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/ayushcodes10/echo-memWrote 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/ayushcodes10/echo-mem/examples)<a href="https://agentmods.dev/commands/ayushcodes10/echo-mem/examples"><img src="https://agentmods.dev/badge/commands/ayushcodes10/echo-mem/examples/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/ayushcodes10/echo-mem/examples"><img src="https://agentmods.dev/badge/commands/ayushcodes10/echo-mem/examples.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 | $0.00965 |
| Opus 5.5 | $0.00000 | $0.00386 |
| Sonnet 5.5 | $0.00000 | $0.00193 |
| Haiku 4.5 | $0.00000 | $0.00097 |
Grade A, and why
examples 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.
Examples Swarm Strategy
Common Swarm Patterns
Research Swarm
Using MCP Tools
// Initialize research swarm
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 6,
"strategy": "adaptive"
})
// Spawn research agents
mcp__claude-flow__agent_spawn({
"type": "researcher",
"name": "AI Trends Researcher",
"capabilities": ["web-search", "analysis", "synthesis"]
})
// Orchestrate research
mcp__claude-flow__task_orchestrate({
"task": "research AI trends",
"strategy": "parallel",
"priority": "medium"
})
// Monitor progress
mcp__claude-flow__swarm_status({
"swarmId": "research-swarm"
})
Using CLI (Fallback)
npx claude-flow swarm "research AI trends" \
--strategy research \
--mode distributed \
--max-agents 6 \
--parallel
Development Swarm
Using MCP Tools
// Initialize development swarm
mcp__claude-flow__swarm_init({
"topology": "hierarchical",
"maxAgents": 8,
"strategy": "balanced"
})
// Spawn development team
const devAgents = [
{ type: "architect", name: "API Designer" },
{ type: "coder", name: "Backend Developer" },
{ type: "tester", name: "API Tester" },
{ type: "documenter", name: "API Documenter" }
]
devAgents.forEach(agent => {
mcp__claude-flow__agent_spawn({
"type": agent.type,
"name": agent.name,
"swarmId": "dev-swarm"
})
})
// Orchestrate development
mcp__claude-flow__task_orchestrate({
"task": "build REST API",
"strategy": "sequential",
"dependencies": ["design", "implement", "test", "document"]
})
// Enable monitoring
mcp__claude-flow__swarm_monitor({
"swarmId": "dev-swarm",
"interval": 5000
})
Using CLI (Fallback)
npx claude-flow swarm "build REST API" \
--strategy development \
--mode hierarchical \
--monitor \
--output sqlite
Analysis Swarm
Using MCP Tools
// Initialize analysis swarm
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 5,
"strategy": "adaptive"
})
// Spawn analysis agents
mcp__claude-flow__agent_spawn({
"type": "analyst",
"name": "Code Analyzer",
"capabilities": ["static-analysis", "complexity-analysis"]
})
mcp__claude-flow__agent_spawn({
"type": "analyst",
"name": "Security Analyzer",
"capabilities": ["security-scan", "vulnerability-detection"]
})
// Parallel analysis execution
mcp__claude-flow__parallel_execute({
"tasks": [
{ "id": "analyze-code", "command": "analyze codebase structure" },
{ "id": "analyze-security", "command": "scan for vulnerabilities" },
{ "id": "analyze-performance", "command": "identify bottlenecks" }
]
})
// Generate comprehensive report
mcp__claude-flow__performance_report({
"format": "detailed",
"timeframe": "current"
})
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 61e11e72b616
examples is a command published in the GitHub repository ayushcodes10/echo-mem (2 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 965 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 2026-09-10.
Other commands, from other repositories
memory-recall
Search and recall relevant memories for the current context.
memory-store
Store an insight, decision, or pattern to memory.
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.
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.