version: "1.0.2" name: simplify-and-harden-ci description: "CI-only Simplify & Harden workflow for pull requests using gh-aw (GitHub Agentic Workflows). Runs headless scan-and-report checks for simplify/harden/document, posts structured findings, and can block merges on critical or advisory classes. Use when: you want automated quality/security review in CI without interactive approvals."

Simplify & Harden CI

Install

gh skill install pskoett/pskoett-skills simplify-and-harden-ci

Fallback using the Agent Skills CLI:

npx skills add pskoett/pskoett-skills/skills/simplify-and-harden-ci

Purpose

Run a CI-only variant of Simplify & Harden in pull requests:

• No code mutation in CI
• Review only changed files
• Emit structured findings
• Optionally block merge based on severity gates

Use simplify-and-harden for interactive/local coding sessions.

Context Limitation (Important)

CI agents do not have the same peak implementation context as the coding agent that wrote the change. Treat CI findings as structured review signals, not as full intent-aware rewrites.

Implications:

• Prefer scan/report and merge gating
• Do not auto-apply code changes in CI
• Escalate ambiguous findings to interactive review

Prerequisites

1. GitHub Actions enabled for the repository
2. GitHub CLI authenticated (gh auth status)
3. gh-aw installed locally for authoring/validation:

gh extension install github/gh-aw

1. In GitHub Actions jobs, install the CLI with:

- uses: github/gh-aw/actions/setup-cli@main
  with:
    version: v0.2.0-beta

CI Contract

The CI skill must enforce:

1. Scope lock: review only files changed in the PR
2. Headless execution: report findings, do not apply patches/refactors
3. Structured output: emit simplify_and_harden summary payload
4. Gate policy:

• critical: fail check when critical harden findings exist
• advisory (optional): fail check when advisory findings are configured to block

Authoring Workflow (gh-aw)

Example-only template lives in references/workflow-example.md. Keep it outside .github/workflows until you explicitly want automation enabled.

When ready to enable:

1. Copy references/workflow-example.md template block into .github/workflows/simplify-and-harden-ci.md. (The .md extension is intentional: gh-aw workflows are markdown files that gh aw compile compiles into the .lock.yml files GitHub Actions actually runs. This is not a standard hand-written Actions YAML workflow.)
2. Compile and validate workflow:

gh aw compile --validate --strict

1. Trigger and push workflow changes:

gh aw run simplify-and-harden-ci --push

1. Check status/logs in GitHub Actions and ensure PR feedback is posted.

Prompt Template (CI)

Use this prompt body in your gh-aw workflow:

Run Simplify & Harden in CI (headless mode) for this pull request.
 
Rules:
1) Review only files changed in this PR.
2) Do not modify repository files.
3) Before reporting findings, re-read all changed code with "fresh eyes" and actively look for obvious bugs, errors, confusing logic, brittle assumptions, naming issues, and missed hardening opportunities.
4) Simplify pass: detect dead code, naming clarity issues, control-flow complexity, unnecessary API surface, and over-abstraction.
5) Harden pass: detect input-validation gaps, injection vectors, auth/authz issues, secret exposure, data leaks, and concurrency risks.
6) Document pass: suggest non-obvious rationale comments as findings (do not edit files).
7) Emit structured YAML under key `simplify_and_harden`, including:
   - simplify findings
   - harden findings (critical/advisory split)
   - summary counts
   - `review_followup_required`
   - learning loop candidates for self-improvement ingestion
8) If blocking policy is enabled and matching findings exist, mark the run as failed.

Recommended Outputs

1. PR comment with concise findings and severity ordering
2. Check run summary with pass/fail reason
3. Machine-readable YAML artifact for downstream automation

Integration with Self-Improvement

Forward simplify_and_harden.learning_loop.candidates into .learnings/LEARNINGS.md via the self-improvement workflow so recurrent patterns can be promoted into durable agent context rules.