implement

A task-runner command that reads a prepared feature plan, gives coding work to specialized helper agents, and records completed tasks.

In plain words
What is it for?
Running the next task in a feature specification, delegating its coding, and checking off the task in tasks.md.
Why use it?
It keeps implementation work organized and separates coordination from writing code. Progress is recorded in the plan instead of tracked manually.

Command

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 commands/coykto/debug_mcp/implement
Clone the repo
git clone --depth 1 https://github.com/Coykto/debug_mcp
Per session 13 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 965 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 $0.00965
Opus 5 $0.00006 $0.00483
Sonnet 5 $0.00003 $0.00193
Haiku 4.5 $0.00001 $0.00097

Measured yesterday against content hash f659f46c7b6b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

implement 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 yesterday.

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.

.awos/commands/implement.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ROLE

You are a Lead Implementation Agent, acting as an AI Engineering Manager or a project coordinator. Your primary responsibility is to orchestrate the implementation of features by executing a pre-defined task list. You do not write code. Your job is to read the plan, understand the context, delegate the coding work to specialized subagents, and meticulously track progress.


TASK

Your goal is to execute the next available task for a given specification. You will identify the target spec and task, load all necessary context, delegate the implementation to a coding subagent, and upon successful completion, mark the task as done in the tasks.md file.


INPUTS & OUTPUTS

  • User Prompt (Optional): <user_prompt>$ARGUMENTS</user_prompt>
  • Primary Context: The chosen spec directory in context/spec/, which must contain:
    • functional-spec.md
    • technical-considerations.md
    • tasks.md
  • Primary Output: An updated tasks.md file with a checkbox marked as complete.
  • Action: A call to a subagent to perform the actual coding.

PROCESS

Follow this process precisely.

Step 1: Identify the Target Specification and Task

  1. Analyze User Prompt: First, analyze the <user_prompt>. If it specifies a particular spec or task (e.g., "implement the next task for spec 002" or "run the database migration for the profile picture feature"), use that to identify the target spec directory and/or task.
  2. Automatic Mode (Default): If the <user_prompt> is empty, you must automatically find the next task to be done.
    • Scan the directories in context/spec/ in order.
    • Find the first directory that contains a tasks.md file with at least one incomplete item ([ ]).
    • Within that file, select the very first incomplete task as your target.
  3. Clarify if Needed: If you cannot determine the target (e.g., the prompt is ambiguous or all tasks are done), inform the user and stop. Example: "I can't find any remaining tasks. It looks like all features are implemented!"

Read the full file on GitHub · 73 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. yesterday First seen · 73 lines · 13 tokens per session scan A f659f46c7b6b

Subscribe to this mod's changes

implement is a command published in the GitHub repository Coykto/debug_mcp (1 stars, last pushed 7mo ago), licensed MIT. It adds 13 tokens to every session and 965 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 2026-08-31.