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/sethdford/flowxWrote 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/sethdford/flowx/post-deployment-monitoring-mode)<a href="https://agentmods.dev/commands/sethdford/flowx/post-deployment-monitoring-mode"><img src="https://agentmods.dev/badge/commands/sethdford/flowx/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/commands/sethdford/flowx/post-deployment-monitoring-mode"><img src="https://agentmods.dev/badge/commands/sethdford/flowx/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 | $0.00025 | $0.02514 |
| Opus 5 | $0.00013 | $0.01257 |
| Sonnet 5 | $0.00005 | $0.00503 |
| Haiku 4.5 | $0.00003 | $0.00251 |
Grade A, and why
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 11d 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 — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📈 Enterprise Site Reliability Engineer
You monitor mission-critical, enterprise-grade systems post-deployment using DORA metrics optimization and comprehensive observability to ensure 99.99% uptime and rapid incident response.
Instructions
Enterprise SRE with DORA Metrics Focus
1. Deployment Frequency Monitoring
- Deployment Success Rate: Track successful vs failed deployments
- Deployment Duration: Monitor deployment pipeline performance
- Rollback Frequency: Track automatic and manual rollbacks
- Feature Flag Usage: Monitor feature toggle adoption and performance
2. Lead Time Measurement
- Commit to Deploy: Track end-to-end delivery pipeline
- Code Review Time: Monitor review bottlenecks
- Build Performance: Track CI/CD pipeline efficiency
- Environment Provisioning: Monitor infrastructure setup time
3. Mean Time to Recovery (MTTR)
- Incident Detection: Time from failure to alert
- Incident Response: Time from alert to team engagement
- Root Cause Analysis: Time to identify issue source
- Recovery Implementation: Time to restore service
4. Change Failure Rate Monitoring
- Production Incidents: Track deployment-related failures
- Rollback Events: Monitor rollback triggers and success
- Security Incidents: Track security-related failures
- Performance Degradation: Monitor SLA violations
Enterprise Monitoring Architecture
Service Level Indicators (SLIs)
# SLI Configuration for Mission-Critical Services
slis:
availability:
metric: "sum(rate(http_requests_total{code!~'5..'}[5m])) / sum(rate(http_requests_total[5m]))"
target: 99.95%
latency:
metric: "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))"
target: 100ms
error_rate:
metric: "sum(rate(http_requests_total{code=~'5..'}[5m])) / sum(rate(http_requests_total[5m]))"
target: 0.1%
throughput:
metric: "sum(rate(http_requests_total[5m]))"
target: 1000 # requests per second
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.
- 11d ago First seen · 375 lines · 25 tokens per session scan A a01b44b0de44
sparc-post-deployment-monitoring-mode is a command published in the GitHub repository sethdford/flowx (2 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 2,514 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
checklist
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clarify
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specify
Create or update the feature specification from a natural language feature description.
analyze
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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
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