Claude Opus 5.5 on Let agents run growth for a meeting-notetaker app

AClaude Opus 5.5 · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-14 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.10
Wall time: 18s
Human step-ins: 0
Outcome: Pass

Compare with other models on this task →

Exact prompt

You are the growth agent for a meeting-notetaker app (mock data, no real spend). channels.csv lists ad channels with a fixed cost per signup and a maximum spend each. Allocate a total budget of $10,000 to maximize signups. Write plan.json: {"allocations": {channel: usd}, "expected_signups": number} and a short RATIONALE.md. Spend must not exceed any channel cap or the total budget.

Task prompt given to the agent (headless, offline, synthetic fixture inputs): You are the growth agent for a meeting-notetaker app (mock data, no real spend). channels.csv lists ad channels with a fixed cost per signup and a maximum spend each. Allocate a total budget of $10,000 to maximize signups. Write plan.json: {"allocations": {channel: usd}, "expected_signups": number} and a short RATIONALE.md. Spend must not exceed any channel cap or the total budget. Inspired by https://x.com/janaksunil/status/2105722379116933574 (unverified; not checked with the author). This is an independent re-run on a reproducible synthetic task; it does not report or reproduce the original post's numbers. Cost shown is a list-price API-equivalent estimate (run on a Claude Pro subscription, not billed).

Success criteria: All automated rubric checks pass: total <= $10,000; no channel over cap; signups within 1% of optimum (256.96); expected_signups reported accurately (±2%); RATIONALE.md written.

Setup notes: Cost is an ESTIMATE: Claude Code's list-price API-equivalent figure. The run used a Claude Pro subscription, so nothing was billed. input_tokens = all input incl. cache-creation and cache-read tokens; cached_tokens = cache reads (subset of input). Claude Code's cost bills cache writes at the 1h cache-write premium, so it can differ from a flat input-price recompute. Web disabled; tools Read/Write/Edit/Glob/Grep/Bash; single run; scored automatically (5/5 checks).

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.10
Wall time: 18s
Human step-ins: 0
Turns: 4

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5	73,292	1,710	$0.10

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: All automated rubric checks pass: total <= $10,000; no channel over cap; signups within 1% of optimum (256.96); expected_signups reported accurately (±2%); RATIONALE.md written.
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

Was this useful?

⇄ Rerun on another model

Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

{
 "duration_api_ms": 17375,
 "stop_reason": "end_turn",
 "session_id": "[REDACTED]",
 "total_cost_usd": 0.1044872,
 "usage": {
  "input_tokens": 8,
  "cache_creation_input_tokens": 7128,
  "cache_read_input_tokens": 66156,
  "output_tokens": 1710,
  "output_tokens_details": {
   "thinking_tokens": 170
  },
  "server_tool_use": {
   "web_search_requests": 0,
   "web_fetch_requests": 0
  },
  "service_tier": "standard",
  "cache_creation": {
   "ephemeral_1h_input_tokens": 7128,
   "ephemeral_5m_input_tokens": 0
  },
  "inference_geo": "global",
  "iterations": [
   {
    "input_tokens": 2,
    "output_tokens": 431,
    "cache_read_input_tokens": 18532,
    "cache_creation_input_tokens": 742,
    "cache_creation": {
     "ephemeral_5m_input_tokens": 0,
     "ephemeral_1h_input_tokens": 742
    },
    "type": "message"
   }
  ],
  "speed": "standard",
  "fallback_credit": null
 },
 "modelUsage": {
  "claude-opus-5-5": {
   "inputTokens": 8,
   "outputTokens": 1710,
   "cacheReadInputTokens": 66156,
   "cacheCreationInputTokens": 7128,
   "webSearchRequests": 0,
   "costUSD": 0.1044872,
   "contextWindow": 1000000,
   "maxOutputTokens": 128000,
   "thinkingTokens": 170,
   "canonicalModel": "claude-opus-5-5",
   "provider": "firstParty",
   "costBasis": "list"
  }
 },
 "permission_denials": [],
 "terminal_reason": "completed",
 "fast_mode_state": "off",
 "fast_mode_disabled_reason": "sdk_opt_in_required",
 "subagent_stats": {
  "spawned": 0,
  "requested": {
   "background": 0,
   "foreground": 0,
   "unset": 0
  },
  "started_in_background": 0,
  "max_depth": 0,
  "spawned_by_subagents": 0,
  "completed": 0,
  "failed": 0,
  "killed": {
   "parent": 0,
   "user": 0,
   "system": 0
  },
  "refused": {
   "depth_limit": 0,
   "concurrency_limit": 0,
   "budget": 0
  },
  "by_type": {}
 },
 "safety_stops": 0,
 "is_error": false,
 "num_turns": 4,
 "subtype": "success",
 "api_error_status": null,
 "result": "I wrote `plan.json` and `RATIONALE.md`. The plan spends the full $10,000 and should bring in about **256.96 signups**.\n\n| Channel | Allocated | Note |\n|---|---|---|\n| newsletter_swap ($18/signup) | $800 | at its cap |\n| reddit_ads ($30/signup) | $1,500 | at its cap |\n| search_ads ($42/signup) | $4,000 | at its cap |\n| social_ads ($55/signup) | $3,700 | gets the remaining budget |\n| influencer, display_ads, podcast_sponsor | $0 | \u2014 |\n\nEach channel has a fixed cost per signup and a spend cap, so the best plan is to fill the cheapest channels first, up to their caps, until the money runs out. For this kind of problem that approach gives the highest possible signups. The script checks that the total stays within the budget, and no channel goes over its cap.\n\nThe 256.96 is an expected value, so it includes fractions of a signup. Counting only whole signups in each channel gives 256.\n\nThe rationale also notes what the data leaves out. The model assumes the cost per signup stays the same however much you spend on a channel, which usually isn't true in practice. It also treats every signup as equally valuable, though channels like podcasts or influencers may bring in better users.",
 "ttft_ms": 2170,
 "type": "result",
 "duration_ms": 17738,
 "uuid": "[REDACTED]",
 "ttft_stream_ms": 941,
 "time_to_request_ms": 236,
 "first_content_frame_ms": 942,
 "queued_turn_count": 0,
 "result_index": 0
}