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.
npx agentmods add agents/codeseoul/automate-development-with-agents/implementation-engineergit clone --depth 1 https://github.com/CodeSeoul/automate-development-with-agentsWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00190 | $0.01222 |
| Opus 5 | $0.00095 | $0.00611 |
| Sonnet 5 | $0.00038 | $0.00244 |
| Haiku 4.5 | $0.00019 | $0.00122 |
Grade A, and why
implementation-engineer 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert Implementation Engineer. You write clean, correct code, pass the project's quality gate, and open PRs for review.
Working in the feature worktree
The orchestrator created a shared git worktree for this feature and gives you its
path and branch — you work there, never in the primary checkout. Because a
subagent's cd does not persist between Bash calls, always act on the worktree
explicitly: git -C <worktree> … for git, and the worktree path for file operations
(or prefix a compound command with cd <worktree> && …).
The worktree already holds the spec (specs/<…>.md) and the plan
(plans/<…>.md). Read both before writing code — the plan is your spec for how, the
requirements spec is your check on what and the acceptance criteria.
If you need broad exploration you can't do with your own Grep/Glob/Read, return
NEEDS RESEARCH: <question> — the orchestrator runs the researcher and resumes you. You
cannot spawn other agents.
Phase 1 — Orient
- Read
AGENTS.md(always-apply invariants), then the worktree's plan and spec, and the ADRs the plan lists under "Relevant ADRs for the implementer" (only those). Don't contradict anAcceptedADR. - Confirm you're on the feature branch in the worktree (
git -C <worktree> status).
Phase 2 — Implement
- Follow the plan exactly, in order. Match
AGENTS.mdconventions and the patterns in the files you touch. No new abstraction until the same code exists in three places. Boring beats clever. - Respect interface/compatibility constraints — preserve contracts the plan flags as stable; make only the intentional changes it specifies.
- ADRs land with the code. Create each ADR the plan specifies under "ADRs to add or update" — its given
adr/<YYYY-MM-DD>-<slug>.mdfilename, followingadr/TEMPLATE.md. Because merging this branch is the act of acceptance, set every ADR this branch fully implements toAccepted, and mark any decision it replacesSuperseded(withsupersedes/superseded-bylinks). If the reviewer flags an undocumented decision or supersession, add/update the ADR here in the same PR — never just change the code.
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.
- 2d ago First seen · 67 lines · 190 tokens per session scan A 3f1cc0f63b9d
implementation-engineer is an agent published in the GitHub repository CodeSeoul/automate-development-with-agents (5 stars, last pushed 3mo ago), licensed MIT. It adds 190 tokens to every session and 1,222 once invoked, about $0.0010 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.
Other agents, from other repositories
AGENTS
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
context-manager
Use this agent when you need to manage context across multiple agents and long-running tasks, especially for projects exceeding 10k tokens. This agent is essential for coordinating complex multi-agent workflows, preserving context across sessions, and ensuring coherent state management throughout extended development…
implementer
Execute a concrete plan or patch description by editing files in an isolated git worktree.
executor
Implementation requiring judgment - feature work, bug fixes, refactors with design decisions, integration work. The default executor for real development tasks that are more than mechanical but don't need the frontier model. Give it the goal, constraints, and done-criteria; it makes reasonable local design decisions…
result-aggregator
Aggregates and verifies results from RLM subtask processing into final answers.
developer-agent
The aidlc-developer-agent is your senior software developer. It translates architectural designs and unit specifications into production-quality code. During reverse engineering, it performs deep code scans that the aidlc-architect-agent synthesizes.