goga-cell-python

A set of Python implementation rules for CODEMANIFEST contracts, which describe required code structure, conventions, imports, and tests.

In plain words
What is it for?
Use it when implementing Python modules, methods, and data models under a CODEMANIFEST specification.
Why use it?
It gives Python code a defined contract to follow, reducing ambiguity about types, behavior, serialization, and testing responsibilities.

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/qarium/goga/goga-cell-python
Any agent
npx skills add qarium/goga --skill goga-cell-python
Clone the repo
git clone --depth 1 https://github.com/qarium/goga

Made for: Claude Code, Codex.

Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 913 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 $0.00013 $0.00913
Opus 5 $0.00006 $0.00456
Sonnet 5 $0.00003 $0.00183
Haiku 4.5 $0.00001 $0.00091

Measured 2d ago against content hash b0cdd8375de7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

goga-cell-python 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.

goga/assets/skills/goga-cell-python/SKILL.md · 163 lines

How it starts

The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python: Contract Implementation Rules

Language skill for Python.

Apply the specification within the context of the invoking skill. Do not paraphrase the content — use it for decision-making.

Invoke via the goga-lang-disp router.


Examples

Full CODEMANIFEST example for Python with all DSL constructs:

Imports:
  - Types:
      - DataModel
      - BaseConfig AS Config
    Usages:
      - serialization
    From: path/to/data_cell

Usages:
  conventions: .goga/usages/python_conventions.md
  pattern: |
    All public methods must use type hints. Return values are immutable where possible.
  testing: |
    Each routine and entity method must have a corresponding test in tests/.

Annotations: |
  Use `conventions` for code style.
  Use `testing` for test requirements.
  Use `serialization` from Imports for data encoding patterns.

  All methods must return concrete types, not `Any`.

---

"parse_input(input: str) -> data: bytes":
  location: parser.py
  annotations: |
    Parse raw input string into structured data.

    `input`: raw string to parse

    Use `pattern` for implementation.

"DataProcessor(config: Config)":
  location: processor.py
  annotations: |
    Process data according to configuration.

    `config`: processor configuration from `Config` type.

    Use `serialization` from Imports for encoding.
    Use `conventions` for code style.
  properties:
    "name -> str": |
      Processor identifier.
    "buffer_size -> int": |
      Maximum buffer size in bytes.
  methods:
    "process(data: list[T]) -> result: list[T]": |
      Process a batch of data items.

      `data`: input items to process
      `result`: processed items

      Use `pattern` for implementation.
    "reset()": |
      Reset internal state.

"BaseHandler::HTTPHandler(host: str)":
  location: handler.py
  annotations: |
    HTTP-specific handler extending BaseHandler.

    `host`: server hostname

    Use `serialization` from Imports for request/response encoding.

->DataModel: {}

---

Author: Goga
CreatedAt: 22/05/26
Description: |
  Example CODEMANIFEST demonstrating all DSL constructs for Python.

Read the full file on GitHub · 163 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 · 163 lines · 13 tokens per session scan A b0cdd8375de7

Subscribe to this mod's changes

goga-cell-python is a skill published in the GitHub repository qarium/goga (13 stars, last pushed 2d ago), licensed BSD-3-Clause. It adds 13 tokens to every session and 913 once invoked, about $0.0001 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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