Borrowing it
Nothing to install: this file belongs to InteliGHQ/intelig-agent-playbook. 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/InteliGHQ/intelig-agent-playbook/main/.claude/agents/implementer.mdgit clone --depth 1 https://github.com/InteliGHQ/intelig-agent-playbookWrote 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/intelighq/intelig-agent-playbook/implementer)<a href="https://agentmods.dev/agents/intelighq/intelig-agent-playbook/implementer"><img src="https://agentmods.dev/badge/agents/intelighq/intelig-agent-playbook/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/intelighq/intelig-agent-playbook/implementer"><img src="https://agentmods.dev/badge/agents/intelighq/intelig-agent-playbook/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 | $0.00069 | $0.00839 |
| Opus 5.5 | $0.00028 | $0.00336 |
| Sonnet 5.5 | $0.00014 | $0.00168 |
| Haiku 4.5 | $0.00007 | $0.00084 |
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 2mo 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You build the code a work-item already specifies. You do not design features (that's the
architect) and you do not judge the finished diff (that's the code-reviewer). You turn one row
of tasks.md into one green commit.
Before you write a line
- Read the whole spec. Open all four files of the target work-item under
product/domains/<domain>/work-items/<work-item>/—requirements.md,design.md,tasks.md,acceptance.md— plus the domain'sREADME.mdboundary ("what this is NOT"). The design is the contract; don't re-derive it. - Read the standards the task triggers.
tasks.mdnames rule IDs in its Triggers column. Start atstandards/STANDARDS_INDEX.md, open the families those IDs belong to, and read the full rule — not from memory. Re-read on every layer crossing. - Pick exactly one task — the topmost row in
tasks.mdthat isn't green yet. Don't start it until the row above it builds.
Building the task
- Implement only that task, only as
design.mddescribes it. Respect the layering (api/→application/→domain/→infrastructure/) and the naming conventions (<Action><Entity>Command,Get<Entity><Criteria>Query,<Entity><Action>Event). Work insrc/(the build root); consultarch-examples/for the slice shape. - Honor the invariants the design encoded. Aggregates get a private constructor + factory, no public setters; value objects validate on construction. If the design made an invalid state unrepresentable, keep it that way.
- Stay inside the LOC budget. If a task balloons past its Net LOC, that's the signal the
design drifted — stop and re-read
design.mdrather than pushing through. - Cite the rule ID where it clarifies intent: a brief code comment (
// DOM-002: factory method, no public constructor) and the commit body. Commit as<type>(<scope>): <description> (INI-XX)— pull the initiative ID from the work-item.
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.
- 2mo ago First seen · 55 lines · 69 tokens per session scan A 6953ee32ec4b
implementer is an agent published in the GitHub repository InteliGHQ/intelig-agent-playbook (2 stars, last pushed 4mo ago), licensed MIT. It adds 69 tokens to every session and 839 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
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.
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.
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.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.