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problem-space

problem-space

github.com/open-horizon-labsAgent workflows3112 installsUpdated 12w ago
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About problem-space

problem-space is a skill for mapping objectives, constraints, systems, assumptions, and evidence before implementation. It helps teams clarify uncertain or repeatedly blocked work and identify what could invalidate their understanding.

Key features

  1. 01Define outcome-focused objectives
  2. 02Classify hard, soft, and assumed constraints
  3. 03Map systems and affected parties
  4. 04Connect evidence, assumptions, and questions
  5. 05Identify signals that challenge the model

Use cases

  1. 01Frame a project before implementation
  2. 02Investigate recurring blockers or patches
  3. 03Revisit estimates that are repeatedly wrong

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/open-horizon-labs/skills --skill problem-space

After installing, type/problem-spacein Claude Code to confirm it loaded.

Bundled filesagents/openai.yaml

Details

From the project README

/problem-space

Map the terrain where solutions live: objective, constraints, systems, assumptions, and what would prove the map wrong.

Problem space precedes solution space. Jump to code too early and you build the wrong thing fast.

When to Use

  • Starting new work before implementation
  • Repeated blockers or patches keep appearing
  • Estimates are badly off
  • Constraints are being hand-waved away

Skip when: The problem is well-understood and you are in execution.

The Problem Space Process

Step 1: State the Objective

"We are optimizing for [outcome]."

Name the behavior change, metric, or problem disappearance that indicates success. Features are not objectives.

Step 2: Map Constraints

List what is treated as fixed:

Constraint: [boundary]
Type: [hard | soft | assumed]
Reason: [why it exists]
Questioning: [could this be false?]

Hard constraints do not bend. Soft and assumed constraints can be questioned. Do not talk yourself out of real constraints because implementation feels easy.

Step 3: Identify Terrain

Capture only terrain that changes decisions:

  • systems involved;
  • affected users/operators/downstream systems;
  • blast radius if wrong;
  • precedents or local metis.

If RNA/OH context tools are available, surface guardrails and metis as candidates with provenance; the human selects what carries forward.

Step 4: Build the Situation Model

Connect objective, terrain, constraints, assumptions, evidence quality, and open questions. Facts with no decision impact are noise.

Step 5: Stress the Map

Make hidden assumptions visible. Check for X-Y mismatch. Name frame-stress signals: evidence that constraints, terrain, assumptions, or precedents were wrong.

If constraints, assumptions, or open questions remain implicit, you are not ready for /solution-space.

Output Format

## Problem Space

**Scope:** [what area this covers]

### Objective
[Outcome, not feature]

### Constraints
| Constraint | Type | Reason | Question? |
|------------|------|--------|-----------|
| [boundary] | hard/soft/assumed | [why] | [could this be false?] |

### Terrain
- **Systems:** [what's involved]
- **Stakeholders:** [who's affected]
- **Blast radius:** [what breaks if wrong]
- **Precedents/metis:** [existing local knowledge]

### Situation Model
- **Explains:** [why these elements matter]
- **May hide:** [blind spots]
- **Evidence quality:** [observed / inferred / assumed]

### Assumptions and Open Questions
- [assumption or unknown] — [risk if false / when to answer]

### Frame-Stress Signals
- [evidence that should trigger problem-space revision]

### Ready for Solution Space?
[yes/no] - [why or what's missing]

Session Handoff

If a session file is in use, read prior Aim, then replace or append ## Problem Space. Later phases carry forward explicit constraints, assumptions, evidence quality, frame-stress signals, and open questions.

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