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feature-tracking

Helps your agent remember code features. It keeps track of the status, documentation, decisions, and changes for each feature. This is useful for larger software projects.

What it does

This tool helps organize and remember important information about specific parts of your code (called 'features'). It creates a simple record in text files to track what's working, decisions made, risks, and changes, preserving this memory even after you stop and resume work.

When to use it

  • ✓When you return to work on a piece of code after a break.
  • ✓When information about a piece of code is scattered across multiple places.
  • ✓When a piece of code is long-lived and needs a clear record of decisions and risks.

Example of what to ask your AI

“Help me organize the information for the login feature, recording key decisions and known risks.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/feature-tracking/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/feature-tracking/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool creates folders and simple text files for each code feature. It stores a summary of what's currently working, where to find official documents, important decisions, risks, and a change history.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill feature-tracking

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/feature-tracking/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Feature Tracking

Overview

Feature Tracking maintains lightweight, repository-native memory for long-lived feature work. It gives AI coding agents a stable place to find the current status, authoritative documents, verified behavior, durable decisions, risks, and recent changes without treating chat history or stale plans as truth.

The workflow uses a global index plus one Markdown track per feature under docs/features/. It complements issue trackers, specifications, and source code by linking the evidence that still matters rather than duplicating it.

When to Use This Skill

  • Use when starting or resuming work on a feature after a session, agent, or tool change.
  • Use when feature knowledge is scattered across PRDs, API notes, plans, issues, and old commits.
  • Use when a long-lived feature needs durable decisions, risks, rollout constraints, or migration notes.
  • Use when reviewing or finishing feature work and recording the verified outcome for future agents.
  • Use when adopting lightweight project memory in an existing repository without reorganizing all documentation.

Do not use this skill merely to log every code edit or replace an existing issue tracker. Use it when future contributors need a concise, current view of an entire feature.

How It Works

Step 1: Discover Existing Feature Memory

Before changing a feature:

  1. Read docs/features/README.md if it exists.
  2. Identify the feature id from the request, code module, route, domain, or existing documentation.
  3. Read docs/features/<feature-id>/README.md if it exists.
  4. Follow its current source-of-truth links before proposing or implementing changes.

Never assume an old plan is authoritative merely because it is detailed. Prefer current code, tests, accepted specifications, and recent verified decisions.

Step 2: Create the Minimal Structure When Missing

Use lowercase hyphen-case for feature ids:

docs/features/
├── README.md
└── <feature-id>/
    ├── README.md
    ├── prd/
    ├── api/
    ├── plans/
    └── archive/

For an existing repository, create only the directories needed now. Link useful documents where they already live before considering a migration.

The global index should remain a compact navigation and status surface:

# Feature Tracks

| Feature | Status | Track | Source of Truth | Updated | Notes |
|---|---|---|---|---|---|
| Checkout | active | `checkout/README.md` | `checkout/prd/checkout.md` | 2026-07-13 | Payment retry work in progress |

Use project-local status names when the repository already defines them. Otherwise prefer a small vocabulary such as planned, active, stable, paused, or deprecated.

Step 3: Maintain the Feature Track

Each docs/features/<feature-id>/README.md should summarize current truth and link to detailed evidence:

# Checkout Feature Track

## Current Status

Checkout supports one-time card payments. Automatic payment retry is in progress.

## Source of Truth

- Checkout PRD: `prd/checkout.md`
- Payments API: `api/payments.md`
- Current implementation plan: `plans/payment-retry.md`

## Current Behavior

- Customers can complete one-time card payments.
- Failed payments currently require a manual retry.

## Decisions

- Preserve idempotency keys across automatic retries.
- Keep retry policy in the payments service.

## Known Risks

- The provider sandbox does not reproduce every production decline code.

## Changelog

- 2026-07-13: Added the retry plan and recorded idempotency requirements.

Update the track when any of these change:

  • user-visible or system-visible behavior,
  • endpoints, data models, dependencies, or integrations,
  • durable decisions and trade-offs,
  • rollout constraints, migrations, risks, or follow-ups,
  • tests, plans, specifications, or other source-of-truth links.

Keep detailed requirements and designs in their own documents. The feature track should explain what is true now and where to find the proof.

Step 4: Reconcile the Track Before Completion

Before claiming the feature work is complete:

  1. Update the feature track with the actual verified outcome, not only the intended plan.
  2. Update the global index when status, date, links, or notes changed.
  3. Check that every relative Markdown link resolves.
  4. Confirm the track contains current status, source-of-truth links, decisions, risks, and a dated changelog.
  5. Record unresolved blockers or follow-ups explicitly.

…

FAQ

Does this replace an issue tracker?

No, it complements it. It focuses on maintaining a concise record of what's important for a specific feature, not listing every small task.

Do I need to move all my documents?

No, you can just link to documents that already exist elsewhere. The goal is to be lightweight and not require a major reorganization.

What does 'durable memory' mean?

It means that important information about the feature is stored in an organized and accessible way for the future, even if you switch tools or take a break.

Details

Original description by the author

Maintain durable feature-level memory across AI coding sessions with lightweight Markdown tracks for status, source-of-truth docs, decisions, risks, and changes.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2991
License
MIT
Updated
10/15/2026

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