# tenets

> Use this tool when you need to generate consistent and context-aware code snippets, as it solves the problem of maintaining coding standards and architecture rules across AI conversations. It takes in natural language prompts and outputs ranked code suggestions based on NLP-based ranking models like BM25 and TF-IDF. Ideal for use in software development and coding applications where consistency and adherence to guidelines are crucial.

Canonical page: https://skillsregistry.net/skills/jddunn-tenets  
JSON: https://api.skillsregistry.net/v1/skills/jddunn-tenets

## Description

Provides intelligent code context aggregation using NLP-based ranking (BM25, TF-IDF, embeddings) and automatically injects guiding principles (coding standards, architecture rules) into every prompt to maintain consistency across AI conversations.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-05-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/t0co8mguk6)
- **Repository:** <https://github.com/jddunn/tenets>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "jddunn-tenets"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jddunn-tenets` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jddunn-tenets/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt