agentleFS
orbyn-devorbyn-dev/orbyn-fullstack-skills/skills/rea-tool-design/SKILL.md

rea-tool-design

Skill4 starsChanged 10 days agoMITView on GitHub

Design REA tools or CLI/MCP contracts when modifying the REA project; not ordinary Orbyn API work.

8 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 7 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this local method for REA tooling itself.

    line 8
  2. Distinctive:

    No runtime installation, source-pin change, publication or model configuration follows from this skill.

    line 14
  3. Distinctive:

    Do not combine unrelated inspect, mutate and runtime modes just to reduce tool count.

    line 30
  4. Distinctive:

    Use inspection for object facts, discovery for candidates, tracing for supported relationships and comparison for explicit paired …

    line 32
  5. Distinctive:

    Keep the requested answer and provenance usable inline, with supported summaries/paging for large facts and access to full evidence.

    line 43
  6. Distinctive:

    When local CLI observations feed a deeper operation, accept them only as the contract's supported context.

    line 49
  7. Distinctive:

    Keep provider interpretation in its owning layer.

    line 69

“Common” is measured: kept by at least 6 in 10 of the 7 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

Design a REA tool contract

Use this local method for REA tooling itself. Ordinary APIs belong to backend design; unsettled ownership belongs to architect. Read the target checkout's instructions and nearest existing contracts from disk. Source, docs and a connected catalog can represent different revisions; record which supports each decision.

No runtime installation, source-pin change, publication or model configuration follows from this skill; follow the user's scope and existing project policy.

For provider-backed work, run the analyst's task through REA and compare it with the native provider and independent artifact observations where available. Check partial results, recovery and session boundaries, and report what was not exercised. In an audit, keep reproduced bugs, supported limits, design opportunities and unverified claims apart. Check rendered prompts, guided workflows and packaged skill advice against the live contracts, and try the handoffs they suggest.

Choose the boundary

State the analyst question, target identity, expected facts, allowed effects and nearest existing operation. Extend an operation when intent and semantics match; add one for a distinct outcome or authority boundary. Prefer provider-neutral public concepts while reporting provider-specific availability and limitations. Do not combine unrelated inspect, mutate and runtime modes just to reduce tool count.

Use inspection for object facts, discovery for candidates, tracing for supported relationships and comparison for explicit paired observations. Compose a workflow only when repeated use demonstrates a join that can preserve choices, evidence and unknowns. Add a new cache, orchestration language or mandatory multi-call plan only for an actual requirement existing primitives cannot satisfy.

Specify the contract before implementation

Define strict inputs, target/admission identity, meaningful options, output fields, coverage and errors. Keep observed, derived and inferred relationships distinct.

Keep the requested answer and provenance usable inline, with supported summaries/paging for large facts and access to full evidence. Report unknown coverage as unknown, not as an empty complete result; complete static direct edges do not establish all runtime edges. Use the shared evidence contract for summary-versus-dossier composition and integrity failures.

When local CLI observations feed a deeper operation, accept them only as the contract's supported context. Bind target digest/architecture and selection at admission, and produce real engine evidence; arbitrary local text is not an engine-issued dossier. Keep local and engine facts distinguishable in exports.

Declare effects and resource owners

Declare process, filesystem, network, UI and mutation effects. Identify resource owners and transitions: admission, readiness, active work, cancellation, observed exit and verified release.

  • A launch handle is not readiness; process exit does not prove output closure.
  • Attach ownership does not imply permission to kill the target.
  • Retain partial results and the primary diagnostic if cleanup fails; bind recovery to unresolved owners and block conflicting replacement work.
  • Shared producers need independently cancellable waits and an explicit final-owner cleanup.

Keep layers in place

Keep provider interpretation in its owning layer. Reuse the same application semantics and typed errors in CLI and MCP rather than reparsing raw reports. Review actual generated catalog/schema ownership in the target before edits; do not hand-edit generated files to hide drift or invent unavailable capabilities.

Deliver within scope

For design-only work, return the proposed input/result, effects, state/error model, closest alternative, compatibility migration and verification plan. For authorized implementation, migrate affected consumers and generated contracts together. Choose checks at real trust, integrity, cancellation and cleanup boundaries; keep mock-provider coverage and live-engine evidence separate.

See the worked example and source record.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • 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.