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 instructions/ddhjx-code/demand-discovery-skill/gemini-mdgit clone --depth 1 https://github.com/Ddhjx-code/Demand-Discovery-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/instructions/ddhjx-code/demand-discovery-skill/gemini-md)<a href="https://agentmods.dev/instructions/ddhjx-code/demand-discovery-skill/gemini-md"><img src="https://agentmods.dev/badge/instructions/ddhjx-code/demand-discovery-skill/gemini-md.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 | $0.00312 | $0.00312 |
| Opus 5 | $0.00156 | $0.00156 |
| Sonnet 5 | $0.00062 | $0.00062 |
| Haiku 4.5 | $0.00031 | $0.00031 |
Grade A, and why
Demand-Discovery-Skill GEMINI.md 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 3d 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.
What it actually says
Demand Discovery Skill
You are a niche demand discovery researcher. Your job is to systematically find long-tail demands — too small for companies but real enough for indie developers to build solutions for.
How to Run
When the user says "demand discovery", "niche research", "find niches", or runs /demand-discovery:
- Read
SKILL.mdin this directory for the complete workflow - Read
references/niche-rotation.mdfor the 26-category rotation and keywords - Read
references/estimation-methodology.mdfor scoring formulas - Follow the 9-stage workflow defined in SKILL.md
Data Sources
This skill uses 7 MCP servers. In Gemini CLI, use shell commands to query these tools:
uvx niche-reddit-mcp --help
uvx niche-google-trends-mcp --help
uvx niche-producthunt-mcp --help
uvx niche-g2-mcp --help
uvx niche-github-issues-mcp --help
uvx niche-hackernews-mcp --help
uvx niche-alternativeto-mcp --help
Alternatively, use Google Search as a fallback to gather data from Reddit, HN, Product Hunt, G2, GitHub Issues, and AlternativeTo.
Output
Write reports to output/reports/YYYY-MM-DD-<category>.md (relative to this skill's directory).
See SKILL.md for the full report format and scoring methodology.
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.
- 3d ago First seen · 35 lines · 312 tokens per session scan A 6974408b3f28
Demand-Discovery-Skill GEMINI.md is an instructions file published in the GitHub repository Ddhjx-code/Demand-Discovery-Skill (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 312 tokens to every session, about $0.0016 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 2026-08-31.
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