Agentic Coding Product Research

Build evidence-backed product intelligence for AI coding assistant and software-agent products.

Use This For

• Mapping audiences, jobs, anxieties, and "I keep coming back" moments for agentic coding tools.
• Comparing Cursor, Claude Code, Codex, Warp Code, Devin, Windsurf/Cascade, Copilot, Cline, Aider, OpenHands, and homegrown stacks.
• Finding unmet needs Port Daddy can own: coordination, durable transcripts, review proof, spend control, sandboxing, swarm visibility, and operator trust.
• Turning tech press, official docs, research papers, GitHub traces, Reddit/HN discourse, and failed local dogfood into user stories.

Do Not Use This For

• Implementing a terminal/editor/runtime agent loop directly.
• UI composition without first turning findings into workflow evidence.
• Treating benchmarks as product truth without user stories and review failure modes.

Process

flowchart TD
  A[Collect current evidence] --> B[Segment audiences]
  B --> C[Extract jobs, pains, craves]
  C --> D[Map product mechanics]
  D --> E[Find hacks and homegrown tools]
  E --> F[Score Port Daddy opportunities]
  F --> G[Emit story matrix and unmet-needs brief]

1. Collect sources across four lanes: official docs, tech press, academic/benchmark work, and social/homegrown workflows.
2. Segment audiences by workflow pressure, not job title alone: solo founder, staff engineer, enterprise admin, maintainer, agent power user, non-developer builder.
3. Extract user stories in the format As <audience>, I want <agentic capability>, so I can <workflow outcome without hidden risk>.
4. Identify product mechanics users praise or crave: context attachment, plan/apply/review, checkpoints, worktrees, visual diff, background agents, model choice, and mobile/cloud handoff.
5. Identify negative demand: surprise spend, invisible state, stale context, agent collisions, weak rollback, AI support hallucinations, unsafe tool execution, and review burden.
6. Translate evidence into Port Daddy opportunities with a proof requirement for each opportunity.

Output Contract

Produce:

• audiences: array of audience profiles with jobs, pains, craves, trust thresholds, and comeback triggers.
• user_stories: array of stories with evidence sources and Port Daddy implications.
• opportunities: ranked Port Daddy product opportunities with proof artifacts required.
• risks: skeptical caveats, source limits, and claims needing live verification.

Use scripts/story_matrix.mjs to validate and derive a JSON story matrix from a source manifest.

Anti-Patterns

Benchmark Theater

Novice: "The product with the highest SWE-bench number wins." Expert: Benchmarks predict only part of adoption. Users come back when the tool reduces start friction, preserves context, proves its work, and makes mistakes recoverable. Detection: Research omits user stories, review workflows, or rollback mechanics.

Vibe Without Receipts

Novice: "People love it on social, so ship the same chat box." Expert: Social praise usually compresses a full loop: prompt, context, agent action, visible progress, diff review, tests, PR, and undo. Capture the loop, not the applause. Detection: No source links, no artifact requirement, no failure case.

Tool-Only Framing

Novice: "Port Daddy should be another coding assistant." Expert: Port Daddy's wedge is the coordination control plane around assistants: identity, claims, transcripts, spend, sandboxing, review proof, and multi-agent orchestration. Detection: Proposed feature competes on code generation alone rather than operator control and durable evidence.

References

File	Load When
references/source-map.md	Need current product/source landscape and research citations.
references/audience-stories.md	Need audience segmentation, user stories, and Port Daddy opportunities.
examples/expected-output.md	Need the shape of a finished research brief.
templates/output-template.md	Need a reusable brief template.
schemas/source-manifest.schema.json	Need to validate research inputs.
scripts/story_matrix.mjs	Need deterministic user-story matrix generation.
agents/openai.yaml	Need a subagent descriptor for delegated product research.

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Skill Bundle Index

Every file in this skill, and when to open it. Auto-generated by the repo skill-architect indexer.

root

• CHANGELOG.md — Agentic Coding Product Research — Changelog — - Initial skill creation - Core process defined - Reference files added
• README.md — Agentic Coding Product Research — Evidence-backed product research for AI coding assistant and software-agent tools.

agents/

• agents/openai.yaml — openai (data/schema)

examples/

• examples/expected-output.md — Example Output: Agentic Coding Product Research — Research question: what should Port Daddy build around AI coding assistants after reviewing Cursor, Claude Code, Codex, Warp Code, Devin, Wi

references/

• references/audience-stories.md — Audience Stories And Port Daddy Opportunities — Use this when turning research into product requirements.
• references/source-map.md — Source Map: AI Coding Assistant Product Landscape — Use this when grounding product claims in current market evidence.

schemas/

• schemas/source-manifest.schema.json — source manifest.schema (data/schema)

scripts/

• scripts/story_matrix.mjs

templates/

• templates/output-template.md — Agentic Coding Product Research Brief — [One sentence naming the product decision, audience, or Port Daddy opportunity.] | Source | Kind | Current As Of | Claim Used | | --- | ---

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