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/remembergit clone --depth 1 https://github.com/lancejames221b/agent-hivemindWhat 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 | $0.00011 | $0.02162 |
| Opus 5 | $0.00005 | $0.01081 |
| Sonnet 5 | $0.00002 | $0.00432 |
| Haiku 4.5 | $0.00001 | $0.00216 |
Grade A, and why
remember 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 2d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
remember - Knowledge Storage
Purpose
Store valuable knowledge, experiences, solutions, and insights in the hAIveMind collective memory for future reference by all agents.
When to Use
- Problem Solutions: Save successful fixes and workarounds
- Configuration Changes: Document important system modifications
- Lessons Learned: Record insights from incidents or projects
- Best Practices: Share effective procedures and approaches
- Important Discoveries: Save research findings or optimization techniques
- Team Knowledge: Preserve expertise that others can benefit from
Syntax
remember "content to store" [category] [options]
Parameters
- content (required): The knowledge, solution, or information to store
- category (optional): Memory classification for better organization
infrastructure: System configs, hardware, network setupincidents: Problem reports, root causes, resolutionssecurity: Vulnerabilities, patches, security proceduresdeployments: Release processes, rollback proceduresmonitoring: Alert configs, dashboard setups, metricsrunbooks: Step-by-step procedures, automation scriptsproject: Project-specific knowledge and context
- options (optional):
--tags="tag1,tag2": Manual tags for better searchability--private: Store only for this machine (not shared)--important: Mark as high-priority memory--expires=days: Auto-delete after N days (default: never)
Memory Processing Intelligence
Automatic Content Analysis
- Smart Categorization: AI determines most appropriate category
- Tag Generation: Automatically extracts relevant keywords
- Sentiment Analysis: Identifies success/failure patterns
- Technical Extraction: Parses commands, configs, error codes
- Relationship Mapping: Links to related existing memories
Content Enhancement
- Context Addition: Adds timestamp, machine, agent info
- Search Optimization: Processes content for better discoverability
- Version Tracking: Links to previous versions if updated
- Cross-References: Identifies connections to other memories
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.
- 2d ago First seen · 258 lines · 0 tokens per session scan A b4f0cb42259d
remember is a command published in the GitHub repository lancejames221b/agent-hivemind (7 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 2,162 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.