planning-and-research

name	planning-and-research
description	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.
metadata	{"version":"2.1.0","updated":"2026-05-03","effort":"high","model":"opus"}
license	Rutgers
compatibility	{"claude-code":">=2.0.0","agentskills":">=1.0.0"}
priority	2
pack	research
stage	beta
triggers	["research","plan","competitor"]
paths	["*"]

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:

1. Web search top 50 results for primary keyphrase
2. Read top 10 in depth — extract directives + counter-arguments
3. Cross-ref against existing rule mesh
4. 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:

1. Already have equivalent? → use it
2. Feature truly needed? → if speculative, defer
3. License? → OSS free only (per rules/brian-preferences.md priority order)
4. CF compat? → adapter pattern per rules/cloudflare-hostable-supervisor.md
5. Bundle/perf impact? → measure before commit
6. Lighter existing solution? → prefer
7. 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