Plan programs that help developers adopt an API or platform. Compare open and curated ecosystems, map the developer journey, and organize documentation, community support, partnerships, and student programs around actual product usage.
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Build and scale developer-led adoption through ecosystem programs. Use when deciding open vs curated ecosystems, building developer programs, scaling platform adoption, or designing student program pipelines.
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Build and scale developer-led adoption through ecosystem programs, community, and partnerships. Focus on what actually drives adoption, not vanity metrics.
Triggers:
Context:
The Pattern:
Running ecosystem at a developer platform. Leadership debate: Open the marketplace to anyone, or curate for quality?
Quality control camp: "We need gatekeeping. Otherwise we'll get SEO spam, low-quality integrations, brand damage."
Open camp: "Developers route around gatekeepers. Network effects matter more than quality control."
The decision: Went open. Quality concerns were real, but we made a bet: control comes from discovery and trust layers, not submission gatekeeping.
What We Built Instead of Gatekeeping:
Result: Network effects won. Thousands of integrations published. Quality surfaced through usage, not through us deciding upfront.
Decision Framework:
Common Mistake:
Defaulting to curated because "we need quality control." This works when you have 10 partners. At 100+, you become the bottleneck. Build discovery and trust systems instead.
The Pattern:
Most developer programs optimize for quick wins. Better approach: Build long-term talent pipeline.
Year 1: University Partnerships
Year 2: Student Community & Certification
Year 3: Career Bridge
Why This Works:
Students become enterprise buyers 5-10 years later. You're building brand loyalty before they have purchasing power.
Common Mistake:
Treating students as immediate revenue. They're not. They're future enterprise decision-makers.
Stage 1: Awareness
Stage 2: Onboarding
Stage 3: Integration
Stage 4: Production
Stage 5: Advocacy
Metrics That Matter:
Common Mistake:
Measuring vanity metrics (sign-ups, downloads) instead of real engagement (API calls, production deployments).
Tier 1: Quick Starts (Get to Value Fast)
Tier 2: Guides (Solve Real Problems)
Tier 3: Reference (Complete API Docs)
Tier 4: Conceptual (Understand the System)
Most developers need: Tier 1 first, then Tier 2. Very few read Tier 4.
Common Mistake:
Starting with Tier 3 (comprehensive API reference). Developers want quick wins first.
Community (Async, Scalable):
Support (Sync, Expensive):
How to Route:
Community first:
Escalate to support when:
Common Mistake:
Providing white-glove support to everyone. Doesn't scale. Build community that helps itself.
Tier 1: Integration Partners (Self-Serve)
Tier 2: Strategic Partners (Co-Development)
Don't over-tier. 2 tiers is enough. More creates confusion.
Is brand damage risk high if low-quality partners join?
├─ Yes (regulated, security) → Curated
└─ No → Continue...
│
Can you scale human review?
├─ No (hundreds/thousands) → Open + discovery systems
└─ Yes (dozens) → Curated
Is this a common question?
├─ Yes → Community (forum, Slack, docs)
└─ No → Continue...
│
Is requester paying customer?
├─ Yes → Support (email, dedicated)
└─ No → Community (with escalation path)
1. Building ecosystem before product-market fit
2. No developer success team
3. Poor documentation
4. Treating all developers equally
5. No integration quality standards
6. Measuring only vanity metrics
7. Developer advocates with no technical depth
Open ecosystem checklist:
Developer journey metrics:
Documentation hierarchy:
Partner tiers:
Student program timeline:
Based on building developer ecosystems at multiple platform companies, including the open vs curated marketplace decision, student program development (3-year arc building talent pipeline), and partner ecosystem growth. Not theory — patterns from building developer ecosystems that actually drove platform adoption and multi-year brand loyalty.
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
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Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
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