AGENT SKILLBY RETIEEDRA-PROFOUND
AI retrieval fan-out mapper
AT A GLANCE
AI retrieval fan-out mapper is an agent skill for general marketing. Maps the search queries and concrete fact needs that answer engines may generate behind a user prompt, then turns them into evidence-backed content. It is published by retieedra-profound in the retieedra-profound/skills-marketing-library GitHub repository under the MIT license. To add it, run npx skills add retieedra-profound/skills-marketing-library --skill ai-retrieval-fanout-mapper
- REPO STARS
- NOT AVAILABLE
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 2.8 KB57 LINES
QUICK ANSWER
- WHAT IT IS01
- Maps the search queries and concrete fact needs that answer engines may generate behind a user prompt, then turns them into evidence-backed content requirements. It brings Profound's specific operating context into AI retrieval fan-out mapper, so the agent is guided by a sharper source than a generic prompt.
- INPUTS02
- Context the agent needs: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add retieedra-profound/skills-marketing-library --skill ai-retrieval-fanout-mapperREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
FRONTMATTER
- name
- ai-retrieval-fanout-mapper
- description
- Maps the search queries and concrete fact needs that answer engines may generate behind a user prompt, then turns them into evidence-backed content requirements. Use when content matches visible prompts but is not retrieved or cited by ChatGPT, Perplexity, or Copilot.
ON THIS PAGE · 5
AI Retrieval Fan-Out Mapper#
Research basis#
This skill operationalizes Profound's research, What AI engines actually search for and why ChatGPT never searches the same way twice (opens in a new tab), published April 30, 2026.
Key takeaways from the study:
- Profound tracked 10,000 prompts across ChatGPT, Perplexity, and Copilot for 14 days.
- The engines generated similar numbers of searches per run but differed greatly in repetition and wording.
- ChatGPT generated highly varied research paths; Perplexity stayed closest to the prompt; Copilot compressed prompts into shorter search-style queries.
- All three often converted broad advice or comparison prompts into concrete fact-retrieval searches.
These findings are observational and may change with models and product updates. Use observed fan-outs when available and label inferred fan-outs as hypotheses.
Goal#
Identify the facts, entities, constraints, vocabulary, and supporting evidence an answer engine may retrieve while answering a prompt family.
Workflow#
- Define the prompt family, audience, stage, engine, language, and market.
- Capture observed fan-out queries when the platform or analytics product exposes them.
- Otherwise infer candidate retrieval paths and label them clearly as inferred.
- Decompose the user job into:
- entities and category terms
- factual subquestions
- comparison criteria
- constraints such as price, compatibility, location, and recency
- standards, definitions, reviews, and primary evidence
- Group fan-outs into stable fact families rather than exact query strings.
- Map each family to the current best source and competing cited sources.
- Audit whether the brand has a canonical, current, specific answer.
- Recommend improving, consolidating, or creating content only when a real information gap exists.
- Test across repeated runs and engines; do not expect identical retrieval paths.
Do not create one doorway page per fan-out query. One strong resource can satisfy several related retrieval needs.
Engine considerations#
- For ChatGPT, plan for broad vocabulary and adjacent research paths.
- For Perplexity, preserve clear category terms and direct factual matches.
- For Copilot, cover concise search-style formulations and core entities.
- For every engine, verify actual behavior before turning a prior into a rule.
Output#
Return:
- Prompt-family definition.
- Fan-out map marking each query as observed or inferred.
- Fact-family matrix with user need, evidence, current source, competitor source, and gap.
- Content recommendation identifying which existing page should own each fact.
- Required claims, sources, dates, examples, and comparison criteria.
- Test plan by engine with repeated observations and success measures.
FAQ
07 QUESTIONS
01What is the AI retrieval fan-out mapper agent skill?
Maps the search queries and concrete fact needs that answer engines may generate behind a user prompt, then turns them into evidence-backed content requirements. It is listed under general marketing in the 100xLab agent skills directory.
02How do I install AI retrieval fan-out mapper in Cursor, Claude Code, or Codex?
Run npx skills add retieedra-profound/skills-marketing-library --skill ai-retrieval-fanout-mapper in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only AI retrieval fan-out mapper, not every skill in the repository.
03What does the AI retrieval fan-out mapper SKILL.md cover?
The SKILL.md includes sections on Research basis, Goal, Workflow, Engine considerations and Output. Read the full text on this page or in plain markdown at /resources/agent-skills/retieedra-profound-skills-marketing-library--ai-retrieval-fanout-mapper--1ly2jul/raw.
04Which agents can use AI retrieval fan-out mapper?
AI retrieval fan-out mapper installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is AI retrieval fan-out mapper under?
The retieedra-profound/skills-marketing-library repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is AI retrieval fan-out mapper free to use?
Reading, copying and installing AI retrieval fan-out mapper costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of AI retrieval fan-out mapper?
AI retrieval fan-out mapper comes from https://github.com/retieedra-profound/skills-marketing-library. The SKILL.md is at https://github.com/retieedra-profound/skills-marketing-library/blob/HEAD/profound/ai-retrieval-fanout-mapper/SKILL.md. The author is retieedra-profound.
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ATTRIBUTION
AI retrieval fan-out mapper was written by its original authors and is published in retieedra-profound/skills-marketing-library (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.