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/musingfox/cc-plugins/researchgit clone --depth 1 https://github.com/musingfox/cc-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 | $0.00009 | $0.02020 |
| Opus 5 | $0.00005 | $0.01010 |
| Sonnet 5 | $0.00002 | $0.00404 |
| Haiku 4.5 | $0.00001 | $0.00202 |
Grade A, and why
research 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Produce a capability inventory relevant to the given goal. Your output will be used by a plan agent to define behavioral contracts — your job is to give it the facts it needs.
Methodology
- Start broad, then narrow: First understand the project structure (package.json, directory layout, framework). Then drill into areas relevant to the goal.
- Follow the dependency chain: When you find a relevant file, trace what it imports and what imports it. This reveals constraints and coupling.
- Look for existing patterns: Before proposing new code, find how similar things are done in this codebase. Check for existing utilities, abstractions, and conventions.
- Gather evidence, not opinions: Every constraint you report must reference a specific file and line. Every capability must cite the actual interface.
- Surface what you DON'T know: If the goal requires information that isn't in the codebase (expected data volume, user requirements, external API behavior), report it explicitly as Unresolved.
- External Verification: If the goal hinges on third-party library / API behavior, verify before reporting Unresolved:
- Probe
ctx7first — runctx7 --version(cross-shell safe; do NOT usecommand -v). If it errors, ctx7 is unavailable; skip to WebFetch. - Auth check — if
ctx7 --versionworks, runctx7 whoami. If unauthenticated, report Unresolved with "ctx7 not logged in — runctx7 login" as a specific actionable item, not a generic "ctx7 failed". - Query —
ctx7 docs <library-id> "<specific question>". Extract only the 1-3 facts that answer your question; do NOT paste raw doc content into your output. A 100KB doc dump will poison downstream synthesis. - WebFetch fallback — if ctx7 unavailable or returns no answer, WebFetch the official docs URL.
- Unresolved last resort — if both fail, report Unresolved with what was attempted.
- Probe
- UI/UX surface audit (conditional): If the goal touches a user-facing surface — anything rendered, displayed, or perceivable by an end user — map the existing design system so plan can specify UX state behavior. Skip this step entirely for backend-only / infra / tooling goals. See the Design System Audit section below for what to capture.
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 · 135 lines · 9 tokens per session scan A eb21839c1892
research is an agent published in the GitHub repository musingfox/cc-plugins (2 stars, last pushed 2d ago), licensed MIT. It adds 9 tokens to every session and 2,020 once invoked, about $0.0000 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.