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 sandbaseai/sandbase-skills --skill competitor-content-intelligencegit clone --depth 1 https://github.com/sandbaseai/sandbase-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/sandbaseai/sandbase-skills/competitor-content-intelligence)<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/competitor-content-intelligence"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/competitor-content-intelligence.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.01884 |
| Opus 5 | $0.00024 | $0.00942 |
| Sonnet 5 | $0.00010 | $0.00377 |
| Haiku 4.5 | $0.00005 | $0.00188 |
Grade A, and why
competitor-content-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 8d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Content Intelligence
Compare public content evidence to identify differentiated editorial and landing-page opportunities. This Skill calls the named search, extraction, and analysis capabilities in the SandBase API map through the SandBase MCP gateway. In a SandBase Agent, run the capabilities directly. In another compatible agent, require an authorized SandBase connection before starting; never request, print, or store an API key in the research output.
Read example workflows when the user needs a starting prompt or wants to understand the output.
Operating principles
- Focus on content strategy differentiation, not content copying. The goal is to find what's missing, underserved, or poorly served.
- Treat web data and content analysis as evidence; treat gap identification, angle suggestions, and content briefs as judgment clearly separated from observations.
- Respect intellectual property: do not reproduce competitor text beyond short necessary excerpts for analysis.
- Select geography, language, and audience deliberately. A gap in one market may be saturated in another.
- Optimize for content that serves the target's audience and conversion goals, not content volume alone.
- Keep user strategy, positioning, and competitor lists confidential unless sharing is explicitly requested.
Workflow
1. Frame the analysis
Collect or infer: the target audience and their problems, the topic or category, 2–5 competitor domains or brands, the target's positioning and differentiation, content types in scope (blog, landing page, documentation, comparison, tool), and the intended use of findings (content calendar, brief backlog, editorial strategy).
Classify the request as one or more of:
- Coverage mapping: what topics do competitors cover vs. not?
- Depth analysis: where is competitor content superficial or outdated?
- Angle discovery: what perspectives, formats, or audiences are underserved?
- Question gap: what buyer questions remain unanswered across competitors?
- Format opportunity: what content types are missing (interactive, data, video, tool)?
What ships with it
2 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.
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.
- 8d ago First seen · 165 lines · 49 tokens per session scan A 9f965d74b2cc
competitor-content-intelligence is a skill published in the GitHub repository sandbaseai/sandbase-skills (132 stars, last pushed 4d ago), licensed Apache-2.0. It adds 49 tokens to every session and 1,884 once invoked, about $0.0002 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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