implement

implement is a skill for Claude Code from iusztinpaul/designing-real-world-ai-agents-workshop. It costs 197 tokens per session (5,760 once invoked), scanned A, a copy of implement, MIT.

A workshop workflow that assigns one prepared coding ticket to a software engineer and then a tester. It creates a branch, checks the acceptance criteria, moves the completed ticket, and commits the work.

In plain words
What is it for?
Use it to process tickets from implement_yourself/tasks/, run the appropriate verification, move finished tickets to tasks/done/, and create a Git commit.
Why use it?
It gives each ticket a defined implementation and review gate while keeping the main coordinator from writing or reviewing the code directly.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions CLAUDE.md; mentions Claude Code; mentions AGENTS.md.

Good fit Use it to process tickets from implement_yourself/tasks/, run the appropriate verification, move finished tickets to tasks/done/, and create a Git commit.

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Install with agentmods
npx agentmods add skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement
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.

Any agent
npx skills add iusztinpaul/designing-real-world-ai-agents-workshop --skill implement
Clone the repo
git clone --depth 1 https://github.com/iusztinpaul/designing-real-world-ai-agents-workshop

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for implement

README.md
[![agentmods](https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement/github.svg)](https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement)
Your own site
<a href="https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement"><img src="https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for implement

Your own site · 80×15
<a href="https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement"><img src="https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 197 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,760 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 95% copy Near-identical to another mod 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 5.1 $0.00197 $0.05760
Opus 5 $0.00098 $0.02880
Sonnet 5 $0.00039 $0.01152
Haiku 4.5 $0.00020 $0.00576

Measured 11d ago against content hash 6d1ba0d8b876, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, 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 11d 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.

Origin

This is a copy

95% identical to implement — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

implement_yourself/.claude/skills/implement/SKILL.md · 366 lines

How it starts

The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Implement Mode — Workshop Single-Ticket Implementation Loop

A workshop-specialized adaptation of squid's /day skill. Drives one pre-groomed ticket from implement_yourself/tasks/NNN-slug.groomed.md through:

new feature branch → SWE implements (+ AC walk on glue tickets) → verifier (Tester on logic tickets, orchestrator spot-check on glue tickets) → orchestrator moves file to tasks/done/ → orchestrator commits directly with `git commit -m` → report to human

After the report, the session ends. The human reviews the commit, talks the workshop audience through what happened, optionally amends or pushes, then types /implement next (or /implement NNN) to pick up the following ticket.

You are the orchestrator — a MANAGER, not an implementer. You do NOT write code, run make targets, or read changed files for review yourself. You launch agents, enforce the Tester gate, and finalize the ticket (branch + done-move + commit).

E2E smoke tests

The Makefile exposes three end-to-end targets that double as smoke tests. Most tickets name one of them in their Acceptance Criteria as the verification target:

  • make test-research-workflow — exercises the Deep Research MCP server end-to-end on the dataset seed. Default smoke test for any research-side ticket (#001–#010, #013).
  • make test-writing-workflow — exercises the LinkedIn Writer MCP server end-to-end on the dataset guideline + prebuilt research. Default smoke test for any writing-side ticket (#011, #014–#019).
  • make test-end-to-end — runs research + writing back-to-back on a dataset sample. Use for cross-cutting tickets (#020 Opik wiring, #024 README, anything that integrates both servers).

When a ticket does not explicitly name a target, infer the right one from the affected server. Bootstrap tickets (make run-research-server / make run-writing-server) are the exception — those boot-and-kill checks are not smoke tests.

Critical rules

  • Never rubber-stamp the Tester's (or SWE's, on glue tickets) report. When the verifier says PASS, re-read each Acceptance Criterion in the ticket and confirm the report's evidence is real (file path, command output excerpt, Python expression result). REJECT and re-launch if not.
  • /implement is single-shot per ticket. After step 7, end the session. Do not auto-pick the next ticket.
  • Commit directly with git commit -m. The orchestrator hand-crafts a one-line commit message from the ticket title (feat: {Title} (#NNN) or docs: {Title} (#NNN) for README tickets) — we no longer route through /commit-commands:commit to save an LLM round-trip.
  • One ticket per invocation. No batching. If the user asks for multiple tickets, decline and tell them to invoke /implement again per ticket.
  • No worktree isolation. The branch is created in the human's working tree; the SWE works directly there so the audience can watch the diff evolve.
  • make eval-online is BANNED. It hits production and burns budget. Never run it — not on the SWE side, not on the Tester side, not for any ticket (especially anything after #023 where it might be implied). Allowed eval targets are make eval-dev, make eval-test, make upload-eval-dataset. If a ticket explicitly names eval-online, push back to the human before proceeding.

Read the full file on GitHub · 366 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. 11d ago First seen · 366 lines · 197 tokens per session scan A 6d1ba0d8b876

Subscribe to this mod's changes

implement is a skill published in the GitHub repository iusztinpaul/designing-real-world-ai-agents-workshop (504 stars, last pushed 3mo ago), licensed MIT. It adds 197 tokens to every session and 5,760 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to implement, differing in 10 lines, and is treated as a copy.

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