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 itallstartedwithaidea/agent-skills --skill knowledge-base-injectiongit clone --depth 1 https://github.com/itallstartedwithaidea/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/itallstartedwithaidea/agent-skills/knowledge-base-injection)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/knowledge-base-injection"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/knowledge-base-injection/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/itallstartedwithaidea/agent-skills/knowledge-base-injection"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/knowledge-base-injection.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 | $0.00039 | $0.02462 |
| Opus 5.5 | $0.00016 | $0.00985 |
| Sonnet 5.5 | $0.00008 | $0.00492 |
| Haiku 4.5 | $0.00004 | $0.00246 |
Grade A, and why
knowledge-base-injection 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 1mo 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Injection
Part of Agent Skills™ by googleadsagent.ai™
Description
Knowledge Base Injection is the technique of dynamically injecting domain expertise into an agent's context at the moment it is most relevant, using TF-IDF pattern matching and semantic scoring. Generic language models lack the deep domain knowledge required for specialized tasks like Google Ads optimization, medical coding, or financial compliance. Rather than fine-tuning (expensive, slow, brittle) or bloating system prompts with everything the model might need (wasteful, dilutes attention), Knowledge Base Injection retrieves and injects only the specific patterns relevant to the current task.
This skill is built on the production knowledge base system powering Buddy™ at googleadsagent.ai™, specifically the gads-knowledge.js module containing over 1,000 curated Google Ads optimization patterns. Each pattern includes a trigger condition (when to apply it), a recommendation (what to do), evidence (why it works), and a confidence score. When Buddy™ analyzes a campaign, the knowledge base engine scores all patterns against the current context using TF-IDF and injects the top-K most relevant patterns into the agent's context, transforming a general-purpose model into a domain expert.
The injection system operates on a retrieval-augmented generation (RAG) paradigm, but with a critical distinction: rather than retrieving raw documents, it retrieves structured action patterns with built-in confidence scores and applicability conditions. This produces more actionable, more reliable agent outputs than document-level RAG.
Use When
- The agent needs domain expertise that general-purpose models lack
- You have a curated knowledge base of patterns, rules, or best practices
- Fine-tuning is too expensive, too slow, or creates model version lock-in
- Different queries require different subsets of domain knowledge
- You want to update the agent's expertise without retraining or redeploying
- The agent must ground its recommendations in verified, authoritative sources
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.
- 1mo ago First seen · 218 lines · 39 tokens per session scan A 3336943eb793
knowledge-base-injection is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 2,462 once invoked, about $0.0002 per session on Opus 5.5. 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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