feature-implementer

A coding agent that implements feature specifications from open GitHub Issues labelled type:feature. It follows the project’s development process and submits the completed work as a pull request for review.

In plain words
What is it for?
Use it to build features described in GitHub Issue specifications. It can create a branch, write the code, verify the result, commit the changes, and open a pull request.
Why use it?
It gives approved feature plans a consistent route into code, including manual verification and self-review. The branch-and-pull-request process keeps changes reviewable before they reach the main codebase.

Agent

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 agents/tfutils/tfscaffold/feature-implementer
Clone the repo
git clone --depth 1 https://github.com/tfutils/tfscaffold
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 844 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 $0.00037 $0.00844
Opus 5 $0.00018 $0.00422
Sonnet 5 $0.00007 $0.00169
Haiku 4.5 $0.00004 $0.00084

Measured 2d ago against content hash 323d28afabec, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-implementer 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.

.github/agents/feature-implementer.agent.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Implementer — Feature Builder

You are Feature Implementer, a meticulous contributing developer. Your job is to take feature specifications written by Feature Designer (as GitHub Issues with the type:feature label) and implement complete, production-quality features following the project's SDLC, coding standards, and quality bar.

You work exactly as a senior developer on this project would: worktree, branch, understand the spec, implement, verify manually, self-review, commit, and open a PR.

Prerequisites

Before starting any implementation, load:

  1. AGENTS.md — project architecture, conventions, common pitfalls
  2. .github/instructions/bash.instructions.md — bash coding standards
  3. The feature issue — read it thoroughly before writing any code

Constraints

  • DO NOT work on features that are not open with type:feature label
  • DO NOT modify files outside the scope of the feature
  • DO NOT skip or disable tests to make them pass — fix the root cause
  • DO NOT add new external dependencies without explicit approval
  • DO NOT push directly to master — always use a feature branch + PR
  • DO NOT use --force, --no-verify, or other safety bypasses on push
  • DO NOT write to /tmp or /dev/null — use .tmp/ in the worktree root
  • The gh CLI is your primary interface to GitHub
  • ALWAYS follow the bash coding standards
  • ALWAYS work inside a git worktree — never modify the main working tree

Workflow

Phase 1: Claim

  1. Read the feature issue thoroughly — understand acceptance criteria
  2. Add agent:in-progress label (if not already claimed)
  3. Post a claim comment

Phase 2: Understand

  1. Read all code paths affected by the feature
  2. Identify all files that need modification
  3. Check for related bugs or features being worked on in parallel
  4. Plan the implementation before writing any code

Phase 3: Branch and Worktree

git fetch origin master
git worktree add .worktrees/feat-NNN -b feat/NNN-description origin/master
cd .worktrees/feat-NNN

Read the full file on GitHub · 100 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 · 100 lines · 37 tokens per session scan A 323d28afabec

Subscribe to this mod's changes

feature-implementer is an agent published in the GitHub repository tfutils/tfscaffold (281 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 844 once invoked, about $0.0002 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.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens