API builder
- When to use it
- Skills for teams building on the Anthropic API — prompt caching, token budgeting, eval generation for skills, and tool-use schema writing.
- Covers
- Build on the Claude API (4), Build and review software (1)
- Where you decide
- No gated loop of its own; the skills run one at a time.
- Size
- 4 skills · 0 loops
30–60 minutes a session
npx skilldrop-cli install --pack api-builder
Installs 4 skills.
Other ways to install
In Claude Code, as a plugin (after /plugin marketplace add sananthanarayan/skilldrop):
/plugin install api-builder@skilldrop
Into Cursor, Kiro or another IDE:
npx skilldrop-cli install --pack api-builder --ide cursor
Start here: Find where prompt caching will cut your input costs
Paste this into your agent:
Here is our system prompt and a typical request: <paste them>. Where should the cache_control breakpoints go, and what hit rate should we expect?
- Before you start
- A system prompt and a typical request from your Claude API integration
- How to tell it worked
- prompt-caching-advisor marks where each cache_control checkpoint goes and estimates the cache hit rate.
- If nothing happens
- If prompt-caching-advisor does not activate, ask for it by name and check that its folder exists in your skills folder (
~/.claude/skills/by default).
A typical session
When: You are building or tuning a Claude API integration and want its tools defined properly and its token costs under control. About 30–60 minutes.
You bring:
- the functions your agent should call: name, purpose and parameters
- your system prompt and a typical request
- a description of the workflow: turns, tool results, output length
-
- You
- Describe each function the model should call.
- You get
- A tool_use JSON schema per function with types, required fields and parameter descriptions, and a Python client.messages.create snippet when you ask for one.
-
- You
- Describe the workflow or share a sample request and response.
- You get
- A per-component token table, the dominant cost driver, and max_tokens and other budget controls per component.
-
- You
- Paste the system prompt and a typical request.
- You get
- Each block classed as static, quasi-static or dynamic, where each cache_control breakpoint goes with code, and an expected cache hit rate.
You end with: Tool schemas ready to wire in, a token budget with its main cost driver named, and a caching plan.
Compare API builder with another pack
Skills in this pack (4)
- eval-harness-generator Takes a skill's SKILL.md and generates a set of eval cases — trigger queries, expected output shape, and pass/fail criteria — ready to drop into the skill's evals/ directory. Use when adding evals to a new or existing skill, verifying a skill behaves as documented, or building a regression gate before modifying a skill. standard
- prompt-caching-advisor Analyzes a prompt or conversation structure and identifies the highest-value cache breakpoints — where to insert cache_control checkpoints to minimize repeated token processing. Use when designing or optimizing a Claude API integration that uses prompt caching, when input costs are dominated by a static system prompt or few-shot block, or when a multi-turn conversation reprocesses the same context on every turn. standard
- token-budget-estimator Estimates input and output token usage for a described agentic workflow, surfaces the dominant cost drivers, and suggests max_tokens budget controls per component. Use when planning a new Claude API integration, sizing a cost budget for a feature, or diagnosing why a workflow's token usage is higher than expected. standard
- tool-use-schema-writer Takes a function description (name, purpose, parameters) and writes a valid Anthropic tool_use JSON schema, with type annotations, required fields, and a description for each parameter. Adds a Python client.messages.create(tools=[...]) snippet only when asked. Use when wiring a Claude agent to a function or API, designing tools for a multi-agent system, or documenting an existing tool for agent consumption. standard