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 agentmods add commands/lancejames221b/agent-hivemind/suggestgit clone --depth 1 https://github.com/lancejames221b/agent-hivemindWrote 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/lancejames221b/agent-hivemind/suggest)<a href="https://agentmods.dev/commands/lancejames221b/agent-hivemind/suggest"><img src="https://agentmods.dev/badge/commands/lancejames221b/agent-hivemind/suggest.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 | $0.00015 | $0.03445 |
| Opus 5 | $0.00008 | $0.01723 |
| Sonnet 5 | $0.00003 | $0.00689 |
| Haiku 4.5 | $0.00002 | $0.00345 |
Grade A, and why
suggest 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 4d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
suggest - AI-Powered Command Suggestions
Purpose
Intelligent command suggestion system that uses AI and collective intelligence to recommend the most appropriate hAIveMind commands based on your current context, recent activity, system state, and stated intent.
When to Use
- Uncertainty: When you're not sure which command to use for your situation
- Optimization: Looking for more efficient ways to accomplish tasks
- Learning: Discovering new commands or usage patterns
- Complex Situations: Multi-faceted problems requiring coordinated command sequences
- Emergency Response: Quick suggestions for incident response and troubleshooting
- Workflow Planning: Getting recommendations for next steps in operational procedures
Syntax
suggest [context] [intent]
Parameters
- context (optional): Current situation or domain
- Examples:
incident,security,deployment,monitoring,database,python
- Examples:
- intent (optional): What you're trying to accomplish
- Examples:
troubleshoot,optimize,monitor,deploy,investigate,document
- Examples:
AI-Powered Suggestion Features
Context Intelligence
- Project Detection: Automatically detects Python, Node.js, Rust, Go projects and suggests relevant commands
- Incident Awareness: Prioritizes incident response commands during active system issues
- Agent Status: Considers available specialist agents when suggesting delegation commands
- Recent Activity: Analyzes your recent commands to suggest logical next steps
- System Health: Factors in current system status and performance metrics
Intent Recognition
- Natural Language Processing: Understands intent from context clues and recent activity
- Goal-Oriented Suggestions: Recommends command sequences to achieve specific objectives
- Workflow Completion: Suggests commands to complete started workflows
- Problem-Solution Matching: Maps current problems to proven solution patterns
- Efficiency Optimization: Recommends faster or more effective approaches
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.
- 4d ago First seen · 350 lines · 0 tokens per session scan A 372afedf9e3e
suggest is a command published in the GitHub repository lancejames221b/agent-hivemind (7 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 3,445 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.