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 jellydn/my-ai-tools --skill handoffsgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/handoffs)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/handoffs"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/handoffs/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/jellydn/my-ai-tools/handoffs"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/handoffs.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 | $0.00012 | $0.01149 |
| Opus 5 | $0.00006 | $0.00575 |
| Sonnet 5 | $0.00002 | $0.00230 |
| Haiku 4.5 | $0.00001 | $0.00115 |
Grade A, and why
handoffs 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 11d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Handoff Plan
Creates a detailed handoff plan of the conversation for continuing the work in a new session.
The user specified purpose:
$ARGUMENTS
You are creating a summary specifically so that it can be continued by another agent. For this to work you MUST have a purpose. If no specified purpose was provided in the <purpose>...</purpose> tag you must STOP IMMEDIATELY and ask the user what the purpose is.
Do not continue before asking for the purpose as you will otherwise not understand the instructions and do not assume a purpose!
Goal
Create a detailed summary of the conversation so far, paying close attention to the user's explicit purpose for the next steps. This handoff plan should be thorough in capturing technical details, code patterns, and architectural decisions that will be essential for continuing development work without losing context.
Process
Before providing your final plan, wrap your analysis in tags to organize your thoughts and ensure you've covered all necessary points:
- Chronologically analyze each message and section of the conversation. For each section thoroughly identify:
- The user's explicit requests and intents
- Your approach to addressing the user's requests
- Key decisions, technical concepts and code patterns
- Specific details like file names, full code snippets, function signatures, file edits, etc
- Double-check for technical accuracy and completeness, addressing each required element thoroughly.
Your plan should include the following sections:
- Primary Request and Intent: Capture all of the user's explicit requests and intents in detail
- Key Technical Concepts: List all important technical concepts, technologies, and frameworks discussed.
- Files and Code Sections: Enumerate specific files and code sections examined, modified, or created. Pay special attention to the most recent messages and include full code snippets where applicable and include a summary of why this file read or edit is important.
- Problem Solving: Document problems solved and any ongoing troubleshooting efforts.
- Pending Tasks: Outline any pending tasks that you have explicitly been asked to work on.
- Current Work: Describe in detail precisely what was being worked on immediately before this handoff request, paying special attention to the most recent messages from both user and assistant. Include file names and code snippets where applicable.
- Next Step: List the next step that you will take that is related to the most recent work you were doing. IMPORTANT: ensure that this step is DIRECTLY in line with the user's explicit requests, and the task you were working on immediately before this handoff request. If your last task was concluded, then only list next steps if they are explicitly in line with the users request. Do not start on tangential requests without confirming with the user first. Include this section only if there is an actionable next step.
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.
- 11d ago First seen · 130 lines · 12 tokens per session scan A 8becc6a93fac
handoffs is a skill published in the GitHub repository jellydn/my-ai-tools (120 stars, last pushed yesterday), licensed MIT. It adds 12 tokens to every session and 1,149 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
agent-expert-creation
Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
self-improve
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past…