Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add assimovt/productskills --skill opportunity-mappinggit clone --depth 1 https://github.com/assimovt/productskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/assimovt/productskills/opportunity-mapping)<a href="https://agentmods.dev/skills/assimovt/productskills/opportunity-mapping"><img src="https://agentmods.dev/badge/skills/assimovt/productskills/opportunity-mapping/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/assimovt/productskills/opportunity-mapping"><img src="https://agentmods.dev/badge/skills/assimovt/productskills/opportunity-mapping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00749 |
| Opus 5 | $0.00023 | $0.00375 |
| Sonnet 5 | $0.00009 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
opportunity-mapping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Map opportunities by connecting business outcomes to customer needs to testable solutions. Teresa Torres' Opportunity Solution Trees (OSTs) prevent the two biggest PM mistakes: building solutions without clear problems, and chasing problems disconnected from business goals.
Opportunity Solution Tree Structure
Build the tree top-down, but fill it bottom-up with evidence:
Desired Outcome (business metric you're trying to move)
|
+-- Opportunity 1 (customer need/pain/desire)
| +-- Solution A
| | +-- Experiment 1
| | +-- Experiment 2
| +-- Solution B
| +-- Experiment 3
|
+-- Opportunity 2
+-- Solution C
+-- Solution D
+-- Experiment 4
Level 1: Desired Outcome
One measurable business outcome. Not a feature, not a project — a metric.
- "Increase 7-day activation rate from 23% to 40%"
- NOT: "Improve onboarding" (not measurable)
Level 2: Opportunities
Customer needs, pain points, or desires that, if addressed, would move the outcome. These come from research — interviews, data, support tickets — not brainstorming.
Rules for good opportunities:
- Framed as customer needs, not product features
- "New users don't understand what to do first" (opportunity)
- NOT "Add an onboarding wizard" (solution masquerading as opportunity)
- Each opportunity is independent — addressing one doesn't depend on another
Level 3: Solutions
Multiple possible solutions for each opportunity. Generate at least 3 before evaluating. The goal is to explore the solution space, not commit to the first idea.
Level 4: Experiments
Small, fast tests to validate whether a solution addresses the opportunity. Experiments should answer: "Does this solution actually solve this opportunity?"
Building the Tree
- Start with the outcome. Align with your team or stakeholders on exactly one outcome to focus on.
- Map opportunities from research. Review interview notes, support tickets, analytics. Cluster evidence into distinct opportunities. Each opportunity needs evidence from 3+ sources.
- Generate solutions per opportunity. Brainstorm at least 3 solutions per opportunity. Include wild ideas — they often reveal assumptions.
- Design experiments per solution. What's the smallest test? Prototype, concierge, Wizard of Oz, fake door, A/B test.
- Prioritize which branch to explore. You can't test everything. Pick the opportunity with strongest evidence and the solution with lowest experiment cost.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 72 lines · 47 tokens per session scan A 3162741dffb3
opportunity-mapping is a skill published in the GitHub repository assimovt/productskills (68 stars, last pushed 6mo ago), licensed MIT. It adds 47 tokens to every session and 749 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
prd-taskmaster
Zero-config goal-to-tasks engine (the Atlas engine). Takes any goal (software, pentest, business, learning), runs adaptive discovery via brainstorming, generates a validated spec, parses into TaskMaster tasks, and hands off to execution. Use when user says "PRD", "product requirements", "I want to build", invokes…
handoff
Phase 3 of the prd-taskmaster pipeline: smart mode selection and user handoff. Detects installed capabilities (superpowers, ralph-loop, task-master-ai, playwright, research providers), recommends ONE execution mode (A/B/C) with reasoned justification, appends the task-execution workflow to CLAUDE.md, surfaces a…
generate
Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholdersfound, grade thresholds), parses the PRD into tasks via task-master, runs TaskMaster's native complexity analysis…
discover
Phase 1 of the prd-taskmaster pipeline: brainstorm-driven discovery. Delegates to superpowers:brainstorming in Interactive Mode (one adaptive question at a time), or self-brainstorms in Autonomous Mode when no user is present. Intercepts before the brainstorming chain hands off to writing-plans — this skill owns the…
execute-fleet
Phase execution skill for licensed Atlas Fleet runs. Use when HANDOFF has selected Atlas Fleet and the project should be executed across isolated launcher worktrees with inbox-based result collection, verified CDD cards, sequential integration merges, and one final PR.
customise-workflow
Customise the prd-taskmaster plugin workflow via curated brainstorm questions. The AI asks, the user answers in plain English, and the skill writes their preferences to .atlas-ai/config/atlas.json. Future runs of prd-taskmaster read that file and apply user preferences to phase gates, validation strictness, default…