agentleFS
orbyn-devorbyn-dev/orbyn-fullstack-skills/skills/rea-ci-tests-audit/SKILL.md

rea-ci-tests-audit

Skill4 starsChanged 11 days agoMITView on GitHub

Audit REA test and CI quality when specifically requested; does not run REA qualification for unrelated Orbyn changes.

6 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

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 4

    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 procedure for an explicit REA CI/test assessment.

    line 8
  2. Distinctive:

    Check positive and negative selection paths:

    line 26
  3. Distinctive:

    Prefer removing proven duplicate preparation to deleting distinct assertions.

    line 62
  4. Distinctive:

    If fixes are authorized, change the owning boundary and verify affected consumers and meaningful negative cases.

    line 69

“Common” is measured: kept by at least 6 in 10 of the 5 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

Audit REA checks and their evidence

Use this local procedure for an explicit REA CI/test assessment. Ordinary application CI belongs to CI design; a single product regression does not require a suite audit. Read the target's local contribution/testing rules.

An audit request does not itself authorize source mutation, a security scan, publication or engine installation. Execute checks only within the requested scope.

Establish what actually runs

Pin baseline/candidate revisions, workflow revision, toolchain, host prerequisites, command and selected tests. Preserve other work in its checkout. Credit a historical pass only to the revision it ran on. If offline history or runtime is missing, assess available source/logs and label the missing baseline or lane explicitly.

Trace workflow triggers and path selection through job dependencies, package scripts, test discovery, fixture generation, subprocesses, artifacts and final gates. Include runtime registration or generated data absent from import graphs.

Check positive and negative selection paths:

  • Confirm required cases are discovered once per intended configuration; count intentional platform repeats as separate coverage.
  • A missing explicit test or required report must not become a green zero-test lane.
  • Let an optional provider lane skip with its prerequisites and missing evidence named.

For a local/engine investigation handoff, check local inspection without an engine, missing-decoder fallback, preservation of hidden package entries, source/target digest agreement and rejection of forged engine records. A fixture provider tests the handoff contract; it is not a live REA engine run. A missing optional engine must not skip available local checks.

Establish a finding

For each suspicious pass/failure, name the expected observable behavior, producer, consumer and independent oracle. Determine whether a mock reaches the production boundary it claims to exercise. Preserve intentionally malformed fixture families and their intended rejection.

In an authorized disposable copy, a controlled failure/mutation can establish whether an assertion or gate detects the regression. Do not modify another session's checkout or weaken the oracle to get green.

Separate reproduced defects, supported costs, hypotheses and unavailable evidence. For a failing candidate, compare a clean baseline with the same command and conditions before attributing it. A passing retry does not establish a fix; retain the original failure and retry only to test a specific transient explanation. Distinguish fake-provider, boundary and real-engine evidence. Use evidence and recovery for partial observations, timeouts and cleanup.

Assess cost

Measure equivalent completed coverage before recommending optimization. Separate setup/build, execution, instrumentation, dependency waits and runner scheduling. Prefer removing proven duplicate preparation to deleting distinct assertions. Preserve malformed input, cancellation, cleanup, identity, permission, capacity and meaningful concurrency cases where relevant. Cached historical provider success does not qualify a new runtime, permissions or engine contents.

Fix and deliver

If fixes are authorized, change the owning boundary and verify affected consumers and meaningful negative cases. Do not suppress failures, blanket-retry, raise all timeouts or prune checks solely to make CI green. Run a full suite for broad risk or unresolved discovery, not for every documentation correction.

Return revision, scope, reproduction, observed/expected result, consequence, recommended correction and check limitations for each finding. Distinguish local runs from hosted current-head CI and main verification. Stop at the authorized assessment/fix; publishing issues, merging or running native providers needs its own scope. 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.