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 anysiteio/agent-skills --skill anysite-competitor-intelligencegit clone --depth 1 https://github.com/anysiteio/agent-skillsWrote 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/anysiteio/agent-skills/anysite-competitor-intelligence)<a href="https://agentmods.dev/skills/anysiteio/agent-skills/anysite-competitor-intelligence"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-competitor-intelligence/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/anysiteio/agent-skills/anysite-competitor-intelligence"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-competitor-intelligence.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 | $0.00150 | $0.05822 |
| Opus 5 | $0.00075 | $0.02911 |
| Sonnet 5 | $0.00030 | $0.01164 |
| Haiku 4.5 | $0.00015 | $0.00582 |
Grade A, and why
anysite-competitor-intelligence 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 — 728 lines — stays where its author put it; the contents beside it link to each section on GitHub.
anysite Competitor Intelligence
Comprehensive competitive intelligence gathering using anysite MCP server. Track competitors across LinkedIn, social media, and the web to understand their strategies, monitor their activities, and identify competitive opportunities.
Overview
The anysite Competitor Intelligence skill helps you:
- Track competitor companies on LinkedIn and Y Combinator
- Monitor hiring patterns to identify growth areas and strategic priorities
- Analyze content strategies across social platforms
- Benchmark positioning and messaging
- Identify key employees and leadership changes
- Track competitive movements like funding, launches, partnerships
This skill provides 90% coverage of competitive intelligence capabilities with excellent LinkedIn and social media monitoring.
v2 Tool Interface
All data fetching uses the universal execute() meta-tool:
execute(source, category, endpoint, params) → returns data + cache_key
After fetching, use these for analysis and export:
get_page(cache_key, offset, limit)— paginate through large result setsquery_cache(cache_key, conditions, sort_by, aggregate, group_by)— filter/sort/aggregate cached data without re-fetchingexport_data(cache_key, format)— export to CSV, JSON, or JSONL for sharing
Always call discover(source, category) first if unsure about endpoint names or params.
Error handling: Check response for llm_hint field on errors — it provides actionable guidance (e.g., "Likely passed fsd_company URN instead of company: prefix").
Supported Platforms
- LinkedIn (Primary): Company pages, employee search, post monitoring, job listings, growth tracking
- Y Combinator: Startup competitor research, funding data, batch analysis
- Twitter/X: Social presence monitoring, content strategy, engagement analysis
- Reddit: Community sentiment, product discussions, competitor mentions
- Instagram: Brand presence, visual content strategy, influencer partnerships
- YouTube: Video content, channel growth, community engagement
- Web Scraping: Company websites, press releases, blog content
- SEC: Public company filings for competitors
What ships with it
1 file 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.
- 10d ago First seen · 728 lines · 150 tokens per session scan A 65e73ec1fa5e
anysite-competitor-intelligence is a skill published in the GitHub repository anysiteio/agent-skills (19 stars, last pushed 25d ago), licensed MIT. It adds 150 tokens to every session and 5,822 once invoked, about $0.0007 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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