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/map-research-landscape

Map candidate research fields or subfields, their maturity, competition, entry barriers, and opportunity structure. Evidence/tool acquisition is host-selected.

From plugin
de-anthropocentric-research-engine
507200 skills

Claude Code

$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill map-research-landscape --agent claude-code

SKILL.md

map-research-landscape.SKILL.md
name: map-research-landscape
description: "Map candidate research fields or subfields, their maturity, competition, entry barriers, and opportunity structure. Evidence/tool acquisition is host-selected."

map-research-landscape

Purpose

Map candidate research fields or subfields, maturity, competition, entry barriers, tractability, and opportunity structure.

Input contract

required: [research_intent, scope_anchor]
optional: [actor_profile, seed_evidence, constraints]
constraints: [candidate fields require evidence references and declared comparison dimensions]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

1. You MUST load skill `generate-candidate-directions` to generate diverse candidate fields and deliberate boundary crossings. 2. You MUST load skill `synthesize-field-panorama` to synthesize maturity, competition, entry-barrier, tractability, and opportunity evidence. If the resulting fields must be ordered, screened, or selected under explicit criteria, consider `rank-candidates` as the next tactic.

Deviation: Evidence acquisition is host-selected; do not invoke removed tool wrappers. Re-run candidate generation only when intent or scope changes materially.

Output contract

produces: [candidate_field_set, field_panorama, maturity_map, competition_map, opportunity_structure]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]

Thresholds and quality gates

  • Each candidate must have evidence for the declared dimensions or an explicit missing marker.
  • The panorama must include at least one direct opportunity and one barrier per candidate.
  • Acquisition sufficiency is relative to the declared eligible evidence universe; saturation is assessed by marginal information gain rather than a fixed source count.

Failure and counterexamples

Reject niche lists with no evidence, maturity claims based on publication volume alone, and opportunity claims that omit competition or entry barriers.

Provenance map

  • `resolved: landscape-reconnaissance`
  • `resolved: north-star-crystallization-generate-candidate-fields`
  • `resolved: north-star-crystallization-landscape-synthesis`
  • `resolved: broad-web-search`
  • `resolved: broad-paper-search`
  • `resolved: north-star-crystallization-deep-web-search`

Preserved source criteria ledger

| source | source line | kind | source criterion | |---|---:|---|---| | none retained | - | - | No source numeric/textual criterion retained after normalization. |

Context checkpoint / Delta notes

Append candidate fields, evidence references, dimension assessments, boundary-crossing rationale, and unresolved acquisition questions.

Read more
@yogsoth-ai
Ships withde-anthropocentric-research-engine

A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.

Stars on GitHub

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Questions about map-research-landscape.

Which coding agents can use map-research-landscape?
map-research-landscape installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.