firefrost-gaming/claude-skills-reference

Files

Reza Rezvani 2f57ef8948 feat(agenthub): add AgentHub plugin with cross-domain examples, SEO optimization, and docs site fixes

- AgentHub: 13 files updated with non-engineering examples (content drafts,
  research, strategy) — engineering stays primary, cross-domain secondary
- AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent,
  dry_run.py validation (57 checks)
- Marketplace: agenthub entry added with cross-domain keywords, engineering
  POWERFUL updated (25→30), product (12→13), counts synced across all configs
- SEO: generate-docs.py now produces keyword-rich <title> tags and meta
  descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name
  propagates to all 276 HTML pages
- SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.),
  slug-as-title cleanup, domain label stripping from titles
- Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts
  references/, scripts/, assets/ links to GitHub source URLs; skills/index.md
  phantom slugs fixed (6 marketing, 7 RA/QM)
- Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands,
  21 plugins — consistent across CLAUDE.md, README.md, docs/index.md,
  marketplace.json, getting-started.md, mkdocs.yml
- Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-17 12:10:46 +01:00

3.8 KiB

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title, description

title	description
Product Discovery — Agent Skill for Product Teams	Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw.

Product Discovery

:material-lightbulb-outline: Product :material-identifier: `product-discovery` :material-github: Source

Install: claude /plugin install product-skills

Run structured discovery to identify high-value opportunities and de-risk product bets.

When To Use

Use this skill for:

Opportunity Solution Tree facilitation
Assumption mapping and test planning
Problem validation interviews and evidence synthesis
Solution validation with prototypes/experiments
Discovery sprint planning and outputs

Core Discovery Workflow

Define desired outcome

Set one measurable outcome to improve.
Establish baseline and target horizon.

Build Opportunity Solution Tree (OST)

Outcome -> opportunities -> solution ideas -> experiments
Keep opportunities grounded in user evidence, not internal opinions.

Map assumptions

Identify desirability, viability, feasibility, and usability assumptions.
Score assumptions by risk and certainty.

Use:

python3 scripts/assumption_mapper.py assumptions.csv

Validate the problem

Conduct interviews and behavior analysis.
Confirm frequency, severity, and willingness to solve.
Reject weak opportunities early.

Validate the solution

Prototype before building.
Run concept, usability, and value tests.
Measure behavior, not only stated preference.

Plan discovery sprint

1-2 week cycle with explicit hypotheses
Daily evidence reviews
End with decision: proceed, pivot, or stop

Opportunity Solution Tree (Teresa Torres)

Structure:

Outcome: metric you want to move
Opportunities: unmet customer needs/pains
Solutions: candidate interventions
Experiments: fastest learning actions

Quality checks:

At least 3 distinct opportunities before converging.
At least 2 experiments per top opportunity.
Tie every branch to evidence source.

Assumption Mapping

Assumption categories:

Desirability: users want this
Viability: business value exists
Feasibility: team can build/operate it
Usability: users can successfully use it

Prioritization rule:

High risk + low certainty assumptions are tested first.

Problem Validation Techniques

Problem interviews focused on current behavior
Journey friction mapping
Support ticket and sales-call synthesis
Behavioral analytics triangulation

Evidence threshold examples:

Same pain repeated across multiple target users
Observable workaround behavior
Measurable cost of current pain

Solution Validation Techniques

Concept tests (value proposition comprehension)
Prototype usability tests (task success/time-to-complete)
Fake door or concierge tests (demand signal)
Limited beta cohorts (retention/activation signals)

Discovery Sprint Planning

Suggested 10-day structure:

Day 1-2: Outcome + opportunity framing
Day 3-4: Assumption mapping + test design
Day 5-7: Problem and solution tests
Day 8-9: Evidence synthesis + decision options
Day 10: Stakeholder decision review

Tooling

`scripts/assumption_mapper.py`

CLI utility that:

reads assumptions from CSV or inline input
scores risk/certainty priority
emits prioritized test plan with suggested test types

See references/discovery-frameworks.md for framework details.

3.8 KiB Raw Blame History