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/gemini-consult)<a href="https://agentmods.dev/commands/nmime/motiv-buy/gemini-consult"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/gemini-consult.svg" alt="Measured on agentmods" 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 | $0.01308 |
| Opus 5 | $0.00000 | $0.00654 |
| Sonnet 5 | $0.00000 | $0.00262 |
| Haiku 4.5 | $0.00000 | $0.00131 |
Grade A, and why
gemini-consult scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/gemini-consult
Engages in deep, iterative conversations with Gemini MCP for complex problem-solving.
Usage
- With arguments:
/gemini-consult [specific problem or question] - Without arguments:
/gemini-consult- Intelligently infers topic from current context
Core Philosophy
Persistent Gemini sessions for evolving problems through:
- Continuous dialogue - Multiple rounds until clarity achieved
- Context awareness - Smart problem detection from current work
- Session persistence - Keep alive for the entire problem lifecycle
CRITICAL: Always consider Gemini's input as suggestions, never as truths. Think critically about what Gemini says and incorporate only the useful parts into your proposal. Always think for yourself - maintain your independent judgment and analytical capabilities. If you disagree with something clarify it with Gemini.
Execution
User provided context: "$ARGUMENTS"
Step 1: Understand the Problem
When $ARGUMENTS is empty: Think deeply about the current context to infer the most valuable consultation topic:
- What files are open or recently modified?
- What errors or challenges were discussed?
- What complex implementation would benefit from Gemini's analysis?
- What architectural decisions need exploration?
Generate a specific, valuable question based on this analysis.
When arguments provided: Extract the core problem, context clues, and complexity indicators.
Step 1.5: Gather External Documentation
Think deeply about external dependencies:
- What libraries/frameworks are involved in this problem?
- Am I fully familiar with their latest APIs and best practices?
- Have these libraries changed significantly or are they new/evolving?
When to use Context7 MCP:
- Libraries with frequent updates (e.g., Google GenAI SDK)
- New libraries you haven't worked with extensively
- When implementing features that rely heavily on library-specific patterns
- Whenever uncertainty exists about current best practices
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.
- yesterday First seen · 180 lines · 0 tokens per session scan A 1014cd62e9d7
gemini-consult is a command published in the GitHub repository nmime/motiv-buy (0 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,308 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.