skills/stress-test-analogy/SKILL.md
--- name: stress-test-analogy description: Stress-tests a proposed analogy by finding the edge where the mapping breaks, then frames that break as a teaching opportunity the writer can fold into the post. Every analogy has a boundary; the writer's style treats that boundary as a feature. Use after generate-analogy-set and map-analogy-to-concept, for each framing. Trigger keywords: where does it break, stress-test, boundary, edge case, fold the break, analogy limits. --- # Stress Test Analogy #
npx skillsauth add lyndonkl/claude skills/stress-test-analogyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Related skills: Called by the Intuition Builder per framing. Input is one framing (source + target + mapping from map-analogy-to-concept). Output feeds the final artifact's "Where it breaks" section for each framing and the Technical Reviewer's flag-boundary-break on drafts.
For one framing (source → target + mapping):
- [ ] Step 1: Identify the mapping's weakest link — which source-component-to-target-component pairs are forced?
- [ ] Step 2: Generate 2-3 edge cases where the forced pair fails
- [ ] Step 3: Write the boundary as one or two sentences — specific and named
- [ ] Step 4: Propose a "fold" — how the writer turns this break into content in the draft
- [ ] Step 5: Rate fold value: high | medium | low (high = the break is its own paragraph or sub-section)
The boundary sentence should be specific enough that the reader could verify it. Good: "The drawer metaphor implies physical contiguity in memory, but a KV cache is logically indexed, not physically contiguous." Bad: "The analogy doesn't quite work at scale."
The writer's voice turns boundary-breaks into features, not hidden flaws. A good fold says: "This is where the analogy stops working — and here's what's interesting about why." Propose one sentence for the writer to edit.
Analogies break in predictable ways:
Use these tags to classify the break type in the output.
Framing: "KV cache is a library card catalog with a fixed drawer count."
Mapping (from map-analogy-to-concept):
Stress test:
Boundary sentence: "The drawer metaphor captures capacity and lookup, but it hides two things: eviction dynamics under streaming decode (real drawers don't lose cards every turn) and the role of the cache as input to attention (a card catalog doesn't compute anything — a KV cache does)."
Proposed fold (for the writer): "Say upfront that the library image is for the shape of the thing, not the motion. Then let eviction be the paragraph that breaks the image — 'real drawers don't evict a card every time you add a new one; a KV cache does, which is where the analogy starts needing a second image.' That becomes the pivot."
Fold value: high. This break is a whole paragraph in the draft.
{boundary: str, break_type: str, fold: str, fold_value: high|medium|low}.testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.