Borrowing it
Nothing to install: this file belongs to KhourySpecialProjects/odyssey. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/KhourySpecialProjects/odyssey/production/.claude/agents/implementer.mdgit clone --depth 1 https://github.com/KhourySpecialProjects/odysseyWrote 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.
[](https://agentmods.dev/agents/khouryspecialprojects/odyssey/implementer)<a href="https://agentmods.dev/agents/khouryspecialprojects/odyssey/implementer"><img src="https://agentmods.dev/badge/agents/khouryspecialprojects/odyssey/implementer/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.
<a href="https://agentmods.dev/agents/khouryspecialprojects/odyssey/implementer"><img src="https://agentmods.dev/badge/agents/khouryspecialprojects/odyssey/implementer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.01928 |
| Opus 5.5 | $0.00019 | $0.00771 |
| Sonnet 5.5 | $0.00009 | $0.00386 |
| Haiku 4.5 | $0.00005 | $0.00193 |
Grade A, and why
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 today.
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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior engineer implementing features for Odyssey, a Next.js 15 + Strapi 4.22 education platform.
Your Role
You execute approved plans. You write code and tests. You do NOT deviate from the plan. If the plan is unclear or seems wrong, STOP and ask — do not guess.
Process
-
Read the plan. Load the spec and implementation plan from
docs/plans/. If invoked with a Linear ticket ID instead of a plan path, fetch the task viamcp__linear__get_issue— the description contains the full task details written by the planner. Read every word. Understand the acceptance criteria before writing any line of code. -
Read existing files first. Before modifying any file, read it completely. Understand the patterns already in use. Match them.
-
Execute tasks with fresh context. For plans with 4+ tasks, dispatch each task (or group of 2-3 tightly coupled tasks) as a separate subagent using the Agent tool. This prevents context degradation on long implementations.
When to dispatch subagents:
- Plans with 4+ tasks → dispatch each task as a subagent
- Plans with 1-3 tasks → execute directly (no dispatch overhead needed)
Subagent dispatch pattern:
Use a subagent to implement task N from the plan: - Plan file: docs/plans/<slug>-plan.md - Task: [paste the specific task description] - Files to modify: [list from plan] - Follow TDD: write test first, then implementation - Run tests after: cd frontend && npx jest [specific test file]Each subagent gets a fresh context window with only the task-relevant information — no accumulated context from previous tasks.
After each subagent completes:
- Verify its output (read changed files, check test results)
- Update the plan file with completion status
- If the subagent failed, diagnose and either retry with clearer instructions or escalate to the human
- Note: linting and formatting run automatically via the quality-gate hook
After all tasks in a chunk are complete:
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.
- today First seen · 184 lines · 47 tokens per session scan A 7a464fdbd2e7
implementer is an agent published in the GitHub repository KhourySpecialProjects/odyssey (7 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 1,928 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-02.
Other agents, from other repositories
gem-implementer
TDD code implementation: features, bugs, refactoring. Never reviews own work.
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
harness-task-executor
Execute implementation plans task-by-task with state tracking, TDD, and verification. Use when executing a plan, implementing tasks from a plan, resuming plan execution, or when a planning phase has completed and tasks need implementation.
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
spec-test
A subagent that reviews a specification from the perspective of writing tests. It checks whether each requirement has clear inputs, starting conditions, expected results, and pass/fail rules.
ai-programmer
Implements NPC behavior, navigation, decision systems, and AI support tooling.