procesio-platform-advisor
Decide what is worth building.
Before automating, separate product capability from assumptions about fit, cost, capacity and risk.
What it helps the agent do
- Assess feasibility and architecture trade-offs.
- Compare automation approaches, hosting options and integration boundaries.
- Identify the pricing, compliance and capacity evidence still needed.
- Estimate single-workload demand with an offline calculator and supplied measurements.
What you should get
A conditional recommendation: requirements, assumptions, architecture options, missing evidence and a sensible next proof.
Where it hands off
To the CLI operator for implementation. To accountable product, commercial or security owners for facts that require their evidence.
Representative request“We process a daily batch of orders. Can PROCESIO support the workflow, and what must we measure before sizing it?”
Illustrative request, not an executed benchmark.
Know the boundary. No certified product fit, current price catalogue, compliance attestation or guaranteed EE capacity. The calculator accepts assumptions; it does not authenticate them.
Mechanics Evidence-led references · offline capacity helper · operational handoff
procesio-cli
Turn a goal into bounded operations.
Discover the right capability, inspect the current state, obtain approval, execute deliberately and check the real outcome.
What it helps the agent do
- Create, change, validate, run and diagnose processes.
- Work with forms, documents, connectors and custom actions.
- Handle schedules, webhooks, data models and credential configuration.
- Move resources through controlled export/import workflows.
- Inspect outputs and side effects instead of trusting status alone.
What you should get
An explicit target and action, a bounded approval request, observable evidence and an honest list of unverified outcomes.
Where it hands off
To the SQL optimizer for query performance; the maintainer for executable defects; the advisor for product-fit decisions.
Representative request“Build a process that produces a document, then inspect the actual generated file—not just the template.”
Illustrative request. Execution and its effects require approval.
Know the boundary. The skill requires approval and direct proof, but these instructions are not universally enforced by the runtime. A verification command or successful status alone is not proof of correct output. Connector guidance is not autonomous end-to-end execution.
Mechanics 9 operational playbooks · registered CLI/MCP capabilities · executable agent guidance
sql-server-optimizer
Improve the query. Preserve the answer.
Treat speed and correctness as a paired problem: understand the workload, propose a change and measure without changing the result contract.
What it helps the agent do
- Analyze T-SQL, plans, indexes, reads and blocking.
- Investigate parameter sensitivity, conversions and sargability.
- Preserve duplicates, NULLs, ordering, transactions and isolation.
- Plan native typed SQL parameters for PROCESIO integrations.
What you should get
A grounded diagnosis, a candidate rewrite or index change, a measurement plan and explicit semantic checks.
Where it hands off
To the CLI operator for the surrounding process and native binding proof. Real database execution needs an approved target and scope.
Representative request“This report is slow for some parameter values. Compare the plans and improve it without changing rows, duplicates or NULL behavior.”
Illustrative request, not a measured query speedup.
Know the boundary. SQL Server only—not PostgreSQL or MySQL. No autonomous tuning or executable result-equivalence guarantee. Metadata exporters require scope and completeness review; read-only does not mean safe to share. Without measurements, a rewrite remains a candidate.
Mechanics T-SQL decision rules · context-export helpers · typed-parameter guidance
procesio-cli-maintainer
Improve the toolbox itself.
When the missing piece is in the repository, work at the implementation boundary—not around it with another prompt.
What it helps the agent do
- Add or repair CLI actions and their manifests.
- Maintain registries, MCP adapters and executable agents.
- Preserve JSON/error contracts, credential handling and compatibility.
- Update tests, CI and generated routing/documentation together.
- Integrate already-designed skills into repository conventions.
What you should get
A coherent repository change with matching contracts, regeneration, focused regression tests and clear validation limits.
Where it hands off
To the operator skill for platform work. To Agent Skill Engineer when the primary deliverable is the skill’s behavior or evaluation design.
Representative request“Expose this supported platform action through the CLI and MCP. Keep the schema, handler, generated router and tests in sync.”
Illustrative repository task. Not proof that an arbitrary missing action is already implemented.
Know the boundary. This is maintenance guidance, not blanket permission to mutate a workspace, publish a release or claim every integration is qualified. Local tests and live platform acceptance remain different evidence.
Mechanics Manifests + implementations · generated artifacts · regression and compatibility gates
agent-skill-engineer
Turn experience into a better method.
Build the smallest reusable intervention that changes a decision—and distinguish a promising instruction from demonstrated improvement.
What it helps the agent do
- Author, improve, split, merge and audit skill packages.
- Define trigger boundaries and explicit neighboring owners.
- Design fixed-rubric baselines, A/A noise checks and A/B comparisons.
- Separate development cases from held-out evaluation.
- Route field lessons to skills, tools or runtime owners.
What you should get
A bounded skill change, its causal hypothesis, regression cases, evidence tier and remaining proof—not just more prose.
Where it hands off
To domain experts for domain truth and maintainers for executable enforcement. Accountable review remains necessary for high-consequence claims.
Representative request“Agents keep repeating an action after a decoder fails. Identify the cause, improve the smallest responsible layer and test an adjacent case.”
Illustrative improvement task. It does not trigger automatic training.
Know the boundary. Not automatic learning after every run. Helpers scaffold, audit and check supplied optimization reports; they do not generate or judge model responses. Final-test integrity is not fully enforced by the controller. Current changes are not behaviorally release-qualified.
Mechanics Scaffold + audit helpers · bounded optimization controller · reviewed field-learning method