post-smoke-ci-plan-boundary-hardening
Harden a CI follow-up plan after an initial smoke/unblock issue lands. Use for post-smoke red workflows where later gates surface broad lint/type debt and the plan must stay bounded, evidence-backed, and review-honest.
Best use case
post-smoke-ci-plan-boundary-hardening is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Harden a CI follow-up plan after an initial smoke/unblock issue lands. Use for post-smoke red workflows where later gates surface broad lint/type debt and the plan must stay bounded, evidence-backed, and review-honest.
Teams using post-smoke-ci-plan-boundary-hardening should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/post-smoke-ci-plan-boundary-hardening/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How post-smoke-ci-plan-boundary-hardening Compares
| Feature / Agent | post-smoke-ci-plan-boundary-hardening | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Harden a CI follow-up plan after an initial smoke/unblock issue lands. Use for post-smoke red workflows where later gates surface broad lint/type debt and the plan must stay bounded, evidence-backed, and review-honest.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Post-smoke CI plan boundary hardening Use when: - a previous issue already unblocked workflow startup/checkout/smoke - the workflow is still red at later gates (lint, mypy, coverage, quality-gate) - the next issue is a bounded CI-hardening tranche, not a repo-wide cleanup ## Goal Draft a plan that removes the current first post-smoke blockers without pretending to solve all downstream CI debt. ## Required approach 1. Verify the live split from the validating CI run - Inspect the exact run/job graph, not just issue comments. - Record which OS/jobs fail at which step. - Separate: - current first blocker(s) - later-stage blockers that will surface after those are removed 2. Reproduce locally enough to classify scope - Run the exact or nearest local validator for the current failing gate(s). - For typing gates, quantify breadth (example: "286 errors across 39 files") before promising fixes. - If the failure surface is broad, do not draft a plan that quietly absorbs repo-wide debt. 3. Convert vague “make CI green” wording into an honest bounded tranche If the issue body is broader than what the evidence supports, the plan must explicitly define: - what gate is being fixed now - what red state is allowed to remain - what later-stage failures must be split or referenced Do not use vague success phrases like: - “meaningfully green” - “advances past blockers” without naming the exact next expected state. 4. Lock the workflow scope explicitly For workflow edits, name the exact maintained target list. Examples: - exact flake8 path set - exact mypy target files/modules - exact dependency-install step(s) that change Do not leave key questions open in the plan such as: - “Should lint target X or Y?” - “Should we install stubs or suppress?” These must be resolved before plan-review if they affect correctness. 5. Create explicit follow-up issues for excluded surfaces If you narrow CI scope away from broken but real files, create tracking issues immediately. Typical excluded surfaces: - auxiliary `.agent-os/` or tooling scripts - duplicate non-package module copies - broad repo-wide type debt already tracked elsewhere In the plan, cite the concrete follow-up issue numbers, not placeholders or intentions. 6. Make TDD executable for workflow changes For workflow-scope changes, add a real test artifact path. Good pattern: - create a workflow-scope regression test file under tests/ - parse/assert the exact workflow targets - require a red phase before editing the workflow Also ensure test commands match repo policy exactly: - use `uv run` consistently if required by repo contract - preserve `python -m pytest` / `--noconftest` conventions when the repo defines them 7. Keep review bookkeeping honest If a review artifact is empty, timed out, or invalid: - mark it INVALID explicitly - do not fabricate or summarize findings that do not exist - do not move to `status:plan-review` until enough valid review evidence exists per repo policy ## Planning checklist - Exact validating run ID captured - First blocker per OS/job captured - Local breadth quantified for current failing gate - Exact workflow target list defined - Any exclusion has a follow-up issue number - TDD workflow-test file path exists in Files to Change - Static-analysis red phase is explicit, not implied - Review summary matches actual artifacts on disk - Success condition is bounded and falsifiable ## Common failure modes - promising green CI while only fixing the first blocker - narrowing lint/type scope without tracking excluded files - using vague close criteria instead of explicit workflow states - leaving critical implementation branches as “open questions” - claiming review convergence when artifact files are empty ## Good outcome A plan that says, in effect: - “This tranche fixes lint scope X and mypy target Y” - “These excluded surfaces are tracked in follow-up issues A/B” - “Broad residual debt remains under issue C” - “We will only move to plan-review after valid review artifacts support this exact bounded contract”
Related Skills
blender-worktree-test-hardening
Recover and harden digitalmodel Blender automation work in isolated worktrees when uv/editable dependency paths break and local machines lack a Blender executable.
plan-review-prompt-refresh-after-plan-edits
Refresh reviewer prompt files from the latest on-disk plan before every adversarial re-review. Prevents Codex/Gemini from critiquing stale plan text after local edits.
workspace-hub-overnight-plan-monitor
Monitor and reconcile workspace-hub overnight planning or implementation batches, including process status, result artifacts, issue/commit verification, and controlled failed-lane recovery.
plan-gated-issue-execution-wave
Execute a multi-issue architecture/planning wave in a plan-gated repo, then safely transition approved issues into implementation with file-based Codex prompts, local approval markers, subprocess monitoring, and cleanup handling for sandbox/hook edge cases.
mixed-ops-vs-repo-fix-plan-boundary
Plan mixed operational-vs-repo remediation issues by proving live-state classification first, then only proposing code changes for confirmed repo-owned failure paths.
wave-based-parallel-plan-execution
Orchestrate phase execution by discovering dependencies, grouping into waves, spawning subagents, and collecting results with optional wave filtering
plan-gated-issue-validation-workflow
Systematic validation pattern for plan-approved GitHub issues with pre-existing deliverables
plan-gated-issue-implementation
Workflow for executing pre-approved GitHub issues with mandatory validation checkpoints
boundary-policy-classification-by-role
Classify artifacts as durable vs transient by their functional role rather than directory path, using multi-layer architectural validation
external-drive-ingest-planning
Plan safe external-drive ingests into repo-aligned storage such as /mnt/ace: read-only mounts, manifests, staged rsync, dedupe-merge gates, GitHub issue traceability, and governance/execution split.
user-approved-plan-state-sync
Reconcile GitHub and local repo state when a plan has been user-approved, including direct approval messages that require creating the local marker and moving the issue to status:plan-approved.
ten-agent-pre-plan-review-wave
Launch and verify a 10-agent planning-only wave that moves open GitHub issues into status:plan-review using one isolated worktree per issue, wave-specific continuation cron, and post-run artifact-reconciliation checks.