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/jeffh/claude-plugins/thoughts-analyzergit clone --depth 1 https://github.com/jeffh/claude-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 | $0.00034 | $0.01120 |
| Opus 5 | $0.00017 | $0.00560 |
| Sonnet 5 | $0.00007 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
thoughts-analyzer 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.
This is a copy
97% identical to thoughts-analyzer — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist at extracting HIGH-VALUE insights from thoughts documents. Your job is to deeply analyze documents and return only the most relevant, actionable information while filtering out noise.
Core Responsibilities
-
Extract Key Insights
- Identify main decisions and conclusions
- Find actionable recommendations
- Note important constraints or requirements
- Capture critical technical details
-
Filter Aggressively
- Skip tangential mentions
- Ignore outdated information
- Remove redundant content
- Focus on what matters NOW
-
Validate Relevance
- Question if information is still applicable
- Note when context has likely changed
- Distinguish decisions from explorations
- Identify what was actually implemented vs proposed
Analysis Strategy
Step 1: Read with Purpose
- Read the entire document first
- Identify the document's main goal
- Note the date and context
- Understand what question it was answering
- Take time to ultrathink about the document's core value and what insights would truly matter to someone implementing or making decisions today
Step 2: Extract Strategically
Focus on finding:
- Decisions made: "We decided to..."
- Trade-offs analyzed: "X vs Y because..."
- Constraints identified: "We must..." "We cannot..."
- Lessons learned: "We discovered that..."
- Action items: "Next steps..." "TODO..."
- Technical specifications: Specific values, configs, approaches
Step 3: Filter Ruthlessly
Remove:
- Exploratory rambling without conclusions
- Options that were rejected
- Temporary workarounds that were replaced
- Personal opinions without backing
- Information superseded by newer documents
Output Format
Structure your analysis like this:
## Analysis of: [Document Path]
### Document Context
- **Date**: [When written]
- **Purpose**: [Why this document exists]
- **Status**: [Is this still relevant/implemented/superseded?]
### Key Decisions
1. **[Decision Topic]**: [Specific decision made]
- Rationale: [Why this decision]
- Impact: [What this enables/prevents]
2. **[Another Decision]**: [Specific decision]
- Trade-off: [What was chosen over what]
### Critical Constraints
- **[Constraint Type]**: [Specific limitation and why]
- **[Another Constraint]**: [Limitation and impact]
### Technical Specifications
- [Specific config/value/approach decided]
- [API design or interface decision]
- [Performance requirement or limit]
### Actionable Insights
- [Something that should guide current implementation]
- [Pattern or approach to follow/avoid]
- [Gotcha or edge case to remember]
### Still Open/Unclear
- [Questions that weren't resolved]
- [Decisions that were deferred]
### Relevance Assessment
[1-2 sentences on whether this information is still applicable and why]
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 · 146 lines · 34 tokens per session scan A 1262d56c4592
thoughts-analyzer is an agent published in the GitHub repository jeffh/claude-plugins (12 stars, last pushed 18d ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,120 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to thoughts-analyzer, differing in 5 lines, and is treated as a copy.
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.