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.
git clone --depth 1 https://github.com/iamironz/ai-config-bundleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/iamironz/ai-config-bundle/thoughts-analyzer)<a href="https://agentmods.dev/agents/iamironz/ai-config-bundle/thoughts-analyzer"><img src="https://agentmods.dev/badge/agents/iamironz/ai-config-bundle/thoughts-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/iamironz/ai-config-bundle/thoughts-analyzer"><img src="https://agentmods.dev/badge/agents/iamironz/ai-config-bundle/thoughts-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00029 | $0.01329 |
| Opus 5.5 | $0.00012 | $0.00532 |
| Sonnet 5.5 | $0.00006 | $0.00266 |
| Haiku 4.5 | $0.00003 | $0.00133 |
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 1mo 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 — 160 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.
KB / RAG (Mandatory)
Before producing your analysis, follow the KB operational loop in ai-kb/AGENTS.md
(prefer ck search for rule discovery; use ai-kb/rules/INDEX.md only as a fallback, then load the relevant rules).
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:
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.
- 1mo ago First seen · 160 lines · 29 tokens per session scan A 19aa816868c1
thoughts-analyzer is an agent published in the GitHub repository iamironz/ai-config-bundle (2 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 1,329 once invoked, about $0.0001 per session on Opus 5.5. 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
nauro-planner
Use to plan a non-trivial change before any code is written. Classifies doctrine risk (GREEN/AMBER/RED) via Nauro, writes a structured plan, and drafts decision additions, updates, or supersedes for the direct-user Delivery parent. Returns a plan and never writes project truth or edits files.
nauro-reviewer
Use to review a PR or diff. First pass looks for real bugs introduced in the change (boundary conditions, error handling, test weakening, caller mismatches); second pass audits against the PR template, Nauro decision references, and project conventions. Read-only. Flags actionable code issues and blocks on hard-rule…
nauro-tech-lead
Use to set or maintain project direction. Reads Nauro decisions, session transcripts, and PR diffs; judges architectural choices against active doctrine; and drafts complete decision additions, updates, or supersedes for the direct-user Delivery parent. Never writes project truth.
nauro-investigator
Use to investigate Nauro use and development cycles across Claude and Codex logs. Find supported defects, repeated friction, drift, useful outcomes, and gaps in follow-through. Check current code and counterevidence. Read-only; return findings and a research notebook update for the caller to save. Invoke for a bounded…
nauro-executor
Use to implement an exact approved plan. Has full source edit and test access. Runs lint and tests before declaring done. Commits locally, drafts the PR, and never pushes or opens it. Never writes project truth.
clawteam-rnd-backend
Backend R&D task agent — layered abstraction, defensive coding, consistency-first data, built-in observability, evolvable design, perf/resource awareness; architecture layers, quality trade-offs, error taxonomy, distributed consistency patterns.