← Files MightShapeARCHIVED FILE
skills/mightshape/references/stage-intake.md
4.32 KB · Oct 3, 2026 · 06:31 UTC
# Intake — MightShape orchestration Intake is MightShape's own orchestration layer, not one of the five design thinking modes. It preserves momentum while identifying whether the request needs facilitation, research, a prototype, or straightforward execution. ## Compact extraction From the user's own words, draft rather than interrogate: - **Proposed solution:** the artifact/intervention already imagined. - **Implied user:** who experiences value, burden, or change. - **Implied problem:** what the solution is assumed to address. - **Desired outcome:** the human or system change, not delivery of the artifact. - **Evidence:** with provenance; user claims remain `USER_PROVIDED`. - **Assumptions:** what must be true for the proposal to work. - **Unknowns:** what could materially change the frame or intervention. - **Constraints:** time, money, policy, environment, access, capability. - **Reversibility:** easy, moderate, or difficult to undo. - **Cost of being wrong:** human, financial, operational, institutional, safety, or opportunity cost. - **Solution lock-in:** `LOW`, `MEDIUM`, or `HIGH`, with reasons. ## Response pattern 1. Infer the starting point and current decision from the user's words when possible: `EARLY_HUNCH`, `GROUNDED_EXPLORATION`, `FRAMED_CHALLENGE`, `CONCEPT`, `PROTOTYPE`, `LIVE`, or `UNSURE`. Record whether it was `USER_DECLARED` or `INFERRED`; never confuse it with the current mode. For a sustained project, persist it with `python3 <skill>/scripts/dc.py orient --project-root <root> --starting-point <value> --basis <value> [--current-decision <decision>]`; omitting the decision preserves any existing focus. 2. Acknowledge the useful energy or intent without endorsing untested claims. 3. Recast a proposal as a candidate solution when relevant. 4. Show no more than five pivotal assumptions/unknowns. 5. Choose depth and the smallest next move from the current decision—not from a requirement to start at Empathize. 6. Ask at most one high-yield question when the answer would change the route. If explicit invocation supplies no usable context, ask which plain-language starting point best fits and include “not sure”; otherwise infer and proceed without a redundant question. For a `LIVE` product or service, name the current strategic fork before proposing another surface. Separate the outcome people need from the locus of the intervention: the product may need to become the destination, bridge into an accepted venue, support a private/incumbent mode, or change a role or process. Existing maturity and evidence narrow the inquiry; they do not make one locus inevitable. Example high-yield question: “Tell me about the last real incident that made you want this—what happened, who was involved, and what did they do next?” ## Do not be obstructive Proceed directly when requirements are explicit and risk is low. Do not validate universal/common-sense premises for ceremony. If the user explicitly wants brainstorming and supplies demand claims, label them `USER_PROVIDED`, honor protected divergence, and avoid turning the turn into a research lecture. ## AI-product Intake Add these checks only when relevant: - Is AI necessary, or can a rule, workflow, interface, or human service create the value? - Is the goal augmentation, recommendation, delegation, or full automation? - Who can override, appeal, recover, or understand uncertainty? - What errors are tolerable; which are not? - What data exists, who controls it, and how representative/reliable is it? - What happens when the model is wrong, unavailable, or confidently ambiguous? - Does the product create dependence or deskilling? - What remains valuable when AI is removed? ## Transition Starting point does not dictate transition. A live product may return to Empathize or Define; an early hunch may move directly to a low-fidelity Prototype when making is the cheapest inquiry. - To `EMPATHIZE` when behavior, context, stakeholders, or workflow is insufficiently known. - To `DEFINE` when grounded evidence exists but the frame is weak or contested. - To `IDEATE` when a strong solution-independent POV exists and breadth is missing. - To `PROTOTYPE` when a candidate concept exists and making is the cheapest inquiry. - To `TEST` when a learning artifact already exists. - To ordinary implementation when the request is explicit and no material design uncertainty remains.
SHA-256: 3cf6c34e62ad99351f21dbcbf14cb4dc8ea35799b8e423dd76046e73849b4f90