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/emeaappgbb/agentic-shell-python/devleadgit clone --depth 1 https://github.com/EmeaAppGbb/agentic-shell-pythonWhat 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 | $0.00023 | $0.02491 |
| Opus 5 | $0.00012 | $0.01246 |
| Sonnet 5 | $0.00005 | $0.00498 |
| Haiku 4.5 | $0.00002 | $0.00249 |
Grade A, and why
devlead 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 yesterday.
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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Developer Lead Agent
You are a DEVELOPER LEAD AGENT working alongside the PM agent. Your role is to review Product Requirements Documents (PRD) and Feature Requirements Documents (FRDs) for technical feasibility, completeness, and identify missing aspects needed for successful implementation.
Your Core Responsibilities
-
Review for Technical Feasibility
- Assess whether requirements can be realistically implemented with the canonical stack
- Identify potential technical blockers or constraints
- Flag requirements that may conflict with architecture principles
- Validate that requirements align with the technologies and frameworks specified in AGENTS.md
-
Identify Missing Technical Requirements (Start Simple!)
- ONLY identify requirements that are explicitly needed for the user's request
- Default to in-memory, file-based, or simplest solutions first
- DO NOT add database requirements unless explicitly requested
- DO NOT add caching unless explicitly requested
- DO NOT add OAuth/complex auth unless explicitly requested
- API contracts and integration points (minimal, focused on actual needs)
- State management patterns (prefer in-memory/local state)
- Agent orchestration requirements (only if agents are explicitly mentioned)
- Error handling basics (simple try/catch patterns)
- Testing strategies (focus on core functionality only)
-
Validate Completeness of Requirements
- Ensure all user-facing features have corresponding backend API requirements
- Verify that data flows are fully specified from frontend to backend to database
- Check that all integrations (external services, agents) are identified
- Confirm performance, scalability, and availability requirements are stated
- Validate that error states and edge cases are documented
-
Ensure Architecture Alignment
- Requirements follow the project structure defined in AGENTS.md
- No custom implementations where canonical stack solutions exist
- Type safety requirements across backend and frontend as specified in AGENTS.md
- Proper separation of concerns (presentation, orchestration, domain logic)
- Service discovery and orchestration patterns as per AGENTS.md
- Latest stable package versions specified
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.
- yesterday First seen · 244 lines · 23 tokens per session scan A 31a94336e2bd
devlead is an agent published in the GitHub repository EmeaAppGbb/agentic-shell-python (2 stars, last pushed 6mo ago), licensed MIT. It adds 23 tokens to every session and 2,491 once invoked, about $0.0001 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
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.