03 planning and research

Skill heymegabyte/claude-skills/03-planning-and-research

14-category autonomous product-building OS for 32+ AI coding tools. One-line prompts → deployed products.

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npx -y skills add heymegabyte/claude-skills --skill 03-planning-and-research

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What its author says it does

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Deep web research, competitor scanning, technology evaluation, and implementation planning. Decomposes work into vertical slices, identifies parallel workstreams, tracks assumptions with confidence levels, and designs the critical path for minimum wall-clock time.

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SKILL.md

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03 — Planning and Research

Decompose work into parallel vertical slices using deep web research, competitor scanning, and technology evaluation.

Deep research protocol

Before any non-trivial implementation:

Web search top 50 results for primary keyphrase
Read top 10 in depth — extract directives + counter-arguments
Cross-ref against existing rule mesh
Confidence-track every claim 0-1

Use web_search_20260209 + web_fetch_20260209 (free when paired with code_execution_20260120).

Competitor scanning

For every website build, run rules/competitor-research.md Phase -1 BEFORE Phase 0:

Identify top 5-10 audience-comparable sites
Score each on 100-pt rubric (10 dims × 10pts)
Set ≥15% beat-floor for Phase 6 loop termination

Technology evaluation

For every new dep / framework / service consideration:

Already have equivalent? → use it
Feature truly needed? → if speculative, defer
License? → OSS free only (per rules/brian-preferences.md priority order)
CF compat? → adapter pattern per rules/cloudflare-hostable-supervisor.md
Bundle/perf impact? → measure before commit
Lighter existing solution? → prefer
Decide: install now · defer · adapter-only · reject + document why

Implementation planning

Decomposition

Identify atomic units (file × layer)
Mark dependencies between units
Distinguish independent vs serial chains

Vertical slicing

Per 06-build-and-slice-loop:

Each slice ships through every layer (UI → API → DB → tests → deploy)
Homepage FIRST (no exceptions)
Slice = one feature, not one layer

Parallelism plan

Per rules/parallel-subagent-economy.md:

≥5-min wall-clock saving + independent → fan out
Sweet spot 3-4 specialists, ceiling 6
Batch beyond 6 in waves
Sonnet default; Opus for security/architect/visual-qa overrides

Critical path

Identify longest dependency chain
Front-load research that gates implementation
Parallelize everything off the critical path
Wall-clock = max(critical_path, max parallel branch)

Assumption tracking

Every assumption logged in _assumptions.md with:

Claim
Confidence 0-1
Evidence sources
Decision impact if wrong
Verification trigger

Confidence <0.7 → research more. Per rules/auto-meta-work.md.

Three-Layer Knowledge

Per ~/.claude/CLAUDE.md § Thinking:

L1 = proven (existing rules + patterns)
L2 = trending (blog posts, recent papers)
L3 = first principles (build from scratch)

Prefer L3. Best outcome of research is NOT finding a solution to copy — it's understanding the problem deeply enough to design a better one.

Self-Argue (before major decisions)

Generate strongest counterargument. If you can't defeat it, decision is wrong.

Boil-the-lake

Marginal cost of completeness is near-zero. When complete costs minutes more than shortcut, do complete. Boil lakes, flag oceans.

Output artifacts

_research.json — raw findings, source URLs, confidence
_assumptions.md — tracked claims
PLAN.md — implementation roadmap w/ parallelism plan + critical path
_decisions.md — architectural decisions w/ rationale + alternatives
_brief_summary.txt — 100-word digest for downstream agents