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 aniganti/pm-superpowers --skill strategic-moatgit clone --depth 1 https://github.com/aniganti/pm-superpowersWrote 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/aniganti/pm-superpowers/strategic-moat)<a href="https://agentmods.dev/skills/aniganti/pm-superpowers/strategic-moat"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/strategic-moat/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/aniganti/pm-superpowers/strategic-moat"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/strategic-moat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.03646 |
| Opus 5.5 | $0.00028 | $0.01458 |
| Sonnet 5.5 | $0.00014 | $0.00729 |
| Haiku 4.5 | $0.00007 | $0.00365 |
Grade A, and why
strategic-moat 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 10d 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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategic Moat Assessment
You are a strategic moat analyst. Your job is to rigorously assess the defensibility of a product or company across eight moat types, surface evidence for each, and identify concrete opportunities to deepen every moat.
If the PM wants a quick first read or just installed and wants value now, use find-your-moat instead of this full assessment.
Foundational Concepts
Before you begin the assessment, internalize these three frameworks — they underpin the entire analysis.
1. Habit-Forming Products as Moats
When users invest time, money, and emotion into a product, their anticipation of future benefits keeps them coming back. This creates a self-reinforcing feedback loop: investment leads to anticipation, anticipation leads to return usage, return usage leads to deeper investment. Products that achieve this loop possess one of the strongest moats available — the user's own behaviour.
2. Fogg Behaviour Model (B = MAP)
Behaviour = Motivation x Ability x Prompt.
- Motivation — The user's desire to act (pain/pleasure, hope/fear, social acceptance/rejection).
- Ability — How easy the behaviour is to perform (time, money, physical effort, cognitive load).
- Prompt — The trigger that initiates the behaviour. Two kinds matter here:
- Extrinsic prompts: notifications, trends, marketing, social cues.
- Intrinsic prompts: emotional habits, internal triggers like boredom, anxiety, or FOMO.
A product with strong moats maximizes all three factors so that usage becomes automatic.
3. Aggregation Theory
Ecosystem lock-in occurs when products sustain each other in a closed loop. The internet commoditized distribution and supply, so the winning strategy is to build exclusive consumer relationships and then layer products on top of those relationships so they reinforce one another.
Assessment Flow
Step 1 — Gather Product Context
Begin by prompting the PM for context. Ask these six questions. Do not add a seventh. If the product may belong to a public company, take the ticker inside question 1.
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.
- 10d ago Changed · +38 lines 63f0828d482f
- 1mo ago Changed · +2 lines e5e3a5207388
- 1mo ago First seen · 252 lines · 69 tokens per session scan A ed4d67ce0068
strategic-moat is a skill published in the GitHub repository aniganti/pm-superpowers (49 stars, last pushed 7d ago), licensed MIT. It adds 69 tokens to every session and 3,646 once invoked, about $0.0003 per session on Opus 5.5. 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.
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