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 skills add Kastalien-Research/thoughtbox --skill thoughtbox-onboardgit clone --depth 1 https://github.com/Kastalien-Research/thoughtboxWrote 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/skills/kastalien-research/thoughtbox/thoughtbox-onboard)<a href="https://agentmods.dev/skills/kastalien-research/thoughtbox/thoughtbox-onboard"><img src="https://agentmods.dev/badge/skills/kastalien-research/thoughtbox/thoughtbox-onboard/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/skills/kastalien-research/thoughtbox/thoughtbox-onboard"><img src="https://agentmods.dev/badge/skills/kastalien-research/thoughtbox/thoughtbox-onboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00110 | $0.02425 |
| Opus 5.5 | $0.00044 | $0.00970 |
| Sonnet 5.5 | $0.00022 | $0.00485 |
| Haiku 4.5 | $0.00011 | $0.00243 |
Grade A, and why
thoughtbox:onboard 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thoughtbox Onboarding
Thoughtbox is an MCP server that gives you a structured reasoning workspace. It persists your thinking across sessions, lets you branch and revise ideas, and builds a knowledge graph from your insights. This guide gets you productive in 5 minutes.
What You Have
Seven modules, two tools:
| Module | What it does | Access via |
|---|---|---|
| thought | Record structured reasoning steps with types, branching, revision | tb.thought() |
| session | List, search, resume, export, analyze reasoning sessions | tb.session.* |
| knowledge | Entity graph with observations, relations, traversal | tb.knowledge.* |
| notebook | Literate programming — create cells, execute code, export | tb.notebook.* |
| theseus | Friction-gated refactoring protocol (scope locking, visa system) | tb.theseus() |
| ulysses | Surprise-gated debugging protocol (S-register, forced reflection) | tb.ulysses() |
| observability | Health checks, session monitoring, cost tracking | tb.observability() |
Two MCP tools give you access to everything:
thoughtbox_search— query the operation catalog (what's available, schemas, examples)thoughtbox_execute— run JavaScript using thetbSDK to chain operations
Quick Start: Your First Session
1. Record a thought
// thoughtbox_execute
async () => {
return await tb.thought({
thought: "Analyzing the authentication flow for security gaps",
thoughtType: "reasoning",
nextThoughtNeeded: true,
thoughtNumber: 1,
totalThoughts: 10,
sessionTitle: "Auth Security Review",
sessionTags: ["security", "auth"]
});
}
The first thought creates a session automatically. Subsequent thoughts append to it.
2. Branch to explore alternatives
async () => {
// Branch from thought 3 to explore two options
await tb.thought({
thought: "Option A: Token rotation with short-lived JWTs",
thoughtType: "reasoning",
nextThoughtNeeded: true,
thoughtNumber: 4,
totalThoughts: 10,
branchFromThought: 3,
branchId: "jwt-rotation"
});
}
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 · 285 lines · 110 tokens per session scan A 8f9d744a4b36
thoughtbox:onboard is a skill published in the GitHub repository Kastalien-Research/thoughtbox (65 stars, last pushed 3mo ago), licensed MIT. It adds 110 tokens to every session and 2,425 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
handoff
Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.
agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are supported, or when a connect adapter writes the wrong config path.
agentmemory-hooks
The agentmemory plugin hooks that capture observations automatically across the agent session lifecycle. Use when explaining how memory gets captured without manual saves, when debugging missing observations, or when tuning what gets recorded.
run-memory
Shared step of the marketing playbooks (analyze-performance, run-paid-campaigns, research-competitors, email-lifecycle): reads the last run's record, checks whether its recommendations were done and whether they worked, and saves a short record of this run for the next one. Loaded by those playbooks; not for direct…
save-learning
Shared step of the marketing playbooks (make-static-ads, analyze-performance, email-lifecycle, write-outreach): saves a short brand rule or finding in the human's own words, one idea per rule, retires the playbook's own older rules it replaces, and reads the result back to confirm. Loaded by those playbooks; not for…
lc-curate-context
Decide which files a task actually needs, record that as a reusable llm-context rule, verify it against the codebase - including the files your selection references but leaves out - and pack it for your own context, a chat, or a sub-agent you dispatch. Load when choosing what code to put in front of a model, packing…