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 skills add ayushcodes10/echo-mem --skill source-command-sparc-post-deployment-monitoring-modegit 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/skills/ayushcodes10/echo-mem/source-command-sparc-post-deployment-monitoring-mode)<a href="https://agentmods.dev/skills/ayushcodes10/echo-mem/source-command-sparc-post-deployment-monitoring-mode"><img src="https://agentmods.dev/badge/skills/ayushcodes10/echo-mem/source-command-sparc-post-deployment-monitoring-mode/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/skills/ayushcodes10/echo-mem/source-command-sparc-post-deployment-monitoring-mode"><img src="https://agentmods.dev/badge/skills/ayushcodes10/echo-mem/source-command-sparc-post-deployment-monitoring-mode.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 | $0.00037 | $0.00659 |
| Opus 5.5 | $0.00015 | $0.00264 |
| Sonnet 5.5 | $0.00007 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
source-command-sparc-post-deployment-monitoring-mode 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
source-command-sparc-post-deployment-monitoring-mode
Use this skill when the user asks to run the migrated source command sparc-post-deployment-monitoring-mode.
Command Template
📈 Deployment Monitor
Role Definition
You observe the system post-launch, collecting performance, logs, and user feedback. You flag regressions or unexpected behaviors.
Custom Instructions
Configure metrics, logs, uptime checks, and alerts. Recommend improvements if thresholds are violated. Use new_task to escalate refactors or hotfixes. Summarize monitoring status and findings with attempt_completion.
Available Tools
- read: File reading and viewing
- edit: File modification and creation
- browser: Web browsing capabilities
- mcp: Model Context Protocol tools
- command: Command execution
Usage
Option 1: Using MCP Tools (Preferred in Codex)
mcp__claude-flow__sparc_mode {
mode: "post-deployment-monitoring-mode",
task_description: "monitor production metrics",
options: {
namespace: "post-deployment-monitoring-mode",
non_interactive: false
}
}
Option 2: Using NPX CLI (Fallback when MCP not available)
# Use when running from terminal or MCP tools unavailable
npx Codex-flow sparc run post-deployment-monitoring-mode "monitor production metrics"
# For alpha features
npx Codex-flow@alpha sparc run post-deployment-monitoring-mode "monitor production metrics"
# With namespace
npx Codex-flow sparc run post-deployment-monitoring-mode "your task" --namespace post-deployment-monitoring-mode
# Non-interactive mode
npx Codex-flow sparc run post-deployment-monitoring-mode "your task" --non-interactive
Option 3: Local Installation
# If Codex-flow is installed locally
./Codex-flow sparc run post-deployment-monitoring-mode "monitor production metrics"
Memory Integration
Using MCP Tools (Preferred)
// Store mode-specific context
mcp__claude-flow__memory_usage {
action: "store",
key: "post-deployment-monitoring-mode_context",
value: "important decisions",
namespace: "post-deployment-monitoring-mode"
}
// Query previous work
mcp__claude-flow__memory_search {
pattern: "post-deployment-monitoring-mode",
namespace: "post-deployment-monitoring-mode",
limit: 5
}
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 · 90 lines · 37 tokens per session scan A e17761ca49b8
source-command-sparc-post-deployment-monitoring-mode is a skill published in the GitHub repository ayushcodes10/echo-mem (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 659 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-12.
Other skills, from other repositories
squish
ALWAYS use this skill when working with agents, memory, context, persistence, or AI agent state. Use when the user mentions: 'the agent forgot', 'save this', 'remember that', 'context is lost', 'memory', 'MCP server', 'agent memory', 'persistent memory', 'session context', 'project context', 'recall', 'remember'…
squish-memory
Persistent memory system for Claude Code. Use when you need to store, recall, or search project memories, facts, decisions, and learnings across sessions. Triggers on: remember, recall, memory, forget, search memories, store context, project knowledge, what did we decide, previous conversation.
postgram
Use the pgm CLI (Postgram) to persist, search, and link knowledge across sessions. Invoke whenever the user wants to remember something across conversations, recall prior context, search their personal knowledge base, track tasks, or explore relationships between stored entities. Assumes pgm is on PATH, PGMAPIURL is…
memanto-companion
Inspect and manage the cross-session engineering memory that Memanto maintains for your Claude Code skills. Use when the user asks what Memanto remembers, wants to see their engineering profile, manually recall context for a skill, or store a decision. The automatic lifecycle hooks handle capture/injection on their…
openlore
Query and publish to an OpenLore knowledge repository over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.
blueprint-from-collection-import-production
After the publish, move the copied records and their files into the production Postgres and Storage with the import proven locally.