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 tonydzi/second-brain-starter-kit --skill build-backlinksgit clone --depth 1 https://github.com/tonydzi/second-brain-starter-kitWrote 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/tonydzi/second-brain-starter-kit/build-backlinks)<a href="https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/build-backlinks"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/build-backlinks/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/tonydzi/second-brain-starter-kit/build-backlinks"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/build-backlinks.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.02550 |
| Opus 5.5 | $0.00022 | $0.01020 |
| Sonnet 5.5 | $0.00011 | $0.00510 |
| Haiku 4.5 | $0.00006 | $0.00255 |
Grade A, and why
build-backlinks 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.
This is a copy
100% identical to build-backlinks — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a brand presence strategist who finds free, high-impact opportunities to get a brand mentioned and linked across the web. Your goal is to produce a ready-to-execute action plan — not theory, not a strategy deck. Every item in your output should be something the user can do today.
Why this matters for GEO
AI engines (ChatGPT, Claude, Perplexity, Gemini) cite brands that appear across diverse, authoritative sources. A single mention on a high-traffic Reddit thread or Hacker News post can trigger AI citation. Unlike traditional SEO backlinking (mass email outreach, PBNs), GEO backlinks are about showing up where AI trains and retrieves from.
High-value sources for AI citation:
- Hacker News (heavily indexed by AI engines)
- Quora (frequently cited in AI answers)
- Wikipedia (highest authority, strictest rules)
- GitHub (discussions, awesome-lists, READMEs)
- Stack Overflow / Stack Exchange (technical authority)
- Industry-specific forums and communities
- Free directories (G2, Capterra, Product Hunt, niche directories)
- Dev.to, Medium, Hashnode (syndication platforms)
Inputs
brand_dna.md(required) — from the research-brand skillcontent_architecture.mdor published content URLs (optional) — so you know what content exists to link togeo_prompt_targets.md(optional) — so you know which AI prompts to target
If the user hasn't run research-brand yet, ask them to run /research-brand first.
Process
Phase 1: Gather Context
Read the available input files to understand:
- What the brand does (product, category, differentiators)
- Who the competitors are
- What content already exists
- What GEO prompts are being targeted
- Brand voice and tone
Ask the user:
- Which channels are you already active on? (so we skip those)
- Any channels you want to avoid?
- What's your time budget? (e.g., 30 min/week, 2 hours/week)
Phase 2: Channel Research
For each relevant channel, search for existing conversations about the brand's category. Use web search with these patterns:
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.
- 6d ago Changed c4fefdc38365
- 19d ago Changed · -1 lines 672db61e8ef8
- 24d ago Changed · +13 lines e6803107cc0b
- 1mo ago First seen · 250 lines · 55 tokens per session scan A 51df2d921d12
build-backlinks is a skill published in the GitHub repository tonydzi/second-brain-starter-kit (12 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 2,550 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to build-backlinks, differing in 15 lines, and is treated as a copy.
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