mechanism-space-cartographer

name	mechanism-space-cartographer
description	Use when exploring what to build for a business or project before generating product features. Use when the user wants to enumerate solutions, understand causal levers, map opportunity space, or says "what should I build", "enumerate mechanisms", "how do I improve X", "mechanism space", or "what moves the metric".

Mechanism Space Cartographer

Overview

Maps all plausible causal levers that move a target metric — before any feature or product decision.

Path: Problem → Metric → Mechanism Taxonomy → Parallel Exploration → Ranked List → mechanism-space.md

The key move: enumerate mechanisms (causal levers), not features. Features implement mechanisms. Most mechanisms will be trash. That's expected — mine for the few that aren't.

When NOT to Use

• Features are already decided — go to agent-skills:spec-driven-development
• You're debugging or implementing — this is a discovery skill only

Process

Phase 1: Interactive Intake

Ask these one at a time, waiting for each answer. Push back on vague answers before moving on.

1. Business/project — what is it, what industry, what context?
2. Target metric — one metric only. Must be: observable, measurable, causal, frequent. Push back if the user says "engagement" or "productivity" — those are slogans. Good examples: pickup rate, accepted PRs/week, claims accepted first-pass, chair utilization.
3. Economic equation — how does this metric connect to revenue, cost, or risk? Help the user write it. Example: Revenue = Calls × P(pickup) × P(qualified) × P(close) × Deal Value
4. Workflow — walk the states from input to outcome. Where do things wait, fail, or leak value?
5. Actors — who acts? Customer, employee, system, vendor, regulator?
6. Known constraints — compliance, data availability, integrations, budget, human ops requirements?

Phase 2: Derive Mechanism Taxonomy

Before spawning agents, derive the domain-specific mechanism families. Ask:

• What system type is this? (queueing system, sales funnel, marketplace, compliance workflow, knowledge-work process, human-behavior system, logistics network…)
• Which variables in the economic equation are elastic and currently unmanaged?
• Map the workflow: at each arrow between states, ask what could go wrong and what mechanism family lives there.

Check the derived families against the universal axes in mechanism-families-reference.md. Add any missing ones. Aim for 5–8 domain-specific families.

Phase 3: Parallel Agent Exploration

Spawn one agent per mechanism family (or cluster 2 small families). Each agent gets:

• Full context: business, metric, economic equation, workflow, actors, constraints
• Its assigned family/families
• This instruction: "Generate 15 distinct mechanisms. Diverge wildly — algorithmic, regulatory, psychological, infrastructural, behavioral. Do NOT self-censor for feasibility."
• Output format per mechanism: name | causal hypothesis | equation variable affected | required data | experiment design | expected upside | key risks

Collect all outputs. Expect duplicates and trash — that's normal.

Phase 4: Synthesize and Rank

Merge outputs. Remove hard duplicates. Score each remaining mechanism:

Score = (V × C × D × T) / (R + F + K)

V = economic value if it works
C = causal plausibility (does the hypothesis hold?)
D = data availability to validate it
T = testability (can it be A/B tested or replay-simulated?)
R = regulatory/compliance risk
F = implementation friction
K = complexity

Rank top 15. Annotate why each made the cut.

Phase 5: Write mechanism-space.md

Write to the project root as mechanism-space.md. Structure:

1. System model — actors, workflow states, key bottlenecks
2. Economic equation
3. Mechanism taxonomy — the derived families with one-line rationale for each
4. Full mechanism list — all non-duplicate mechanisms grouped by family
5. Top 15 ranked — scores, rationale, required data per mechanism
6. Data requirements — what historical data is needed to validate each top mechanism

Common Mistakes

Mistake	Fix
Vague metric ("improve engagement")	Push until it's measurable. No exceptions.
Jumping to features	Name the causal mechanism first; features implement mechanisms
Single mechanism family	Force orthogonal divergence across all relevant families
Skipping the economic equation	Without it, mechanisms have no value grounding
Treating all mechanisms as equal	Score everything; most will be discarded
Accepting "this is feasible" as a filter during generation	Feasibility filtering happens at ranking, not generation

Verification: Is the Mechanism Space Good?

A good mechanism space has these properties:

1. Contains non-obvious levers (not just "make a dashboard" or "send an email")
2. Every mechanism ties to a specific variable in the economic equation
3. Each mechanism implies a simulation or experiment — if you can't imagine testing it, it's a story not a mechanism
4. The exploration reveals why the problem is hard (data gaps, incentive misalignment, regulatory limits)
5. The top mechanisms point clearly to what to build next