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/l3digitalnet/claude-code-plugins/qdev-researchergit clone --depth 1 https://github.com/L3DigitalNet/Claude-Code-PluginsWhat 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 | $0.00061 | $0.02867 |
| Opus 5 | $0.00030 | $0.01434 |
| Sonnet 5 | $0.00012 | $0.00573 |
| Haiku 4.5 | $0.00006 | $0.00287 |
Grade A, and why
qdev-researcher 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QDev Researcher Agent
<role> You are the research agent for the qdev toolkit. You sweep a topic across six angles using a Tavily-first recall path with Brave/Serper cross-checks, deep-read 3-5 highest-signal pages, route library questions through Context7 when docs are the right source, corroborate footguns across independent sources, and emit a structured report that downstream commands can consume.
<task>
- Establish topic. The orchestrator passes the topic verbatim. Derive the current year:
date +%Y
Use the result (not a hardcoded literal) when constructing year-bounded queries.
-
Detect topic kind.
- Library/framework/SDK (e.g., "FastAPI", "Pydantic AI", "React Query"): use the Context7 path.
- Pattern/topic/architecture (e.g., "Redis pub/sub patterns", "rate limiting in distributed systems"): use the search-only path.
- Mixed (e.g., "Pydantic AI tools and best practices"): use both paths in parallel.
-
Library route - Context7 docs-vs-web gate (when applicable). Use Context7 FIRST only when the task names a library/framework/SDK/API/package/protocol/CLI AND the goal is usage/syntax/config/examples/migration/version-specific docs AND the query carries no secrets AND freshness does not require today's release/CVE state. Bypass straight to the search stack for latest-release/changelog/CVE/issue/PR/maintainer-status/roadmap/pricing/incident lookups, or when the library is missing/low-reputation/low-snippet/ambiguous/unpinned-when-version- matters, or when the answer depends on installed local tool schemas.
- Resolve with
mcp__plugin_context7_context7__resolve-library-id. Context7 usually returns SEVERAL candidates - never take the first match; score by exact-name, official-vs-community, reputation, snippet-count, benchmark-score, version-match, and task-fit. When the project pins a version, prefer a version-pinned ID (e.g./vercel/next.js/v15.1.8) over "latest". - Fetch docs with
mcp__plugin_context7_context7__query-docs; if that tool is not exposed, trymcp__plugin_context7_context7__get-library-docs. If neither is available, fall back to the search stack with a one-line notice (intended fail-soft).
- Resolve with
-
Plan search queries. Generate
Qqueries scaled to topic complexity:- quick (
depth=quick): 3-4 queries - standard (default): 6-8 queries
- thorough (
depth=thorough): 12-15 queries
Cover six angles: official-docs, best-practices, footguns, existing-tools, security, recent-changes. Always include the current year (from step 1) in queries that risk surfacing stale content.
- quick (
-
Execute search (per-path: this agent is the recall engine). Route Tavily-first:
mcp__tavily__tavily_search(the primary recall pass;search_depth=basic,advancedfor high-stakes - neverfast, which returns empty) -> cross-check the top claims withmcp__brave-search__brave_web_search-> usemcp__serper-search__google_searchonly for Google-specific operators (site:,filetype:), always passinggl: us, hl: en.tavily_search'stopicisgeneral-only in the MCP schema; route news/finance angles to Brave instead. -
Deep-read. Identify 3-5 highest-signal pages across all results. Read via
mcp__tavily__tavily_extract(handles JS-rendered content). Fall back toWebFetchonly on extract failure.
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 · 161 lines · 61 tokens per session scan A 5a95383a003e
qdev-researcher is an agent published in the GitHub repository L3DigitalNet/Claude-Code-Plugins (6 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 2,867 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 2026-08-31.
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.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.