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 inhouseseo/superseo-skills --skill linkbuildinggit clone --depth 1 https://github.com/inhouseseo/superseo-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/inhouseseo/superseo-skills/linkbuilding)<a href="https://agentmods.dev/skills/inhouseseo/superseo-skills/linkbuilding"><img src="https://agentmods.dev/badge/skills/inhouseseo/superseo-skills/linkbuilding/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/inhouseseo/superseo-skills/linkbuilding"><img src="https://agentmods.dev/badge/skills/inhouseseo/superseo-skills/linkbuilding.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 | $0.00041 | $0.01623 |
| Opus 5 | $0.00020 | $0.00812 |
| Sonnet 5 | $0.00008 | $0.00325 |
| Haiku 4.5 | $0.00004 | $0.00162 |
Grade A, and why
linkbuilding 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Link Building
A phase-appropriate link acquisition strategy with specific, executable tactics. The agent classifies your site's authority phase from what it can read (the domain, site age, what's on the homepage), then picks tactics from the 9 detailed playbooks in references/tactics/.
Input
Your domain or a URL (required). Optionally: your niche, what you sell, and any specific constraint ("I have no budget for outreach," "I already tried guest posting," etc.).
User context (optional)
If the user specifies an action, adapt accordingly:
- "find easy link opportunities" → Focus on low-effort, high-conversion tactics: existing relationships, testimonials, citations, entity stacking
- "help me with outreach" → Focus on outreach tactics: guest posting, resource pages. For each, provide search operators and pitch angles.
- "create link-worthy content" → Focus on skyscraper content and statistics pages.
- "plan a full strategy" → Run the full workflow below.
Role
You are a senior link building strategist with 10+ years building backlink profiles across new, growth, and authority-stage sites.
Step 1: Phase Assessment
Fetch the homepage of the domain. Google the brand name. From what you can see:
- Site age — WHOIS or first Wayback Machine snapshot
- Content volume — approximate pages indexed (
site:domain.com) - Brand presence — does Google return a knowledge panel? A company card? Any news coverage?
- Apparent size — solo / small team / established company
Classify the phase:
- Foundation phase: new (< 1 year), thin content, no brand signals, likely DR 0-15
- Growth phase: 1-3 years, 20-100 pages of content, some brand mentions, likely DR 16-40
- Authority phase: 3+ years, established brand, knowledge panel, media mentions, likely DR 41+
State your reasoning, naming which signals you actually observed. If site age or indexed volume can't be verified, classify from the remaining signals and say so — don't present a guess as a measurement. If the signals are ambiguous, load references/phase-classification-tree.md for the full decision tree before falling back on asking the user.
What ships with it
12 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/anchor-text-safety-guide.md 6.8 KB
- references/link-velocity-redflags.md 5.7 KB
- references/phase-classification-tree.md 5.7 KB
- references/tactics/citations-directories.md 6.4 KB
- references/tactics/competitor-backlink-gap.md 6.3 KB
- references/tactics/entity-stacking.md 6.6 KB
- references/tactics/guest-posting.md 6.6 KB
- references/tactics/new-site-launch-strategy.md 41 KB
- references/tactics/podcast-guesting.md 6.3 KB
- references/tactics/resource-pages.md 5.0 KB
- references/tactics/skyscraper-technique.md 5.5 KB
- references/tactics/strategic-partnerships.md 5.5 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.
- 8d ago Changed f56a45951746
- 12d ago First seen · 118 lines · 41 tokens per session scan A 004c20fddaa4
linkbuilding is a skill published in the GitHub repository inhouseseo/superseo-skills (320 stars, last pushed 8d ago), licensed Apache-2.0. It adds 41 tokens to every session and 1,623 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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outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.