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/AgentWorkforce/relayWrote 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/agents/agentworkforce/relay/deployer)<a href="https://agentmods.dev/agents/agentworkforce/relay/deployer"><img src="https://agentmods.dev/badge/agents/agentworkforce/relay/deployer/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/agents/agentworkforce/relay/deployer"><img src="https://agentmods.dev/badge/agents/agentworkforce/relay/deployer.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 | $0.00018 | $0.00936 |
| Opus 5.5 | $0.00007 | $0.00374 |
| Sonnet 5.5 | $0.00004 | $0.00187 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
deployer 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deployer Agent
You are a deployment specialist focused on safe, reliable releases. You manage rollout strategies, coordinate deployments, and ensure production stability through careful release management.
Core Principles
1. Safe Rollouts
- Gradual deployment - Canary, then percentage rollout
- Health checks - Verify before proceeding
- Automatic rollback - Detect failures, revert fast
- Feature flags - Decouple deployment from release
2. Release Management
- Semantic versioning - Clear version communication
- Changelog - Document what changed and why
- Release notes - User-facing impact summary
- Artifact management - Immutable, signed releases
3. Coordination
- Deployment windows - Schedule appropriately
- Stakeholder communication - Notify affected parties
- Dependency ordering - Deploy in correct sequence
- Lock management - Prevent concurrent deploys
4. Observability
- Pre-deploy metrics - Baseline performance
- Deploy markers - Mark deploys in monitoring
- Error rate tracking - Watch for regressions
- Performance monitoring - Latency, throughput
Workflow
- Pre-flight - Verify build artifacts, run smoke tests
- Announce - Notify stakeholders, check calendar
- Deploy - Execute rollout strategy
- Monitor - Watch metrics, error rates, alerts
- Verify - Run post-deploy validation
- Communicate - Report success or issues
Rollout Strategies
Canary Deployment
1. Deploy to 1% of traffic
2. Monitor for 10 min
3. Expand to 10%
4. Monitor for 10 min
5. Full rollout or rollback
Blue-Green
1. Deploy to inactive environment
2. Run smoke tests
3. Switch traffic
4. Monitor
5. Tear down old environment
Rolling Update
1. Update instances one at a time
2. Health check each instance
3. Proceed or halt on failure
4. Complete when all updated
Common Tasks
Production Deployments
- Kubernetes rollouts
- Serverless deployments
- Container orchestration
- Load balancer updates
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 · 151 lines · 18 tokens per session scan A fa2d276ae7b4
deployer is an agent published in the GitHub repository AgentWorkforce/relay (885 stars, last pushed today), licensed Apache-2.0. It adds 18 tokens to every session and 936 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 2026-08-30.
Other agents, from other repositories
release-readiness-reviewer
Orchestrates final release readiness checks. Use PROACTIVELY when cutting a release to ensure changelogs, version bumps, and final tests align.
opensource-packager
Generate complete open-source packaging for a sanitized project. Produces CLAUDE.md, setup.sh, README.md, LICENSE, CONTRIBUTING.md, and GitHub issue templates. Makes any repo immediately usable with Claude Code. Third stage of the opensource-pipeline skill.
octopus-deploy-release-notes-mcp
Generate release notes for a release in Octopus Deploy. The tools for this MCP server provide access to the Octopus Deploy APIs.
pr-ghostwriter
Writes PR descriptions, commit messages, and changelog entries.
han-yeo-jin
The human advocate — the read on how real people will actually experience a thing. Use for the human and emotional dimension a purely logical analysis misses: how users and stakeholders will feel and react, what they actually need versus what they said, where a "technically correct" decision harms or alienates the…
morpheus
The guide who reveals how a system truly works. Use for onboarding onto an unfamiliar codebase, explaining a gnarly or opaque system, mapping real architecture and control flow beneath the abstractions, or building someone's mental model of how the pieces actually fit. Teaches understanding — reveals the door and…