agentleFS
wakqasahmedwakqasahmed/ai-engineering-workflow-skills/skills/engineering/workflow-router/SKILL.md

workflow-router

Skill2 starsChanged 2 months agoMITView on GitHub

Routes a software-work request to the smallest applicable delivery workflow and records repository conventions once. Use when a user asks what engineering workflow to follow, wants help starting a change, needs a release or blocker path, or says setup workflow.

10 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 9 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this as the entry point.

    line 9
  2. Very specific:

    Use subagent-pipeline only for a claimed non-trivial GitHub issue.

    line 55
  3. Very specific:

    When invoked as workflow-router setup, inspect before proposing anything:

    line 59

“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Workflow Router

Use this as the entry point; downstream skills own their detailed instructions.

Route

Classify the request, choose only the matching sequence below, and state each transition rationale in one line. Do not add steps from another route unless its trigger is present. In an unfamiliar repository, perform repository reconnaissance first (inspect structure, README/CONTRIBUTING, build/test framework, CI, recent git log, and existing patterns per AI_ENGINEERING_WORKFLOW.md).

Idea to staging

Vague but issue-sized request

  • Vague or decision-heavy request that clarification resolves into one independently verifiable issue: clarify-work → define-done.
  • Transition rationale: clarify creates a safe boundary; define-done makes success testable; a single bounded issue needs no decomposition or durable spec.

Broad or multi-issue request

  • Broad request that will not fit one independently verifiable issue even after clarification (several independent slices, unsettled cross-cutting decisions, a durable spec the team will reference across issues): clarify-work → to-prd → decompose-to-issues.
  • Transition rationale: clarify creates a safe boundary; to-prd drafts and publishes the durable spec the multi-issue breakdown depends on; decomposition protects independent delivery.

Concrete single behavior change

  • Concrete single behavior change: define done → test-driven-development test-first change → code-simplification on the changed code → review-gate → staging PR.
  • Transition rationale: the request is already bounded, so clarification and decomposition add no value; tests prove behavior before review.

Claimed non-trivial GitHub issue

  • Sequence: subagent-pipeline → CI → staging PR.
  • Transition rationale: a claimed non-trivial GitHub issue has an issue-sized boundary, so the pipeline supplies independent implementation, review, and CI evidence.

Bug or regression

  • Sequence: diagnose → regression test → smallest fix → review-gate.
  • Transition rationale: reproduce and isolate before changing code; retain the failing case as proof; review checks regressions after the fix.

Release

  • Sequence: release-gate.
  • Transition rationale: release needs a reviewed artifact, target, health signal, and rollback path rather than a new implementation workflow.

Human-held blocker

  • Sequence: hitl-blocker.
  • Transition rationale: missing credentials, DNS, billing, permissions, or approval require a visible owner and verification command, not repeated automation.

Standalone tools

  • clarify-work for unclear scope; define-done for acceptance criteria on a single bounded issue; to-prd for a durable spec a multi-issue plan will reference; decompose-to-issues for a multi-issue plan; review-gate before merge; handover at a context boundary.
  • Use subagent-pipeline only for a claimed non-trivial GitHub issue. Its CI gate ends with the appropriate staging PR.

Run-once setup

When invoked as workflow-router setup, inspect before proposing anything:

  1. Tracker: GitHub remotes, issue/PR templates, and local tracker files.
  2. Delivery: target branch from remote/default-branch configuration and existing CI workflows.
  3. Labels: existing issue labels and any documented label vocabulary.
  4. Project context: docs/ layout, ADRs, and existing AGENTS.md, CLAUDE.md, or equivalent instructions.
  5. Verification: test commands, runtime/container instructions, and test-storage constraints.

Report detected conventions and gaps, then show a file-by-file proposed edit or command. Ask for approval before writing any instruction, config, label, or tracker change. Never overwrite or replace existing instructions; add only an approved, conflict-free change, or report the conflict for a human decision.

Setup output

Return: detected tracker; target branch; labels; docs/instruction files; test/runtime command; proposed changes; and Approval required: yes/no. yes is mandatory whenever a change is proposed.

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  • It worked
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  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.