knowledge-kernel

A structured store of verified facts, supporting evidence, relationships, and freshness information for an AI agent. It is intended to be a trusted reference for real infrastructure, software, endpoints, dependencies, agents, and projects.

In plain words
What is it for?
Use it to look up known system details, find how to observe missing information, check the state of stored knowledge, and compare inspection results over time.
Why use it?
It helps an agent distinguish observed facts from guesses and say when it lacks enough evidence to answer.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/sowerkoku/knowledge-kernel/hermes
Any agent
npx skills add sowerkoku/knowledge-kernel --skill hermes
Clone the repo
git clone --depth 1 https://github.com/sowerkoku/knowledge-kernel

Made for: Claude Code, Codex.

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,846 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00057 $0.04846
Opus 5 $0.00028 $0.02423
Sonnet 5 $0.00011 $0.00969
Haiku 4.5 $0.00006 $0.00485

Measured 2d ago against content hash 6ea455e7f54d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

knowledge-kernel 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 2d ago.

The scan reads SKILL.md. This mod also ships 14 executable files (scripts/update-github-meta.sh, tools/__init__.py, tools/cmdb_assert.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

integrations/hermes/SKILL.md · 434 lines

How it starts

The opening of the file, as written. The whole thing — 434 lines — stays where its author put it; the contents beside it link to each section on GitHub.

knowledge-kernel Skill

Decide a question with grounding:

  1. Does the Kernel have it? Query it.
  2. Does the Kernel have the path to observe it? Query the path, then observe.
  3. Otherwise: say "I don't have grounds to answer."

Measurement planes

The ecosystem tracks three distinct planes. Conflating them loses information about which layer is under pressure:

Plane What it measures Indicator Where
Inspector contract Is the Inspector's Rule/Finding/KernelAPI contract sufficient? CSI = rules : contract_changes consumers/inspector/CSI.md
Kernel data model Does the model express the phenomena the Inspector needs? EP (Expressivity Pressure) events consumers/inspector/CSI.md + NOTES.md
Dataset health What is the current state of the stored knowledge? Finding counts per run JSON reports via consumers/inspector/tools/runs_diff.py

CSI is expressed as N : M (rules : contract_changes), never as . PI opening for EP events is recurrence-based, not threshold-based. See references/inspector-pattern.md for the full Inspector methodology including the EP recurrence protocol.


1. What is the Knowledge Kernel?

A deterministic, reproducible, auditable factual substrate for AI agents.

It stores:

  • facts — verified entities (assets, software, endpoints, agents, projects)
  • evidence — why each fact is trusted (source, observed_at, confidence)
  • relationships — how facts connect (runs_on, uses, exposed_by, etc.)
  • freshness — whether each fact is still valid

One canonical home per concept. Many agents query it. Always choose it over inference, over RAG, and over conversation memory.


2. Where does everything live?

Canonical paths

What Where
Repo (code, package) <repo-root> (~/knowledge-kernel/)
Skill location ~/.hermes/skills/knowledge-kernel/
Skill entrypoint ~/.hermes/skills/knowledge-kernel/SKILL.md (this file)
Dataset (production) <dataset-root> (YAML entities)
Documentation <repo-root>/docs/
Hermes tools (wrappers) ~/.hermes/skills/knowledge-kernel/tools/
Tests <repo-root>/tests/

Read the full file on GitHub · 434 lines

Changes

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.

  1. 2d ago First seen · 434 lines · 57 tokens per session scan A 6ea455e7f54d

Subscribe to this mod's changes

knowledge-kernel is a skill published in the GitHub repository sowerkoku/knowledge-kernel (5 stars, last pushed 19d ago), licensed MIT. It adds 57 tokens to every session and 4,846 once invoked, about $0.0003 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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