prefix. Caches are model-scoped — switching models mid-conversation invalidates the cache. For a cheaper sub-task, spawn a subagent on the cheaper model (Haiku) rather than swapping the main loop’s model.
• Use exact model ID strings, never date-suffixed aliases (claude-sonnet-4-6, not claude-sonnet-4-6-20251114). Pull IDs from references/model-routing.md.
• Effort over a thinking budget. On 4.6+ models, control depth with output_config.effort (low→max; xhigh on Opus 4.7/4.8) and adaptive thinking (thinking: {type: "adaptive"}). budget_tokens is removed on Fable 5 / Opus 4.8 / 4.7 (400) and deprecated on 4.6.

Prompt Caching Is the Default

Every integration this skill builds ships prompt caching from the first commit — not as a later optimization pass. Prompt caching is a prefix match: any byte change anywhere in the prefix invalidates everything after it. Get the prompt assembly order right (stable content first, volatile content last) and most caching works for free.

What to cache, in render order (tools → system → messages):

• Tool definitions — deterministic, sorted by name, never reshuffled per request.
• System prompt — frozen. No datetime.now(), no per-user IDs, no conditional sections interpolated into it. Inject dynamic context later in messages.
• Large shared context — retrieved docs, few-shot examples, big preambles — breakpoint at the end of the shared span, before the varying question.

cache_control: {type: "ephemeral"} gives a 5-minute TTL (ttl: "1h" for bursty traffic). Max 4 breakpoints per request. Verify with usage.cache_read_input_tokens — zero across repeated identical-prefix requests means a silent invalidator is at work. Full placement patterns, cost math, and the silent-invalidator audit checklist live in references/prompt-caching.md.

/oc-claude-api cache-audit — target ≥ 60% hit rate

cache-audit traces the prompt-assembly path, classifies every input by stability, checks rendered order matches stability order, and reports the cache hit rate against a ≥ 60% target. Output:

## Prompt Cache Audit

### Hit rate
- cache_read / (cache_read + cache_creation + input): 41%  ← BELOW 60% TARGET

### Silent invalidators found
| Location | Pattern | Fix |
|---|---|---|
| system prompt header | `f"Current date: {datetime.now()}"` | Move to a user-turn message |
| tools=build_tools(user) | tool set varies per user | Freeze + sort deterministically |

### Breakpoint placement
- system block: present, but invalidated upstream by the date interpolation
- Recommended: 1 breakpoint on last (frozen) system block; 1 on last shared-context block

### Verdict: FAIL — fix the two invalidators, re-audit

Tool-Use Patterns

Three patterns, schema-first throughout. Full code shapes in references/tool-use.md.

1. Schema-first design. Write the JSON Schema (or Zod / Pydantic) first; the tool description is prescriptive about when to call, not just what it does (“Call this when the user asks about current prices”). Recent Opus models reach for tools conservatively — trigger conditions in the description give measurable lift. Use strict: true on the tool definition (with additionalProperties: false + required) to guarantee inputs validate.
2. Deferred-load (tool search). With a large tool library, mark tools defer_loading: true and add a tool_search_tool_regex_20251119 / _bm25_20251119 tool. Claude searches and loads only the relevant schemas. Schemas are appended, not swapped — the prompt cache survives. Never defer every tool (400) — the search tool and at least one real tool stay loaded.
3. Parallel tool calls. One assistant message may carry multiple tool_use blocks. Execute concurrently and return all tool_result blocks in a single user message — splitting them across messages silently trains Claude to stop calling tools in parallel. A failed tool returns tool_result with is_error: true — don’t drop it.

Prefer the SDK’s tool runner (client.beta.messages.tool_runner / toolRunner) for the loop unless you need human-in-the-loop approval, custom logging, or conditional execution — then use the manual agentic loop.

Migration Playbooks — /oc-claude-api migrate

Moves existing Claude API code across model versions (4.6 → 4.7 → 4.8, Mythos Preview → Fable 5) and replaces retired models. Produces a diff PR, not a silent in-place rewrite. Full per-target breaking-change checklists in references/migration-playbooks.md.

The flow is fixed:

1. Confirm scope first. If the user didn’t name a specific file, directory, or file list, ask before editing — “migrate my codebase” / “upgrade to Sonnet 4.6” is ambiguous (what, not where). Never start editing on an ambiguous scope.
2. Classify each file. Not every file with the old ID is an API caller — model registries, capability gates, and test fixtures need different handling (add alongside vs. swap vs. regenerate). See the classification table in the playbook.
3. Apply the per-target breaking-change checklist. [BLOCKS] items (e.g. budget_tokens → adaptive thinking, strip temperature/top_p/top_k, remove last-assistant-turn prefills) are code edits; [TUNE] items (effort, prompt re-tuning) are recommendations.
4. Explain every edit, especially system-prompt changes, tied to the specific API or behavioral shift that motivates it.
5. Eval-gate the rollout. Hand the diff to oc-prompt-ops for a regression run against the golden set before merge, then open the PR via oc-git-ops.
6. Verify. One test request, assert response.model.startswith(target).

Cost Guardrails — /oc-claude-api cost

Sets and enforces token ceilings per phase so an LLM feature can’t run away with spend. Levers, cheapest-first:

• Right-size max_tokens. Non-streaming default ~16000 (under SDK HTTP timeouts); streaming default ~64000; classification ~256. Lowballing truncates mid-thought and forces a retry — the opposite of cheap.
• Prompt caching. Cache reads cost ~0.1× input price; a ≥ 60% hit rate is the single biggest cost lever on repeated-context workloads.
• Per-phase model floor. Route classification/extraction to Haiku, balanced work to Sonnet, reserve Opus/Fable for the phases that need them.
• Task Budgets (beta, Fable 5 / Opus 4.8 / 4.7) for agentic loops — the model sees a running countdown and self-moderates. Distinct from max_tokens, which is an enforced ceiling the model doesn’t see.
• Batch API for non-latency-sensitive jobs — 50% of standard price.

Deeper cost ops land in v1.6. Cross-project spend dashboards, per-route cost budgets, and live cost-regression alerts route through oc-cost-ops once it ships. Until then, this command sets static per-phase ceilings and surfaces them in the checkpoint; it does not yet track live spend.

Cross-Skill Integration

Boundary: this skill owns the Claude API request surface — model choice, caching, tool wiring, streaming/batch, migration. It does not own agent topology, retrieval, prompt eval, or third-party auth. Siblings own those and build on the request layer this skill defines.

Checkpoint Integration

Checkpoint Location

{project-dir}/.checkpoints/oc-claude-api.checkpoint.json

When to Write

skill_state

{
  "model_routing": {
    "spec": "claude-opus-4-8",
    "build": "claude-sonnet-4-6",
    "classify": "claude-haiku-4-5"
  },
  "caching": { "breakpoints": 2, "hit_rate": 0.71, "ttl": "5m" },
  "tools": { "count": 6, "strict": true, "defer_load": false, "parallel": true },
  "last_migration": {
    "from": "claude-opus-4-7",
    "to": "claude-opus-4-8",
    "files": 4,
    "blocks_applied": 1,
    "pr": "https://github.com/org/repo/pull/123"
  },
  "cost": { "max_tokens_default": 16000, "batch": false, "task_budget": null }
}

Principles

1. Prompt caching is not optional. Every integration ships caching from the first commit. A frozen system prompt and deterministic tool order are architectural decisions, not afterthoughts.
2. One model per cached prefix. Switching models mid-conversation throws the cache away. Spawn a cheaper subagent instead of swapping the main loop’s model.
3. Default to capability; downgrade is the caller’s call. Use claude-opus-4-8 for intelligence-sensitive work and surface cheaper options — never silently route to Haiku to save money.
4. Schema-first tools, prescriptive descriptions. Write the schema first; tell the model when to call, not just what the tool does. Parse tool inputs as JSON, never raw-string-match.
5. Model IDs and prices come from claude-api, never memory. Stale priors are the most common bug — verify against the bundled skill.
6. Migrations are diffs, not rewrites. Confirm scope, classify each file, apply the breaking-change checklist, eval-gate, explain every edit, then open a PR.
7. Right-size max_tokens. Lowballing truncates and forces retries; the cheap path is the correctly-sized one plus caching, not a starved ceiling.
8. Handle stop_reason before reading content. refusal, pause_turn, max_tokens, and model_context_window_exceeded are real branches — reading content[0] unconditionally breaks on refused or paused turns.

Reference docs

• references/model-routing.md — per-task/phase model selection with a decision table
• references/prompt-caching.md — cache_control usage, what to cache, TTL, hit-rate math
• references/tool-use.md — schema-first design, tool_choice, parallel calls, defer-load
• references/migration-playbooks.md — step-by-step model-version migration → diff PR