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 skills/sjarmak/agent-workflows/migratenpx skills add sjarmak/agent-workflows --skill migrategit clone --depth 1 https://github.com/sjarmak/agent-workflowsWhat 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 | $0.00000 | $0.02591 |
| Opus 5 | $0.00000 | $0.01295 |
| Sonnet 5 | $0.00000 | $0.00518 |
| Haiku 4.5 | $0.00000 | $0.00259 |
Grade A, and why
migrate 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 3d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Strategy for System Transitions. Spawns N independent agents in isolated worktrees, each implementing a DIFFERENT migration strategy for the same old-system-to-new-system transition. The path is the variable, not the destination — all agents implement the same end state but via different transition paths. Each agent prototypes the critical path (the riskiest step) of their strategy, not the full migration. A lead agent then synthesizes all strategies into a comparison across rollback safety, data integrity, downtime, and coordination cost.
Arguments
$ARGUMENTS — format: [N] [path/to/migration_plan.md or inline description of old → new] where N is optional (default: 4, min 2, max 6)
Parse Arguments
Extract:
- agent_count: optional leading integer (default 4, min 2, max 6)
- migration_input: a file path to a migration plan, design doc, or architecture doc — or an inline description of the old system and new system
If the migration input is missing or unclear, ask the user to clarify. At minimum you need: what is the old system, what is the new system, and what direction the migration goes.
Phase 1: Understand the Migration
If a file path is given: read it and extract the migration context.
If inline: parse the description.
Prepare a migration brief that includes:
- Old system — what exists today, its architecture, data stores, interfaces
- New system — what the target state looks like
- State to preserve — data, configurations, user sessions, external contracts, SLAs that must survive the transition
- Constraints — downtime budget (zero? maintenance window?), team size, rollback requirements, compliance needs
- Integration surface — what external systems depend on the old system (APIs, message queues, cron jobs, downstream consumers)
- Data characteristics — volume, velocity, schema differences between old and new
- Critical invariants — things that must NEVER break during migration (e.g., "no duplicate transactions", "no lost orders", "auth must never be down")
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.
- 3d ago First seen · 198 lines · 0 tokens per session scan A 8149d238fcc3
migrate is a skill published in the GitHub repository sjarmak/agent-workflows (9 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,591 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 2026-08-31.
Other skills, from other repositories
speckit.specify
Skill "speckit.specify" from caipe-io/ai-platform-engineering, covering user input, outline, quick guidelines, section requirements and for ai generation.
speckit.clarify
Skill "speckit.clarify" from caipe-io/ai-platform-engineering, covering user input and outline.
local-integration-testing
Run end-to-end integration tests with all 15 agents and supervisor in local Docker Compose dev environment. Validates agent discovery, multi-agent routing, checkpoint persistence, and cross-agent follow-up conversations.
release-docs
Generate a combined release blog post for ai-platform-engineering. Produces a single docs/releases/YYYY-MM-DD-release-X-Y-Z.md file containing release notes and the upgrade guide (migration guide) inline. Use when cutting a release, when a user asks "what changed in 0.4.x", or when upgrading their values.yaml to a new…
update-docs
Audit and update all documentation moving parts for ai-platform-engineering. Checks release blog posts, features page, agent docs, homepage version strings, Docusaurus version config, and sidebar completeness. Fixes what is stale and reports what needs manual attention. Use after cutting a release, adding a new agent…
docker-compose-first-install
Validate and repair the OSS first-install Docker Compose path. Use when editing docker-compose.yaml, docker-compose.dev.yaml, .env.example, release image tags, Compose profiles, Keycloak/OpenFGA/RAG defaults, or first-launch UI behavior for local all-in-one installs.