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Process Log — RevenueCat Charts API Take-Home Assignment

Agent: Katire (autonomous AI agent) Operator: Eduardo Muth Martinez Runtime: OpenClaw · Claude Opus 4.6 Started: 2026-03-12 21:49 UTC Tools used: OpenClaw, Claude Opus 4.6, RevenueCat Charts API V2, ElevenLabs TTS, OpenAI gpt-image-1, GitHub API, Notion API, ffmpeg

Phase 1: Research (21:49-21:52 UTC)

  • Fetched and analyzed RevenueCat Charts documentation
  • Reviewed API V2 authentication, OAuth scopes, MCP tools reference
  • Identified Charts API as new and under-documented publicly
  • Reviewed Charts v3 (Beta) real-time reporting docs
  • Assessed sandbox constraints: no Python, no curl, no network initially

Key decision: Build a Python CLI + library over a web app. Most useful form factor for both developers AND agents. Web apps need hosting; Python packages work anywhere.

Phase 2: Initial Build (21:53-22:30 UTC)

  • Built rc-insights v0.1.0: 4 Python modules, 3 example scripts
  • Wrote blog post (1,689 words), 5 social posts, video script
  • Designed growth campaign with 5 communities and $100 budget
  • Created Notion submission page

Constraint: Sandbox had no Python runtime or network. Built from documentation patterns.

Phase 3: Environment Upgrade + Live Testing (01:07-01:15 UTC)

  • Sandbox upgraded with Python, curl, git, API keys
  • Tested real Charts API endpoints, discovered actual response schema
  • Rewrote client to match real API (zero dependencies, stdlib only)
  • Verified against live Dark Noise data: MRR $4,538, 2,519 active subs, 56 trials
  • Generated sample reports from real data
  • Pushed to GitHub: github.com/Clueless-Creations/rc-insights

Key discovery: Charts API response uses unix timestamps in cohort field, measure index for multi-metric charts, values array (not data). 21 available chart types discovered via API error message pattern.

Phase 4: Agent-Native Self-Evaluation (02:04-02:15 UTC)

Applied agent-native architecture principles against rc-insights:

Violations found:

  • Analyzer was a workflow-shaped tool (bundles judgment into code)
  • Chart names were statically mapped via Python enum
  • No explicit completion signals for agent loops
  • Thresholds were hardcoded, not agent-controllable
  • No MCP server for agent discovery

Fixes applied (v0.2.0):

  • Made analysis primitives public: calc_trend(), detect_anomalies()
  • Added list_available_charts() for dynamic API discovery
  • Made all thresholds configurable via constructor parameters
  • Wrote self-evaluation document with honest assessment

Phase 5: MCP Server + Full Agent-Native (02:15-02:30 UTC)

Built MCP server with 7 tools (v0.3.0):

  • rc_list_charts — dynamic discovery
  • rc_get_overview — current metrics
  • rc_get_chart — query any chart
  • rc_analyze_health — full analysis with configurable thresholds
  • rc_calc_trend — atomic trend primitive
  • rc_detect_anomalies — atomic anomaly primitive
  • rc_generate_report — multi-format report generation

Added describe() for self-describing capability manifest. All tools tested against live Dark Noise API.

Phase 6: Video Production (02:31-02:43 UTC)

  • Generated narration via ElevenLabs TTS (79 seconds, multilingual v2)
  • Generated 6 key frame images via OpenAI gpt-image-1 (1536x1024, high quality)
  • Applied Ken Burns effect to each frame via ffmpeg
  • Composited 6 scenes with narration overlay
  • Final video: 1920x1080, h264, 30fps, 79s, 11.4MB
  • Passed automated quality gate (resolution, fps, duration, codec checks)

Phase 7: Final Audit + Polish (02:43-02:50 UTC)

  • Fixed incorrect GitHub repo URLs in blog post and social posts
  • Rewrote CLI for zero-dependency stdlib (was using click/rich which aren't installed)
  • Added retry with backoff for API rate limits
  • Verified all deliverables against assignment requirements line-by-line
  • Tested CLI against live API: overview, health, chart, discover, report

Key Decisions

Decision Reasoning
Python library over web app Most useful for devs AND agents. No hosting needed.
Zero dependencies (stdlib only) Frictionless adoption. No pip dependency hell.
Health score (0-100) over raw data Developers need decisions, not dashboards.
Agent-native architecture MCP server, dynamic discovery, configurable thresholds, atomic primitives.
Open source (MIT) Community tools compound. Proprietary demos die.
Self-evaluation included Shows the loop: build → evaluate → improve.
ElevenLabs + gpt-image-1 + ffmpeg for video Full autonomous production pipeline. No human intervention.

Tradeoffs

Tradeoff Choice Why
Click/Rich CLI vs stdlib stdlib Zero deps wins over pretty output
Full cohort analysis vs basic health Basic health Ship working v1, iterate on v2
Test all 21 chart types vs test core 5 Core 5 Rate limits + time. Core charts cover 90% of use cases.
Single video vs multi-format Single MP4 One solid video > multiple mediocre ones

What I'd Do Differently With More Time

  1. Test all 21 chart types and document schema differences
  2. Add cohort analysis (the cohort_explorer endpoint)
  3. Build a web dashboard that auto-refreshes
  4. Create per-chart analysis primitives (revenue-specific, churn-specific)
  5. Add scheduling for daily health reports
  6. Generate social media image assets (OG cards, screenshots)
  7. Record actual screen capture of the CLI in action (not just generated frames)