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managed-workflow

Use this skill to run bounded remote workflows for tasks with independent parts and clear acceptance checks. It helps inspect worker activity and verify results against the original requirements; a completed run alone does not establish correctness.

OriginalShow the original description

Discover and run bounded remote dynamic workflows with Ruflo mod policy, worker inspection and independent acceptance gates

ruvnet/ruflo80,108 stars(GitHub 80,108 + cccskills 0)

Run score Not tested

See how to install

Good for

  • Splitting a task into independent remote work
  • Checking worker progress and failures
  • Verifying results against acceptance criteria
  • Reviewing recommendations for a repeated task

Overview

What it does

This skill runs tasks with independent parts and clear acceptance criteria as managed remote workflows. Before work begins, it organizes the scope, source commit, output format, deadline, budget, allowed network hosts, and independent acceptance test, then assigns workers within the host policy. During the run, it checks worker events, pauses, and failures. Afterward, it verifies each result against the original source and requirements. Verified outcomes can be recorded as learning evidence with a signature and supporting details.

When it helps

Use it to run independent research or implementation tasks in parallel, monitor remote work, and check its results. It can also help when you want to state budget and permission limits for a request. It is not intended to split a simple task just because remote tools are available.

Example requests

  • “Split this research into independent tasks and check the results against these criteria.”
  • “Check each worker’s progress, then compare the completed results with the source material.”
  • “Consult the previous verified record and see whether its recommendation applies to this task.”

Notes

This skill requires Ruflo’s managed agent features and host-approved settings. Do not include credentials or MCP tokens in prompts. A run marked complete does not prove that its results are correct; verify them independently. Remote readiness, permissions, and budget must be checked under the host policy.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Document check

AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).

SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingRemote work and termination are covered, with limits on credentials and permissions.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingRuflo features and host settings are required; setup steps are not provided.

Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 25, 2026. Treat it as a guide and read the original before installing.

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Files included (1)

  • SKILL.md5.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Bounded managed workflows

Use this for remote Claude tasks with independent pieces and explicit acceptance tests. Use wasm-agent for local isolated work, and ruflo-workflows for persisted approval gates or native workflow scripts. Do not turn a simple task into remote fanout just because tools are registered.

  1. Inspect managed_agent_capabilities. Registration, configured credentials, provider beta access, tool authorization and remote health are separate facts. A configured credential is not proof of access. Never infer remote readiness.
  2. The agent mod provides /agent-mod capabilities and the workflow mod provides /wf-mod managed. These commands list tools without making provider calls. Dynamic creation through the agent mod is off by default. Existing user authorization takes precedence; do not introduce another confirmation when the task already authorizes bounded execution.
  3. Respect the host's managedWorkflows, maxBudgetCents and allowInlineAgents settings. Do not disable policy to bypass a refusal. Default maximum is 2500 US cents. Inline agents are off by default, requiring predefined agents whose tools were reviewed separately. In flight work can exceed a provider budget. This is local creation admission, not a global organization spending ceiling or a policy for externally created sessions.
  4. Record task scope, source commit, output contract, deadline, budget, allowed network hosts and independent acceptance test before creation. Do not put keys or MCP tokens in prompts. Never give remote workers recursive Ruflo swarm launch, release or broad secret access.
  5. Create with dynamicWorkflows: true, budgetCents, exact allowedHosts, and appropriate predefinedAgents. Save resource IDs. Send one scoped task asking for inventory, parallel work, independent reproduction and synthesis. Workers share files; require separate worker outputs and serialize patches.
  6. Inspect open runs, pauses, failures and every worker thread with managed_agent_events. A run ending completed never proves correctness. Observation timeout does not stop remote work. Interrupt requests a pause; terminate confirms session deletion. Clean up only owned resources.
  7. Independently test artifacts against the original source and acceptance contract. Persist only verified evidence through the repository's memory/RVF integration, binding source identity, reproduction commands and real usage. Never store credentials or promote an unverified transcript as learned truth.

The console's Mods page shows the agent mod's creation policy, cap, inline setting and counters. It does not establish cloud run state. This skill grants no additional tool, network, credential, memory or release authority.

Verified learning and console use

Use the console Workflows, Cost, Approvals and Learning pages to inspect managed runs. Snapshots are staleable local observations and never authorize actions. Use managed_agent_control for fresh policy checked inspect/interrupt/terminate; the host must separately allow the exact session. Never pass an approval flag.

Before a repeated task, ask managed_agent_learning for a recommendation in the exact task/policy/evaluator scope. It is advisory and cannot expand budget, worker permissions, network access or default models. After independent host verification, record the closed outcome and host Ed25519 signature. Unknown or unsigned observations stay quarantined. Never request the verifier private key. Use index/recall only for authenticated training evidence; preserve reported backend/embedding failures. Use evaluate on a frozen, disjoint heldout manifest with three or more trials before treating a strategy as eligible. Eligibility never grants promotion, deployment or configuration writes.

After the host verifier has tested and signed outcomes, use managed_agent_learning action=cycle with a closed cycle object: scope, at most 24 evidence records, optional index, maxP95Ms and evaluation (manifest, baselineId, candidateId). One atomic batch precedes bounded native indexing and advisory evaluation. Replays preserve attempt identity. Never place commands, keys, approval or promotion flags in a cycle. A failed later phase can leave the signed batch persisted; inspect status and retry the same evidence rather than inventing new attempts. This is an explicit feedback step, not automatic task execution.

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