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/eveld/claude/thoughts-analyzergit clone --depth 1 https://github.com/eveld/claudeWhat 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 | $0.01243 |
| Opus 5 | $0.00017 | $0.00622 |
| Sonnet 5 | $0.00007 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00124 |
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
91% identical to thoughts-analyzer — 13 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 — 152 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- Ignore outdated information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 format:
- Personal:
thoughts/{username}/NNNN-slug/*.md(work-in-progress, may be incomplete) - Shared:
thoughts/shared/NNNN-slug/*.md(published team docs, reviewed) - Legacy:
thoughts/shared/*/*.mdwith timestamped filenames
- Personal:
- Check frontmatter for feature_slug, shared_as, status
- 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
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 · 152 lines · 34 tokens per session scan A 32dcb2406475
thoughts-analyzer is an agent published in the GitHub repository eveld/claude (10 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 1,243 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to thoughts-analyzer, differing in 13 lines, and is treated as a copy.
Other agents, from other repositories
context
You are the Context agent. Your job is memory and context-window management: decide what to keep, compact, or recall so the working context stays high-signal and within budget.
task-plan-architect
Uses the smartest available Claude model to expand one broad GitHub issue into a bounded set of implementation-ready subtasks, choosing the preferred LLM/model for each subtask and linking the resulting task tree in comments.
ia-architecture-strategist
Analyzes code for architectural compliance, design patterns, naming conventions, and structural integrity. Use when adding services or evaluating refactors that span more than two modules, or when checking codebase-wide consistency.
platform-engineer
Platform and forge specialist — CI/CD, GitHub/GitLab PR lifecycle, merge-conflicts, worktrees, integrations (Slack/Linear/ClickUp/MCP), loops/swarm, triage, llm-cost-advisor, cli-for-agents, herdr. Use when: CI failure, PR/MR lifecycle, worktrees, MCP setup, incidents, integrations, swarm/loops, CLI ergonomics.
architecture
🇷🇺 Russian version: architecture.ru.md.
security-reviewer
인증, 권한, 결제, 데이터 삭제, 외부 입력 처리 변경 전후에 사용한다.