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/mverab/egeoagents/competitive-analysisnpx skills add mverab/eGEOagents --skill competitive-analysisgit clone --depth 1 https://github.com/mverab/eGEOagentsWrote 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/mverab/egeoagents/competitive-analysis)<a href="https://agentmods.dev/skills/mverab/egeoagents/competitive-analysis"><img src="https://agentmods.dev/badge/skills/mverab/egeoagents/competitive-analysis.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00561 |
| Opus 5 | $0.00015 | $0.00280 |
| Sonnet 5 | $0.00006 | $0.00112 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
competitive-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 6d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Analysis Skill
When analyzing competition for AI-engine rankings:
Process
1. Identify the Query Space
- What queries would users search?
- What intent do these queries have?
- What type of content would AI engines prefer?
2. Generate Competitor Profiles
Create 5 realistic competitor archetypes:
| Type | Description |
|---|---|
| Market Leader | Established player with strong brand recognition |
| Specialist | Niche focus with deep expertise |
| Budget Option | Price-competitive alternative |
| Innovator | New approach or technology |
| Content King | Best educational/informational content |
3. Evaluate Ranking Factors
For each competitor, assess:
- Content depth and quality
- Social proof (reviews, testimonials, usage stats)
- Authority signals (expertise, credentials)
- User intent alignment
- Technical optimization (schema, structure)
4. Create Comparison Matrix
┌─────────────────────────────────────────────────────────────┐
│ 🏆 COMPETITIVE ANALYSIS │
├─────────────────────────────────────────────────────────────┤
│ │
│ Query: "[analyzed query]" │
│ │
│ RANKING PREDICTION │
│ ────────────────── │
│ #1 Market Leader ████████████ Strong brand + proof │
│ #2 Specialist ██████████ Deep expertise │
│ #3 YOUR CONTENT ████████ [current position] │
│ #4 Content King ██████ Good info, weak CTA │
│ #5 Budget Option ████ Price only │
│ │
│ YOUR DIFFERENTIATION OPPORTUNITY │
│ ───────────────────────────────── │
│ • [specific opportunity 1] │
│ • [specific opportunity 2] │
│ • [specific opportunity 3] │
│ │
└─────────────────────────────────────────────────────────────┘
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.
- 6d ago First seen · 74 lines · 29 tokens per session scan A bce473d53dfb
competitive-analysis is a skill published in the GitHub repository mverab/eGEOagents (173 stars, last pushed 4d ago), licensed MIT. It adds 29 tokens to every session and 561 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 2026-08-30.
Other skills, from other repositories
content-amplifier
Use when the user asks to "amplify influencer content with paid media", "set up whitelisting or Spark Ads", "decide which posts to boost", "repurpose influencer content", "turn one video into multiple ads", or "build a UGC asset library"; produces (paid mode) a content-selection scorecard, a paid amplification…
outreach-manager
Use when the user asks to "write influencer outreach", "follow up with a creator", "pitch a journalist, hunter, or launch partner", or "negotiate partnership terms"; produces personalized pitches, multi-touch follow-up sequences, negotiation scripts with objection handling, and a status pipeline tracker — the shared…
campaign-planner
Use when the user asks to "plan an influencer campaign", "build a campaign blueprint", "track or close a creator campaign", or "record a late campaign correction"; produces the plan and, when requested, a non-canonical evidence tracker with scoped identity, publication, reconciliation, close, and reopen receipts. Not…
cold-outbound-sequencer
Use when the user asks to "build a B2B cold-outbound sequence", "design reply-triage branching", "plan a domain warmup / sending throttle", or "make my outbound CAN-SPAM / opt-in compliant"; produces a multi-step outbound sequence with reply-triage branches (positive / objection / referral / not-now / opt-out), a…
inbox-placement-monitor
Use when the user asks to "track where my emails are actually landing after I send", "read my seed-list inbox vs spam vs promotions results", "trend my Gmail Postmaster / Microsoft SNDS reputation", or "did placement drop after my last send"; produces a per-provider inbox/spam/promotions placement read, a domain/IP…
send-experiment-designer
Use when the user asks to "design an email A/B test", "set up a multivariate subject/CTA test", "run a send-time test", "build a hold-out group", or "is this email result statistically and practically material?"; produces a falsifiable hypothesis, one-variable-per-cell matrix, sample-size/MDE/duration/power plan, and…