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Product design harness

Skill cis2042/product-design-harness/skills/product-design-harness

Agentic Design Process with Designer-in-the-Loop product judgment across User, Evidence, and Business flows.

Install
npx -y skills add cis2042/product-design-harness --skill product-design-harness

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • 27 days oldThe repository was created 27 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 12 stars12 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when a product direction, feature, workflow, experiment, launch, feedback signal, automation, or scale decision needs judgment before execution or the next iteration.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.7 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Product Design Harness

Decide whether a product direction should continue, be verified, or be reframed anywhere in the product loop.

Execution includes design, implementation, prototype, launch, growth, automation, or investment.

Start

Load in this order:

  1. resources/schemas/session-config.schema.json: set working_language; keep canonical_identifiers in English.
  2. resources/knowledge/ontology.json: use the canonical UX3 knowledge kernel and its approved Flow definitions.
  3. resources/knowledge/rules.json: load required rules plus only triggered conditional rules.
  4. resources/prompts/start-review.md.

Then fill resources/templates/product-brief.md, select the smallest responsible mode, and return resources/schemas/review-result.schema.json.

Validate every review result with resources/scripts/check_review.py. JSON Schema checks shape and conditional fields; check_review.py is the canonical validator for the worst-verdict, weakest-flow, and headline-tier semantic checks.

Modes

ModeUse when
Quick GateSmall, reversible, low-risk change.
Standard GateNew feature, workflow, or experiment.
UX3 CouncilHigh uncertainty, external evidence, meaningful risk, multiple reviewers, or human-owned trade-off.

Handoff is not a review mode. When a prior review already returned continue, reuse its context pack instead of running a new review.

Reversibility lowers mode, not the review requirement.

UX3

Review three connected dimensions:

FlowCore question
User FlowWho is the product for, who is affected, what are they trying to accomplish, and what cost or loss of control exists now?
Evidence FlowWhat signal was observed, where did it come from, what interpretation is being made, what counter-signal weakens it, and what decision impact follows?
Business FlowWho creates value, receives value, pays or decides, bears cost or risk, and can the exchange remain viable and legitimate?

Then inspect the intersections: Situated Understanding, Viable Learning, and Sustainable Value Exchange.

UX3 Decision Kernel

Apply the same reduction sequence to every submitted review:

  1. Validate lane contracts and evidence references with resources/scripts/check_review.py.
  2. Reduce to the worst verdict and deterministic weakest flow.
  3. Gate uncertainty, risk, reversibility, and human-owned calls.
  4. Emit one canonical verdict, next action, execution boundary, and stop conditions.

Do not average lanes. Human-owned calls are recorded through an accountable decision record; they are not converted into a machine score.

Before handoff, name product organization, issuer, stakeholder effects, and accountable owner. Missing issuer or owner blocks implementation.

Operating Rules

  • Do not implement during review.
  • verify branch: verify authorizes only the exact proof_step and never general coding.
  • continue branch: continue plus a valid context pack authorizes implementation.
  • Every result states may_do, must_not_do, and must_ask.
  • List disconfirming evidence before support.
  • Do not average reviewer verdicts.
  • The weakest flow controls the next action.
  • External tools return evidence receipts, not verdicts.
  • Evidence tier controls action size; confidence does not upgrade weak proof.
  • Council mode requires independent reviews and a challenge round.
  • If all independent Council reviews return continue, a valid resources/schemas/red-team-review.schema.json result must be attached to the Council input before synthesis.
  • Designer taste is a human-owned input, never an evidence score.
  • Incoming feedback must be classified before it changes direction or work.
  • Human-owned decisions stay human-owned.

Triggered Distilled Rules

Load only rules whose observable trigger applies. These families refine the three Flow reviews; they do not create new verdicts or allow a strong Flow to outvote a weak one.

SituationRequired rule
Mental-model mismatch, hidden capability, or misunderstood consequenceux3.rule.mental_model_alignment
Cognitive or recovery burden across human, task, tool, and environmentux3.rule.human_factors_load
Research claim, synthetic user, simulation, interview, survey, test, or analytics resultux3.rule.research_validity
Value proposition, positioning, buyer, channel, or GTM path before fit is provenux3.rule.value_market_path
Agent plan or tool action that needs visibility, control, handoff, or recoveryux3.rule.human_agent_interaction
Retention, personalization, or gamification that changes repeated behaviorux3.rule.motivation_ethics

Outcome

VerdictRequired next step
continueCreate resources/templates/context-pack.md within the execution boundary.
verifyRun one proof step only.
stop_reframeStop execution and return a better product question.

Use triggered rule IDs for detail instead of duplicating every rule in prompts. Common triggers include ux3.rule.actor_boundary, ux3.rule.problem_hypothesis, ux3.rule.evidence_separation, ux3.rule.minimum_validated_proof, ux3.rule.feedback_classification, and ux3.rule.human_judgment. The distilled trigger table above covers additional rules that activate only when their specific product condition appears.

Use resources/docs/HARNESS.md for the handbook, resources/docs/OPERATING-PROTOCOL.md for the six-gate protocol, resources/docs/CONTRACTS.md for output rules, and resources/examples/ for worked reviews.

What ships with it: 85 files

298.8 KB alongside SKILL.md, 2 of them executable

45 more files not listed here. See all 85 in the repository.

Gives 0 of the 12 instructions most design frontend skills give in ~1.2k tokens

Counted across 1,169 of the 1,878 authors here whose files we hold, read 2026-08-07

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Said here and by no other author read

  • load configuration files in specified order
  • select smallest responsible review mode
  • review user evidence and business dimensions
  • inspect dimension intersections
  • reduce review to worst verdict
  • gate uncertainty and risk

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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