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 agentmods add skills/matrixfounder/agentic-development/skill-product-analysisnpx skills add MatrixFounder/Agentic-development --skill skill-product-analysisgit clone --depth 1 https://github.com/MatrixFounder/Agentic-developmentWhat 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 | $0.00015 | $0.00694 |
| Opus 5 | $0.00008 | $0.00347 |
| Sonnet 5 | $0.00003 | $0.00139 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
skill-product-analysis 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 2d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analysis & Vision
1. Objective
To define what we are building and why, before writing code. This skill powers the Product Analyst (p02) role.
2. Core Tooling
You MUST use the provided Python script to scaffold the Vision document.
DO NOT write the PRODUCT_VISION.md header/structure manually.
How to Initialise
Run the following command (Headless mode):
python3 [skill_path]/scripts/init_product.py --name "Product Name" --problem "Problem description" --audience "Users" --metrics "KPI1, KPI2"
Note:
[skill_path]is the path to this skill (e.g..agent/skills/skill-product-analysis).
3. The 10-Factor Scoring Matrix
Before drafting the Vision, you MUST score the idea to determine viability.
Protocol
Run the scoring script with your estimated values (1-10, where 10 is Best/Easiest):
# Example: High problem intensity (9), but weak moat (3)
python3 [skill_path]/scripts/score_product.py --problem_intensity 9 --moat_durability 3 --market_size 8
- Goal: Score > 70/100.
- Output: Application of this script must be included in
PRODUCT_VISION.md.
4. Artifact Standards (PRODUCT_VISION.md)
Template Structure
See assets/vision_template.md for the authoritative structure.
All sections are mandatory.
INVEST Criteria (User Stories)
When breaking down the vision into the Backlog:
- Independent
- Negotiable
- Valuable (to the user)
- Estimable
- Small
- Testable
4. Frameworks (The "Soul")
Crossing the Chasm (Differentiation)
When defining the product features, you MUST position it for the Early Majority.
- The Beachhead: What is the single specific niche we can dominate?
- The Whole Product: What eco-system/integrations are needed to make it a complete solution?
Emotional Logic (User Centricity)
Every feature implementation plan must start with:
- Trigger: What emotional state is the user in? (e.g. Anxiety about API keys)
- Action: The feature they use.
- Reward: The emotional relief/gain. (e.g. "Safety", "Control")
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 77 lines · 15 tokens per session scan A 33fa79a08f93
skill-product-analysis is a skill published in the GitHub repository MatrixFounder/Agentic-development (5 stars, last pushed 20d ago), licensed Apache-2.0. It adds 15 tokens to every session and 694 once invoked, about $0.0001 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 2026-08-31.
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