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 ferdinandobons/startup-skill --skill startup-competitorsgit clone --depth 1 https://github.com/ferdinandobons/startup-skillWrote 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/ferdinandobons/startup-skill/startup-competitors)<a href="https://agentmods.dev/skills/ferdinandobons/startup-skill/startup-competitors"><img src="https://agentmods.dev/badge/skills/ferdinandobons/startup-skill/startup-competitors.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 | $0.00155 | $0.03570 |
| Opus 5 | $0.00077 | $0.01785 |
| Sonnet 5 | $0.00031 | $0.00714 |
| Haiku 4.5 | $0.00015 | $0.00357 |
Grade A, and why
startup-competitors 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 7d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Startup Competitors
Deep competitive intelligence that goes beyond surface-level profiles. Produces actionable battle cards, pricing landscape analysis, and strategic vulnerability mapping using real web data.
How It Works
INTAKE → RESEARCH (3 sequential waves) → SYNTHESIS → BATTLE CARDS
The process is focused: understand the product, research competitors deeply across 3 dimensions, synthesize findings, and produce actionable output. Typical runtime: 15-25 minutes in Claude Code (parallel agents), 30-45 minutes in Claude.ai (sequential).
Language
Default output language is English. If the user writes in another language or explicitly requests one, use that language for all outputs instead.
Phase 0: Resume Check
Before anything else, check if a PROGRESS.md created by this skill exists in the working directory or a project subdirectory (the skill name field says startup-competitors). If it does, read it and resume from the last incomplete phase. Tell the user: "I found progress from a previous session. You completed [phases]. Picking up from [next phase]."
If no progress file exists — or the one found belongs to a different skill — start from Phase 1.
Phase 1: Intake
Short and focused — 1-2 rounds of questions, not an extended interview. The goal is just enough context to run targeted research.
Check for Prior startup-design Work
Before asking questions, check if a startup-design session has already been completed for this project. Look for these files in the working directory or subdirectories:
01-discovery/competitor-landscape.md— competitor profiles and analysis01-discovery/market-analysis.md— market size, trends, regulatory01-discovery/target-audience.md— customer personas, pain points00-intake/brief.md— product description and context
If these files exist, read them and use the data as a head start:
- Extract the product description, target market, and known competitors from the brief
- Use the competitor list from
competitor-landscape.mdas the starting point for deeper analysis (startup-design profiles 5-8 competitors at surface level — this skill goes much deeper on each) - Pull market size and trends from
market-analysis.mdto contextualize the competitive landscape - Use customer pain points from
target-audience.mdto focus the sentiment mining on what matters most
What ships with it
8 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.
- references/honesty-protocol.md 4.8 KB
- references/research-principles.md 2.8 KB
- references/research-scaling.md 4.3 KB
- references/research-synthesis.md 8.5 KB
- references/research-wave-1-profiles-pricing.md 6.8 KB
- references/research-wave-2-sentiment-mining.md 6.8 KB
- references/research-wave-3-gtm-signals.md 7.2 KB
- references/verification-agent.md 5.1 KB
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.
- 7d ago First seen · 269 lines · 155 tokens per session scan A 79701a39ce42
startup-competitors is a skill published in the GitHub repository ferdinandobons/startup-skill (890 stars, last pushed 2mo ago), licensed MIT. It adds 155 tokens to every session and 3,570 once invoked, about $0.0008 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.
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