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context-discovery skillA

context-discovery is agent-read markdown (skill) from jellydn/my-ai-tools: Use MCP tools to trace behavior that spans multiple modules or history sources..

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# Context Discovery

## When to Use

Use this skill **before and during implementation** when:
- Starting work on an unfamiliar module or feature
- The task involves multiple files or systems
- You need to understand existing patterns before coding
- Previous decisions or discussions may be relevant
- You want to avoid duplicating existing functionality

## What It Does

Leverages available MCP tools to proactively discover context about the codebase, existing patterns, decisions, and related work. Instead of relying solely on grep/read cycles, it uses purpose-built discovery tools.

## Discovery Workflow

### Step 1: File Discovery

Find the relevant files using `fff`:

```
fff auth                        # Find auth-related files
fff "*order*"                   # Find order-related files by pattern
fff config                      # Find config files
```

Scan the results to identify the module structure. `fff` returns frecency-ranked results — the files you access most appear first.

### Step 2: Pattern Discovery via `sem`

Once you know the relevant files, use `sem` to understand the code's history and structure:

```
…

Read the whole file at its exact version.

How to install

Latest version
mdr add jellydn/my-ai-tools/context-discovery@git:20260913.e2e25be
Exact content
mdr add jellydn/my-ai-tools/context-discovery@sha256:6605b2e68bca37a9

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_nquqbkhwicjfh53p.svg)](https://markdownregistry.com/a/art_nquqbkhwicjfh53p)

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Versions

versioncommittedcommitsizeaudit
git:20260913.e2e25be latest2026-09-13 e2e25be 5,075 BA view · diff
git:20260814.d1987f52026-08-14 d1987f5 5,099 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (5075 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

jellydn/my-ai-tools · 121 stars · license MIT · pushed 2026-09-24 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_nquqbkhwicjfh53p
GET https://markdownregistry.com/api/v1/resolve?ref=jellydn/my-ai-tools/context-discovery
GET https://markdownregistry.com/api/v1/blob/6605b2e68bca37a919c3779ab7ee4c01c0dec302322ad869fe82679436dda6a7

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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