How do I install the Map Frictions skill?

Run npx skillmds@latest add alaimo-labs/map-frictions in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Map Frictions safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Map Frictions?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Map Frictions free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Map Frictions?

alaimo-labs (@alaimo-labs) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Map Frictions by alaimo-labs — Agent Skill

Name: Map Frictions
Rating: 4.2 (5 reviews)
Author: alaimo-labs

File contents

/map-frictions

Map cognitive frictions across the steps of a user journey, following the cognitive-frictions skill.

Input: $ARGUMENTS

Workflow

Resolve the journey. If a file was named, use it. If the journey was described or pasted in the conversation, use that. If a spec in product/specs/ contains a journey section, offer to analyze that journey. With nothing to start from, elicit it: who is the actor (link to a persona from product/personas/ if one fits), what goal they pursue, and the steps from trigger to outcome — one user action or decision per step, typically 5–15. Confirm the step list before analyzing.
Analyze each step through the MFC lens per the cognitive-frictions skill: which of the four categories applies (if any — never force a friction onto a frictionless step) with a one-line why per category, the friction (mental effort), the problem (observable consequence, one sentence), the evidence metric, and severity as frequency × intensity. Evidence metrics come from data already in the repo (insights, survey analyses, research, analytics the user shares) — when nothing measures a friction yet, propose the metric to instrument marked "not yet measured", never invented numbers.
Prioritize. Rank the identified frictions by severity and state each one's metric impact — the input metric it degrades, per product/overview.md or specs. If the product declares no input metrics yet, note that once and mark each impact as proposed. The top of the ranking is the list of AI-opportunity candidates.
Save to product/journeys/{YYYY-MM-DD-HHMM}-{journey-slug}.md: journey (actor, goal, steps), the per-step friction map, and the ranked opportunities — in the language of the conversation, with a header noting the persona (if any) and the journey's source (elicited, spec, existing file).
Suggest next steps in natural language: shape the top friction into a feature idea with /clarify-idea or straight into a spec with /write-spec; or pressure-test whether the friction is real by interviewing a persona with /interview-persona.

---name: map-frictionsdescription: Analyze a user journey step by step to identify cognitive frictions where AI could add real value (Cognitive Friction Map)---# /map-frictionsMap cognitive frictions across the steps of a user journey, following the `cognitive-frictions` skill.Input: $ARGUMENTS## Workflow1. **Resolve the journey.** If a file was named, use it. If the journey was described or pasted in the conversation, use that. If a spec in `product/specs/` contains a journey section, offer to analyze that journey. With nothing to start from, elicit it: who is the actor (link to a persona from `product/personas/` if one fits), what goal they pursue, and the steps from trigger to outcome — one user action or decision per step, typically 5–15. Confirm the step list before analyzing.2. **Analyze each step** through the MFC lens per the `cognitive-frictions` skill: which of the four categories applies (if any — never force a friction onto a frictionless step) with a one-line why per category, the friction (mental effort), the problem (observable consequence, one sentence), the evidence metric, and severity as frequency × intensity. Evidence metrics come from data already in the repo (insights, survey analyses, research, analytics the user shares) — when nothing measures a friction yet, propose the metric to instrument marked "not yet measured", never invented numbers.3. **Prioritize.** Rank the identified frictions by severity and state each one's metric impact — the input metric it degrades, per `product/overview.md` or specs. If the product declares no input metrics yet, note that once and mark each impact as proposed. The top of the ranking is the list of AI-opportunity candidates.4. **Save** to `product/journeys/{YYYY-MM-DD-HHMM}-{journey-slug}.md`: journey (actor, goal, steps), the per-step friction map, and the ranked opportunities — in the language of the conversation, with a header noting the persona (if any) and the journey's source (elicited, spec, existing file).5. **Suggest next steps** in natural language: shape the top friction into a feature idea with `/clarify-idea` or straight into a spec with `/write-spec`; or pressure-test whether the friction is real by interviewing a persona with `/interview-persona`.

Frequently asked questions

Map Frictions