# Stg Scoring Problems

> Scores problem candidates using four-property framework (frequency, severity, breadth, alternatives' inadequacy) with compression-model elimination. Use when constructing problem hypothesis during BUILD phase 2.

- **Type:** Skill
- **Install:** `agentstack add skill-bellabe-strategy-os-stg-scoring-problems`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [BellaBe](https://agentstack.voostack.com/s/bellabe)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [BellaBe](https://github.com/BellaBe)
- **Source:** https://github.com/BellaBe/strategy-os/tree/main/.claude/skills/stg-scoring-problems
- **Website:** https://bellabe.github.io/leanos/

## Install

```sh
agentstack add skill-bellabe-strategy-os-stg-scoring-problems
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Problem Scoring

Score problem candidates using four-property framework with compression-model elimination. Every score carries a tier label and cited evidence. Output is a problem hypothesis, not a canvas section.

## Procedure

### Step 1: Load Context [S]

Read: segment hypothesis (or candidates if segment not yet finalized), governor's problem space description, research signals from BUILD phase 1.

Produce: problem generation parameters anchored to segment.

**Gate:** `context_loaded: bool` -- segment context and problem space available.
- Pass: Step 2.
- Fail: Report missing inputs. If no segment data, proceed with governor input alone, noting reduced confidence.

### Step 2: Enumerate Candidate Problems (Minimum 5) [K-grounded]

**Grounded in:** segment context, governor's problem description, research signals.

For the target segment, identify 5-7 candidate problems from:
- Governor input (what they believe the problem is)
- Public signals (forums, reviews, job postings, support tickets)
- Competitive analysis (what alternatives solve -- implies the problem)
- Adjacent segment patterns (problems common in related segments)

WebSearch for segment-specific pain signals.

Produce: candidate problem list.

**Gate:** `candidates_enumerated: bool` -- at least 5 candidates identified from at least 2 different sources.
- Pass: Step 3.
- Fail: If fewer than 5, broaden search to adjacent problem categories. If still 1M potential buyers | T1 if from market sizing; T2 if estimated |
| 4 | 100K-1M | Cross-reference with segment size from stg-segmenting-customers |
| 3 | 10K-100K | |
| 2 | 1K-10K | |
| 1 | 80% satisfaction."

Produce: complete problem hypothesis in register format.

**Gate:** `hypothesis_written: bool` -- claim is solution-independent, kill condition references observable thresholds, possibility space records all candidates.
- Pass: Done.
- Fail: If claim contains solution language, rewrite as pain statement. If kill condition is vague, add specific thresholds.

## Quality Criteria

- Minimum 5 candidate problems enumerated
- Each problem scored on all four properties with tier labels
- Every score cites specific evidence (not "likely high frequency")
- Governor's stated problem is evaluated honestly (not auto-promoted)
- Composite score used for ranking, not as false-precision metric
- Kill condition references specific observable thresholds
- Problem statement is independent of solution ("customers struggle with X" not "customers need a tool that does Y")

## Failure Modes

| Mode | Signal | Recovery |
|------|--------|----------|
| Solution-shaped problem | Problem statement includes solution language ("need a tool", "need a platform") | Rewrite as pain: "teams spend 40 hours building design systems" not "teams need an automated design system generator" |
| Governor's problem auto-promoted | Governor's stated problem has highest score despite weak evidence | Explicitly compare evidence quality between governor's problem and alternatives. If governor's problem has weaker evidence but higher score, flag: "Your stated problem scored highest but evidence is thinner than alternative X. This may be confirmation bias." |
| All problems score similarly | Top 3 problems within 20% of each other | This is useful information, not a failure. Carry all 3 as alternatives. Recommend governor input to distinguish |

## Boundaries

**In scope:** Problem enumeration, four-property scoring with evidence, compression-model elimination, possibility space recording, hypothesis writing in register format.

**Out of scope:** Market sizing (stg-sizing-markets), segment definition (stg-segmenting-customers), competitive mapping (stg-analyzing-competition), solution design (stg-designing-solutions).

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [BellaBe](https://github.com/BellaBe)
- **Source:** [BellaBe/strategy-os](https://github.com/BellaBe/strategy-os)
- **License:** MIT
- **Homepage:** https://bellabe.github.io/leanos/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-bellabe-strategy-os-stg-scoring-problems
- Seller: https://agentstack.voostack.com/s/bellabe
- Browse the marketplace: https://agentstack.voostack.com/browse

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