Think Affinity Mapping

Affinity Mapping

Affinity mapping takes a pile of many individual items - raw notes, observations, quotes, data points - and groups them bottom-up by felt similarity until a small set of emergent themes appears, then names each theme so the names become the structure. The load-bearing move is deferred, bottom-up categorization: you do not sort items into predefined buckets, you let the categories surface from the items themselves. This externalizes comparison so patterns hidden in a linear list become visible, resists the frame you walked in with, and compresses many items into a few themes while keeping every item traceable to its theme. The output is a clustered theme map, not a discussion.

When to Use

• When dozens to hundreds of existing items - user-research notes, interview quotes, support tickets, survey free-text, retro stickies, workshop output - need to become a few themes.
• When the right structure is not known in advance and should emerge from the data rather than be imposed.
• When the items already exist and the job is synthesis, not generation.
• When traceability matters: you want each theme to point back to the specific items that support it.

When NOT to Use

• When there are only a handful of items. With a dozen or fewer you can reason about them directly; the clustering ceremony adds overhead without insight.
• When you need a top-down logical structure - a question decomposed into MECE sub-questions or a hypothesis tree. That is top-down decomposition from a question; use an issue-tree skill. Affinity mapping is bottom-up, from items.
• When you need to generate ideas or options. Affinity mapping only organizes items that already exist and produces no new ideas. Use an ideation skill (for example brainwriting) to create the items first, then affinity-map them.
• When the categories are already fixed and authoritative (a required taxonomy, a compliance schema). Then you are coding into known buckets, not discovering emergent themes.
• As a ritual - grouping into a few buckets and slapping confident names on them with no traceability is cargo-cult synthesis, not insight.

Instructions

When asked to run an affinity map, follow these steps:

1. Frame the question and gather the items. State in one sentence what synthesis is for (for example, "what is blocking free-tier activation?"), and assemble every item as a discrete, comparable unit. If there are only a handful of items, or the categories are already fixed, say so and stop.
2. Cluster before naming. Place items that feel related together, bottom-up, by similarity. Do not start from predefined buckets and do not name groups yet. Let clusters form, split, and merge as items accumulate. This deferral is the mechanism; naming first defeats it.
3. Name each emergent theme from its contents. Once clusters are stable, give each a short descriptive name that answers to the items inside it, not to your prior frame. A theme whose items do not cohere is a signal to split or dissolve it, not to force a label.
4. Keep every item traceable. Each theme records the source items it contains (by list, or by count plus representative examples). Items that did not cluster go to an explicit outliers / parking lot, never silently dropped.
5. Weight and read the themes. Note each theme's relative size or strength, and state what the map tells you - which themes dominate, which are thin, what surprised you. Size is a signal of salience, not of truth; flag thin or borderline clusters as tentative.
6. Emit the theme map and a short summary. Produce the artifact in references/TEMPLATE.md: a one-paragraph "themes and what they tell us" summary above the named-theme table, with outliers kept visible.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the filled clustered theme map plus its summary, not a prose essay.

Quality Checklist

Before finalizing, verify:

• [ ] Clustering happened before naming (themes emerged from items, not from predefined buckets).
• [ ] Each theme has a short name that answers to the items inside it, not to a prior frame.
• [ ] Every item is traceable to a theme, and items that did not cluster are kept in an explicit outliers / parking lot, not dropped.
• [ ] Themes are weighted by relative size or strength, and thin or borderline clusters are flagged as tentative, not laundered by a confident label.
• [ ] The output is the clustered theme map artifact, not prose.
• [ ] No overclaiming: the skill organizes a scattered pile into named, traceable themes; it does not promise objectively better or bias-free themes (see evidence/dossier.md).

Evidence

Tier P (practitioner). Affinity mapping is a long-standing, widely-taught practitioner standard for synthesizing large qualitative piles (the KJ method; Kawakita 1967), with a plausible cognitive basis in external representation and chunking. It does not have strong controlled evidence that it produces better, more accurate, or less biased themes than another synthesis method, and "group by similarity" remains a subjective judgment. The evidence is transferred from human practice and has not been validated for AI-augmented use. Full grading, sources, and caveats: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed run.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: product-on-purpose
• Source: product-on-purpose/thinking-framework-skills
• License: Apache-2.0
• Homepage: https://product-on-purpose.github.io/thinking-framework-skills/

Install and usage instructions live in the source repository linked above.