# AI Capability Pattern Matching > Uses a ready-made, research-based library of 13 AI capability patterns (see ../../references/ai-capability-pattern-library.md) to pose diagnostic questions to a new client/industry, to assemble a raw list of AI opportunity candidates — an alternative or complement to task-level decomposition. - Skill: `pilot2service/ai-capability-pattern-matching` (Agent Skill) - Install (CLI): `npx skillmds@latest add pilot2service/ai-capability-pattern-matching` - Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/ai-capability-pattern-matching/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: Pilot2Service (https://skillmd.com/u/pilot2service) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pilot2service/ai-capability-pattern-matching --- # AI Capability Pattern Matching ## Purpose Produces a raw list of AI opportunity candidates **top-down, hypothesis-driven**, using a ready-made capability pattern library — as a counterweight to the **bottom-up, data-driven** approach of `../task-level-decomposition-and-automation-fit/SKILL.md` (process/task mining one task at a time). The two are complementary: - **Bottom-up** (task-level-decomposition): works through an existing process/role one task at a time and classifies each one. Strong when the process is already precisely described, but won't surface opportunities that would only emerge from *redesigning* the process. - **Top-down** (this skill): poses the client the diagnostic questions of 13 patterns before the process has been precisely described. Faster first-pass mapping, surfaces opportunities the client wouldn't have named themselves ("we hadn't thought about it that way"), but requires validation (point 4) before a candidate goes to scoring. Use this skill especially at the **start** of a discovery workshop/ interview, before detailed process description has been done — and use `../task-level-decomposition-and-automation-fit/SKILL.md` afterward, once a specific process has been selected for closer examination. ## Anchored in research - `../../references/ai-capability-pattern-library.md` — 13 patterns, abstracted from 81 verified use cases in a broad industry report (2026) and cross-checked against a second, independent AI use-case digest (63 cases, 16 functions). - `../../../opportunity-recognition/skills/pattern-and-analogy-connector/SKILL.md` — the general Capability Pattern Mapping abstraction method, of which the pattern library is the concrete AI application. ## Method 1. **Before the meeting: select the 4–6 most relevant patterns** from the pattern library based on the client's industry/situation (not all 13 at once — too many questions at once drowns the conversation). Use the source material's industry-specific function weighting as a rough guide (e.g. manufacturing → patterns 4, 5, 6, 11; professional services → patterns 2, 3, 9). 2. **Pose each selected pattern's diagnostic question to the client as written**, don't turn it into something more technical or AI-jargon heavy. The questions are deliberately phrased in business language, not technology language (e.g. "where does a highly paid expert have to search for anomalies..." not "could an LLM read documents..."). 3. **Log every "yes, we have a situation like that" answer as a structured candidate:** pattern name, the client's own description of the situation, who does the work today, estimated volume/ frequency (if known). Don't score it yet at this stage — that happens in point 5. 4. **Validate every candidate before further processing, with three checks:** - Is the situation genuinely recurring/high enough volume to be an opportunity, or a one-off exception case? - Does the pattern's assumed AI type (Agentic/Physical/other) match the organization's current maturity level, or is there a gap risk (e.g. a Physical AI pattern in an organization with no sensor data at all)? - Is there an obvious reason why this would NOT work in this particular context (regulation, union agreement, safety criticality)? If so, flag it visibly, don't hide it. 5. **Move validated candidates into `../ai-opportunity-portfolio/SKILL.md`** for 5D scoring and 2x2 prioritization — this skill only produces the raw list, it doesn't prioritize. 6. **If the client doesn't recognize any pattern as their own**, that's information in itself: either the organization is already highly automated in these areas, or the conversation hasn't reached the right level in the organization (try a different role/team) — don't force a fit. ## What this skill does NOT do - Doesn't replace `../task-level-decomposition-and-automation-fit/SKILL.md` — it produces a fast, hypothesis-driven raw list, not a precise task-level classification. It's worth using both in the same engagement, at different stages. - Doesn't score or prioritize candidates — that's `../ai-opportunity-portfolio/SKILL.md`'s job. - Doesn't claim that all 13 patterns fit every client — some patterns are clearly more industry-specific (e.g. the Physical AI patterns) than others. - Doesn't expand the pattern library's examples with details that aren't in `../../references/ai-capability-pattern-library.md` — if you need a deeper example, refer to the original sources rather than filling in from memory. - Isn't an exhaustive listing of every possible AI opportunity — the 13 patterns are a curated sample, not a comprehensive taxonomy. New patterns will be found over time; add them to the library using the same method (see the final section of `../../references/ai-capability-pattern-library.md`). ## Refinement notes The pattern library is built on a research basis (from two independent industry AI use-case reports). Areas to keep deepening with real practice: - your own observations on which patterns resonate most often with which types of clients - new patterns you've identified yourself but that aren't in the sources — add them to `../../references/ai-capability-pattern-library.md` in the same format - a concrete workshop template/question sheet (into `../../references/`) Once this section is filled in and validated in practice, update the `maturity` field in `skills_index.json` to `draft`, `validated`, or `canonical` (see `../../../meta/maturity_levels.md`). **Don't add new fields to the frontmatter** — `name` and `description` are the only ones allowed (see `../../../meta/frontmatter_schema.md`). ## Continue from here - Before this (the general method): `../../../opportunity-recognition/skills/pattern-and-analogy-connector/SKILL.md` - A parallel, bottom-up approach: `../task-level-decomposition-and-automation-fit/SKILL.md` - Next in this pack: `../ai-opportunity-portfolio/SKILL.md` — scores and prioritizes the validated candidates this skill produces. - If the whole process is run as a paid engagement: `../ai-discovery-engagement-design/SKILL.md` - A ready-made skill chain for this situation: see `../../../playbooks/` - This pack's shared guardrails: `../../CLAUDE.md` ## References - `../../references/ai-capability-pattern-library.md` — the library of 13 patterns with examples - `../../references/` — the pack's shared background material - `../../CLAUDE.md` — the pack's shared guardrails