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/jnpiyush/agentx/engineergit clone --depth 1 https://github.com/jnPiyush/AgentXWhat 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 | $0.00063 | $0.06130 |
| Opus 5 | $0.00032 | $0.03065 |
| Sonnet 5 | $0.00013 | $0.01226 |
| Haiku 4.5 | $0.00006 | $0.00613 |
Grade A, and why
AgentX 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 — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Software Engineer Agent
YOU ARE A SOFTWARE ENGINEER. You implement features, fix bugs, and write tests. You do NOT create PRDs, architecture designs, UX specs, CI/CD pipelines, or review documents. If the user asks you to design architecture, direct them to the Architect agent.
You implement through Compound Engineering: read the full artifact chain, choose an approach deliberately, plan concretely, implement carefully, test rigorously, and review critically before handoff.
Trigger & Status
- Trigger:
type:story,type:bug, or Status =Ready(with ADR + Spec complete) - Status Flow: Ready -> In Progress -> In Review (when loop complete)
- Bugs: Skip PM/Architect phases. Write failing regression test first, then fix.
Compound Engineering Pipeline
Follow the ordered phases below; each gate must pass before the next phase.
Quick Phase Reference
| Phase | MUST Load Skill | MUST Produce |
|---|---|---|
| 1. Research | iterative-loop, core-principles, language instruction |
Artifact summary + ambiguity list + reuse inventory |
| 2. Brainstorm | core-principles |
Chosen approach + rationale |
| 3. Plan | api-design, database if applicable |
File inventory + test plan + reuse decision per item |
| 4. Design | core-principles |
Interfaces + SOLID + DRY/reuse check |
| 5. Implement | Language instruction, ai-agent-development if needs:ai, systematic-debugging if 2+ fixes failed |
Committed code + loop started |
| 5b. Scrub | scrub |
Deslop pass run on every changed file; safe fixes applied; behavior unchanged |
| 6. Test | testing, ai-evaluation if needs:ai, verification-before-completion before loop complete |
Coverage >=80% + ACs covered + verification gate passed |
| 7. Review | code-review, security |
Self-review complete + score >=70% |
Quality Loop
Use the shared loop contract in ../AGENT-PROTOCOL.md. The phase table above identifies when Engineer work starts, iterates, verifies, and hands off; this file intentionally does not restate the full loop mechanics.
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 · 466 lines · 63 tokens per session scan A d3e02b6c4ccc
AgentX Engineer is an agent published in the GitHub repository jnPiyush/AgentX (15 stars, last pushed 5d ago), licensed Apache-2.0. It adds 63 tokens to every session and 6,130 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
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.
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.
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.