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/nmime/motiv-buyWrote 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/commands/nmime/motiv-buy/handoff)<a href="https://agentmods.dev/commands/nmime/motiv-buy/handoff"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/handoff/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/commands/nmime/motiv-buy/handoff"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/handoff.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 | $0.00000 | $0.01151 |
| Opus 5.5 | $0.00000 | $0.00460 |
| Sonnet 5.5 | $0.00000 | $0.00230 |
| Haiku 4.5 | $0.00000 | $0.00115 |
Grade A, and why
handoff 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are concluding work on the current project and need to create a comprehensive handoff for the next AI session. This command intelligently analyzes your current session achievements and updates the handoff document with both auto-detected progress and user-provided context.
Auto-Loaded Project Context:
@docs/ai-context/HANDOFF.md @/CLAUDE.md
Step 1: Process User Arguments
Handle the arguments flexibly:
- With Arguments:
$ARGUMENTSprovides user context about what was accomplished or attempted - Without Arguments: Focus purely on auto-detection from session analysis
User provided context: "$ARGUMENTS"
Step 2: Analyze Current Session Achievements
Think about what was accomplished in this session and how to best capture it for handoff. Review your recent conversation and tool usage to identify significant work:
Auto-Detect Evidence of:
- File Operations (Write, Edit, MultiEdit tools) - what files were modified and why
- New Features - functionality added or implemented
- Bug Fixes - issues resolved or debugging attempts
- Architecture Changes - structural improvements or refactoring
- Configuration Updates - settings, dependencies, or environment changes
- Documentation Work - updates to documentation files
- Incomplete Work - attempts that didn't reach completion
- Blockers Encountered - issues that prevented completion
Generate Session Summary:
Session Analysis:
- Primary work area: [component/domain affected]
- Main accomplishments: [key achievements]
- Files modified: [list of changed files]
- Status: [completed/in-progress/blocked]
- User context: [if $ARGUMENTS provided]
Step 3: Analyze Auto-Loaded HANDOFF.md
Analyze the auto-loaded docs/ai-context/HANDOFF.md to understand:
- Existing sections and their current status
- Related ongoing work that might connect to your session
- Structure and formatting patterns to maintain consistency
- Unrelated content that should be preserved
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 · 169 lines · 0 tokens per session scan A a9395ca6a819
handoff is a command published in the GitHub repository nmime/motiv-buy (0 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,151 tokens. 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 2026-09-04.
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