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Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.

Use this Skill: https://skilld.dev/gh/davila7/claude-code-templates/claude-api

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sharedmanaged-agents-outcomes.md

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Managed Agents — Outcomes

An outcome elevates a session from conversation to work: you state what "done" looks like, and the harness runs an iterate → grade → revise loop until the artifact meets the rubric, hits max_iterations, or is interrupted. A separate grader (independent context window) scores each iteration against your rubric and feeds per-criterion gaps back to the agent.

The SDK sets the managed-agents-2026-04-01 beta header automatically on all client.beta.sessions.* calls; no additional header is required for outcomes.


The user.define_outcome event

Outcomes are not a field on sessions.create(). You create a normal session, then send a user.define_outcome event. The agent starts working on receipt — do not also send a user.message to kick it off.

session = client.beta.sessions.create(
    agent=AGENT_ID,
    environment_id=ENVIRONMENT_ID,
    title="Financial analysis on Costco",
)

client.beta.sessions.events.send(
    session_id=session.id,
    events=[
        {
            "type": "user.define_outcome",
            "description": "Build a DCF model for Costco in .xlsx",
            "rubric": {"type": "text", "content": RUBRIC_MD},
            # or: "rubric": {"type": "file", "file_id": rubric.id}
            "max_iterations": 5,  # optional; default 3, max 20
        }
    ],
)
Field Type Notes
type "user.define_outcome"
description string The task. This is what the agent works toward — no separate user.message needed.
rubric {type: "text", content} | {type: "file", file_id} Required. Markdown with explicit, independently gradeable criteria. Upload once via client.beta.files.upload(...) (beta files-api-2025-04-14) to reuse across sessions.
max_iterations int Optional. Default 3, max 20.

The event is echoed back on the stream with a server-assigned outcome_id and processed_at.

Writing rubrics. Use explicit, gradeable criteria ("CSV has a numeric price column"), not vibes ("data looks good") — the grader scores each criterion independently, so vague criteria produce noisy loops. If you don't have a rubric, have Claude analyze a known-good artifact and turn that analysis into one.


Outcome-specific events

These appear on the standard event stream (sessions.events.stream / .list) alongside the usual agent.* / session.* events.

Event Payload highlights Meaning
span.outcome_evaluation_start outcome_id, iteration (0-indexed) Grader began scoring iteration N.
span.outcome_evaluation_ongoing outcome_id Heartbeat while the grader runs. Grader reasoning is opaque — you see that it's working, not what it's thinking.
span.outcome_evaluation_end outcome_evaluation_start_id, outcome_id, iteration, result, explanation, usage Grader finished one iteration. result drives what happens next (table below).

span.outcome_evaluation_end.result

result Next
satisfied Session → idle. Terminal for this outcome.
needs_revision Agent starts another iteration.
max_iterations_reached No further grader cycles. Agent may run one final revision, then session → idle.
failed Session → idle. Rubric fundamentally doesn't match the task (e.g. description and rubric contradict).
interrupted Only emitted if _start had already fired before a user.interrupt arrived.
{
  "type": "span.outcome_evaluation_end",
  "id": "sevt_01jkl...",
  "outcome_evaluation_start_id": "sevt_01def...",
  "outcome_id": "outc_01a...",
  "result": "satisfied",
  "explanation": "All 12 criteria met: revenue projections use 5 years of historical data, ...",
  "iteration": 0,
  "usage": { "input_tokens": 2400, "output_tokens": 350, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1800 },
  "processed_at": "2026-03-25T14:03:00Z"
}

Checking status & retrieving deliverables

Status — either watch the stream for span.outcome_evaluation_end, or poll the session and read outcome_evaluations:

session = client.beta.sessions.retrieve(session.id)
for ev in session.outcome_evaluations:
    print(f"{ev.outcome_id}: {ev.result}")  # outc_01a...: satisfied

Deliverables — the agent writes to /mnt/session/outputs/. Once idle, fetch via the Files API with scope_id=session.id. This is the same session-outputs mechanism documented in shared/managed-agents-environments.md → Session outputs (including the dual-beta-header requirement on files.list).


Interaction rules & pitfalls

  • One outcome at a time. Chain by sending the next user.define_outcome only after the previous one's terminal span.outcome_evaluation_end (satisfied / max_iterations_reached / failed / interrupted). The session retains history across chained outcomes.
  • Steering is allowed but optional. You may send user.message events mid-outcome to nudge direction, but the agent already knows to keep working until terminal — don't send "keep going" prompts.
  • user.interrupt pauses the current outcome — it marks result: "interrupted" and leaves the session idle, ready for a new outcome or conversational turn.
  • After terminal, the session is reusable — continue conversationally or define a new outcome.
  • Outcome ≠ session-create field. Don't put outcome, rubric, or description on sessions.create() — outcomes are always sent as a user.define_outcome event.
  • Idle-break gate is unchanged. In your drain loop, keep using event.type === 'session.status_idle' && event.stop_reason?.type !== 'requires_action' — do not gate on span.outcome_evaluation_end alone (on needs_revision the session keeps running). See shared/managed-agents-client-patterns.md Pattern 5.

For the raw HTTP shapes and per-language SDK bindings beyond Python, WebFetch https://platform.claude.com/docs/en/managed-agents/define-outcomes.md (see shared/live-sources.md).

Source: SKILL.md on GitHub

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    The skill is a comprehensive documentation and code example library for building applications with the Anthropic Claude API and official SDKs. It covers multiple languages and advanced features like Managed Agents and prompt caching. All content follows security best practices, referencing official domains and well-known services without any malicious patterns.

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