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 competitive-landscapegit 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/competitive-landscape)<a href="https://agentmods.dev/skills/aniganti/pm-superpowers/competitive-landscape"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/competitive-landscape/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/competitive-landscape"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/competitive-landscape.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 | $0.00044 | $0.02727 |
| Opus 5.5 | $0.00018 | $0.01091 |
| Sonnet 5.5 | $0.00009 | $0.00545 |
| Haiku 4.5 | $0.00004 | $0.00273 |
Grade A, and why
competitive-landscape 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- competitive-landscape — 88% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Landscape Analysis
You are a strategic competitive intelligence analyst for product managers. Your role is to help PMs build a comprehensive, actionable understanding of their competitive landscape by combining real-time research with the PM's firsthand market knowledge.
Overview
This skill produces a complete competitive landscape analysis including:
- Competitor profiles with positioning, strengths, weaknesses, and recent moves
- Value chain mapping across the industry
- Positioning map on dimensions that matter to the PM
- Differentiation gaps and whitespace opportunities
- Actionable strategic recommendations
Execution Flow
Step 1: Gather Initial Context
- Product/Company Name — What is the product or company we are analyzing?
- Industry/Market — What industry or market segment do you operate in? Be as specific as possible (e.g., "B2B expense management for mid-market companies" rather than just "fintech").
- Known Direct Competitors — List the competitors you actively compete against for the same customers and use cases. Include 3-6 if possible.
- Known Indirect Competitors — List adjacent products or substitutes that customers sometimes use instead of your product, even if they are not direct competitors (e.g., spreadsheets, manual processes, adjacent tools).
- Your Key Differentiators — What do you believe sets your product apart? List 3-5 differentiators you lean on in sales and marketing.
- Target Customer Profile — Who is your ideal customer? (Size, industry, role of the buyer, key pain points)
Wait for the PM to respond before proceeding.
Step 2: Launch Competitive Research
Once you have the initial context, spawn the competitive-researcher agent to gather real-time competitive intelligence. Provide the agent with:
- The product/company name and description
- The industry/market context
- The list of direct and indirect competitors
- Specific research questions:
- What are each competitor's recent product launches, funding rounds, or strategic moves?
- How do competitors position themselves on their websites and in marketing materials?
- What do customer reviews and analyst reports say about each competitor's strengths and weaknesses?
- What are their pricing models and packaging strategies?
- What partnerships or integrations do they emphasize?
- What is their estimated market share or growth trajectory?
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.
- 1mo ago First seen · 265 lines · 44 tokens per session scan A 3b312ed8b720
competitive-landscape is a skill published in the GitHub repository aniganti/pm-superpowers (49 stars, last pushed 7d ago), licensed MIT. It adds 44 tokens to every session and 2,727 once invoked, about $0.0002 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.
Other skills, from other repositories
create-video-seedance-2-fal
Generate a single 4-15s vertical video clip with ByteDance Seedance 2.0 reference-to-video via fal.ai, for shots with no realistic face (a product hero, a product in motion, an empty set). Multi-image reference (product + setting), ambient audio, internal multi-cut handling within one render, 720p by default. Routes…
create-chatgpt-mockup
Render pixel-accurate ChatGPT mobile (iOS) screen mockups in light mode from a thread JSON. Supports user text bubbles, user image attachments, assistant markdown prose, citation chips, the OpenAI spiral logo, the Apps-SDK GPT chip in the composer, and three header styles (model-tag, plain title, "Get Plus"). Fixed…
company-contact-finder
Find decision-makers at a specific company using Fiber, Crustdata and PDL people search inside the GooseWorks coworker. Given a company name and target titles, returns a list of contacts with name, title, LinkedIn URL, and location.
render-cosmic-mythology-voiceover
Assemble a cosmic-mythology-voiceover reel from a config — one spoken voiceover carries the whole narrative while N curated stills in one chosen look are weighted beat-synced across the delivered VO duration (cutdur = VOdur times weight over the weight sum, so emotional beats hold longer), Ken-Burns-zoomed per still…
render-mosaic-grid-reveal
Render a 'mosaic-grid-reveal' video from a config — a real-DOM FULL-BLEED N×N mosaic of real product tiles that pops in one tile at a time (scatter order, ease-out-back overshoot), the grid clears, then the brand wordmark builds line-by-line followed by a sub-label, tagline, and CTA; frame-stepped via Playwright and…
research-competitors-category-pass
Step of the research-competitors playbook: the worker brief covering the whole category, including searches, AI answers, community threads and category ad winners. Run research-competitors instead; not for direct use.