akashi

A connection to Akashi, a local and private knowledge vault that stores the user's notes and project information. Answers are based on passages from that vault and include where the information came from.

In plain words
What is it for?
Use it to answer questions about the user's projects, conventions, past decisions, or saved knowledge.
Why use it?
It lets an agent consult the user's own notes and decisions instead of relying on memory or pasted context.

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/apiliumcode/mayros/akashi
Any agent
npx skills add ApiliumCode/mayros --skill akashi
Clone the repo
git clone --depth 1 https://github.com/ApiliumCode/mayros

Made for: Claude Code, Codex.

Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,000 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 $0.00069 $0.01000
Opus 5 $0.00034 $0.00500
Sonnet 5 $0.00014 $0.00200
Haiku 4.5 $0.00007 $0.00100

Measured 2d ago against content hash 02a0bc592cad, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

akashi 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 3 executable files (citations.test.ts, citations.ts, types.ts), 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.

skills/official/akashi/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Akashi: the user's second brain, connected

Akashi is a local-first, encrypted knowledge vault with a semantic engine (AIngle, the same engine inside Mayros). It runs on the user's machine and serves an MCP endpoint at http://127.0.0.1:19191/mcp with a bearer token. Retrieval is grounded: every passage carries source:lines citations and a cryptographically signed provenance anchor. Measured on the standard demo vault: about 94% fewer input tokens than pasting notes into context.

1. Connect (once)

If aingle_ground and the other aingle_* tools are already available, you are connected. Skip to section 2.

Otherwise, add Akashi as an MCP server in the mcp-client extension config:

{
  "servers": [
    {
      "id": "akashi",
      "name": "Akashi vault",
      "transport": {
        "type": "http",
        "url": "http://127.0.0.1:19191/mcp",
        "authToken": "<TOKEN>"
      },
      "autoConnect": true
    }
  ]
}

The user creates <TOKEN> in the Akashi app: Settings, "AI & connections", "Access tokens", named mayros. One token per runtime: revoking one never disconnects the rest. Akashi is a free download for macOS, Windows, and Linux at https://apilium.com/en/products/akashi.

If the connection is refused, Akashi is not running: ask the user to open it. On HTTP 401 the token was revoked: mint a new one.

2. Query-first protocol (the core rule)

BEFORE answering anything that may touch the user's knowledge (projects, past decisions, conventions, people, notes, plans), query the vault:

  • aingle_ground {question, k}: the primary tool. Returns cited passages (source, lines, text, signed provenance anchor), a groundedness verdict, and an instruction you MUST follow.
  • aingle_vault_map: orientation. Hubs, semantic clusters, indices.
  • aingle_note_context {note}: the verified neighborhood of one note.
  • aingle_sources: what is indexed, with content hashes.
  • To trace how two notes or topics connect: call aingle_note_context on each and intersect their neighborhoods, or walk aingle_backlinks from one side; report the connecting chain with citations for every hop.

Read the full file on GitHub · 94 lines

Files

What ships with it

8 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.

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 · 94 lines · 69 tokens per session scan A 02a0bc592cad

Subscribe to this mod's changes

akashi is a skill published in the GitHub repository ApiliumCode/mayros (12 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 1,000 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 2026-08-30.