cellar AGENTS.md

Repository working notes for Cellar, describing a system that supplies context, memory, task briefs, actions, checks, and records of results to AI-operated software. They separate the open-source data contracts from the hosted runtime and the agents that use them.

In plain words
What is it for?
Use them when developing Cellar's source adapters, context snapshots, durable memory, brief assembly, event and receipt formats, or integrations with agents such as Codex, Claude Code, GPT, Gemini, Cursor, and n8n.
Why use it?
They clarify which parts belong to Cellar and which belong to the commercial runtime, reducing duplicated or misplaced work. They also allow different AI tools and runtimes to connect to the same contracts.

Instructions file for CodexOpenCode

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 instructions/dimpagk92/cellar/agents-md
Clone the repo
git clone --depth 1 https://github.com/dimpagk92/cellar

Made for: Codex, OpenCode.

Per session 1,296 This file is loaded in full into every session.
When invoked 1,296 The same file — it is already loaded in full.
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 $0.01296 $0.01296
Opus 5 $0.00648 $0.00648
Sonnet 5 $0.00259 $0.00259
Haiku 4.5 $0.00130 $0.00130

Measured 2d ago against content hash 1cd19bc78706, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cellar AGENTS.md 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.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

AGENTS.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Working Memory

This repository should be built around four layers:

  • Sources / adapters
  • CEL OSS contracts
  • Cellar/Dilipod runtime
  • Agents

The durable OSS value is the context/memory/brief/receipt data plane, not one planner and not the live runtime itself.

The product direction is CEL as the context and trust layer for AI-operated software. Agents can plan in many ways; Cellar should make their context, memory, briefs, actions, verifications, and receipts reliable.

Repo Direction

  • Sources and adapters should be easy to build and extend.
  • OSS CEL should own context snapshots, merge contracts, memory contracts, brief assembly, transport schemas, and receipt schemas.
  • The commercial runtime should own live cortex operation, policy, monitoring, compliance, and hosted execution.
  • Agents should be pluggable: LangGraph, Mastra, Codex, Claude Code, GPT, Gemini, Cursor, n8n, or future in-house runtimes.

What CEL Owns

  • cel-context: fused context snapshot and merge contracts
  • cel-memory / cel-memory-sqlite: durable memory contract and local backend
  • cel-brief: per-turn LLM brief assembly / budgeting / brief receipts
  • receipt, event, MCP, CLI, SDK, and N-API schemas
  • the split between dispatch proof, model-input proof, and task completion proof

What The Commercial Runtime Owns

  • live cortex operation, freshness, diffs, anomalies, source prioritization
  • runtime capability reporting
  • adapter lifecycle, dispatch, and policy enforcement in production sessions
  • audit timelines, retention, alerting, compliance exports, and governance workflows

What CEL Does Not Need To Own Right Now

  • one mandatory planner
  • one mandatory orchestration runtime
  • retry / branching / checkpoint policy as a repo-defining concern

Built-in planners and runners can exist, but they should be treated as clients, examples, or transitional implementations unless proven otherwise.

Boundary Rules

  • Keep the agent boundary generic.
  • Preserve stable context, action, result, receipt, and adapter contracts.
  • Keep improving AX and the shared crates even when an app later gets an adapter.
  • Prefer app-specific structured truth in adapters over forcing everything through generic UI perception.
  • Do not make LangGraph, Mastra, or any single runtime the identity of the platform.
  • Do not design evals so they only make sense for one agent backend.
  • Treat intent -> dispatch -> observed effect -> evidence as the core trust loop.

Read the full file on GitHub · 110 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 · 110 lines · 1,296 tokens per session scan A 1cd19bc78706

Subscribe to this mod's changes

cellar AGENTS.md is an instructions file published in the GitHub repository dimpagk92/cellar (4 stars, last pushed 22d ago), licensed Apache-2.0. It adds 1,296 tokens to every session, about $0.0065 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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