
self-improvement-ci
CI-only self-improvement workflow using gh-aw (GitHub Agentic Workflows). Captures recurring failure patterns and quality signals from pull request checks, emits structured learning candidates, and proposes durable prevention rules without interactive prompts. Use when: you want automated learning capture in CI/headless pipelines.
- Installs
- 545
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/pskoett/pskoett-ai-skills --skill self-improvement-ci- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads self-improvement-ci automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Self-Improvement CI
Install
gh skill install pskoett/pskoett-skills self-improvement-ci
Fallback using the Agent Skills CLI:
npx skills add pskoett/pskoett-skills/skills/self-improvement-ci
Purpose
Run self-improvement in CI without interactive chat loops:
- Inspect PR check results and CI failures
- Ingest learning candidates from
simplify-and-harden-ci - Ingest
Handoffblocks from.learnings/HEALS.md(filed byself-healing/self-healing-ci) and surface them as promotion candidates - Deduplicate recurring patterns by stable
pattern_key - Emit promotion-ready suggestions for agent context/system prompts
This skill is read-only with respect to the repository (see CI Contract): it does not write .learnings/ entries. Its candidates are emitted as machine-readable output, and promotions are proposed as a PR or comment for human review.
Use self-improvement for interactive/local sessions.
Context Limitation (Important)
CI agents do not have peak task context from the original implementation session. Use this skill to aggregate recurring patterns across runs, not to infer nuanced one-off intent.
Implications:
- Favor stable
pattern_keyrecurrence signals over single-run conclusions - Require recurrence thresholds before promotion
- Route uncertain or high-impact recommendations to interactive review
Prerequisites
- GitHub Actions enabled for the repository
- GitHub CLI authenticated (
gh auth status) gh-awinstalled for authoring/validation:
gh extension install github/gh-aw
CI Contract
The CI skill must:
- Read only PR-scoped data (checks, workflow outcomes, existing learning entries)
- Avoid direct code modifications in CI
- Emit machine-readable learning output
- Recommend promotion only when recurrence thresholds are met
Output Schema
self_improvement_ci:
source:
pr_number: 123
commit_sha: "abc123"
candidates:
- pattern_key: "harden.input_validation"
source: "simplify-and-harden-ci"
recurrence_count: 3
first_seen: "2026-02-01"
last_seen: "2026-02-20"
severity: "high"
suggested_rule: "Validate and bound-check external inputs before use."
promotion_ready: true
summary:
candidates_total: 4
promotion_ready_total: 1
followup_required: true
Recurrence and Promotion Rules
- Track recurrence by
pattern_key - Default threshold for promotion:
recurrence_count >= 3- seen in
>= 2distinct tasks/runs - within a 30-day window
- Promotion targets:
CLAUDE.mdAGENTS.md.github/copilot-instructions.mdSOUL.md/TOOLS.mdwhen using openclaw workspace memory
Authoring Workflow (gh-aw)
Example-only templates live in references/workflow-example.md.
Keep examples outside .github/workflows until you explicitly decide to enable CI automation.
When ready:
- Copy the template into
.github/workflows/self-improvement-ci.md - Customize tool access, outputs, and policy thresholds
- Validate:
gh aw compile --validate --strict
- Trigger test run manually:
gh aw run self-improvement-ci --push
Heal Handoff Intake
self-healing-ci appends Handoff blocks to .learnings/HEALS.md entries that meet the promotion rule. On each run:
- Read
.learnings/HEALS.md(read-only) and collect entries with aHandoffblock - Map each to a candidate:
pattern_keyfrom the HEAL'sPattern-Key,suggested_rulefrom theDistilled Rule, recurrence fields from the entry metadata - Mark
promotion_ready: truewhen the promotion rule holds, and include the candidate in the output schema alongsidesimplify-and-harden-cicandidates - Propose the promotion (target file + rule text) as a PR or comment — never write instruction files directly from CI
Integration with Other Skills
- Pair with
simplify-and-harden-cito ingestsimplify_and_harden.learning_loop.candidates - Pair with
self-healing-ci, whose HEALS.mdHandoffblocks this skill consumes (see Heal Handoff Intake) - Feed promoted patterns back into
self-improvementmemory workflow for durable prevention rules
FAQ
What does the self-improvement-ci skill do?
CI-only self-improvement workflow using gh-aw (GitHub Agentic Workflows). Captures recurring failure patterns and quality signals from pull request checks, emits structured learning candidates, and proposes durable prevention rules without interactive prompts. Use when: you want automated learning capture in CI/headless pipelines.
How do I install self-improvement-ci?
Run npx skills add https://github.com/pskoett/pskoett-ai-skills --skill self-improvement-ci in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/self-improvement-ci/ (personal) or .claude/skills/self-improvement-ci/ (project).
Which AI agents can use self-improvement-ci?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is self-improvement-ci safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains self-improvement-ci?
It is published by pskoett in pskoett/pskoett-ai-skills. It has 545 recorded installs.
More from pskoett/pskoett-ai-skills
self-improvement
pskoett/pskoett-ai-skills
Captures learnings, errors, corrections, and feature requests to enable continuous improvement. Use when: (1) User corrects Claude ('No, that's wrong...', 'Actually...'), (2) User requests a capability that doesn't exist, (3) Claude realizes its knowledge is outdated or incorrect, (4) A better approach is discovered for a recurring task, (5) Receiving a Handoff block from self-healing (a recurring verified heal with Recurrence-Count at least 3) to distill into a memory file or new skill. For ACTIVE runtime failures where the agent needs to apply and verify a fix mid-task, use self-healing instead (it files HEAL- entries with proof; self-improvement promotes accumulated patterns). Also review learnings before major tasks. For CI-only/headless learning capture, use self-improvement-ci.
simplify-and-harden
pskoett/pskoett-ai-skills
Post-completion self-review for coding agents that runs simplify, harden, re-verification, and micro-documentation passes on non-trivial code or high-impact configuration changes. Use when a task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.
agent-teams-simplify-and-harden
pskoett/pskoett-ai-skills
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or the loop cap is reached. Use when: implementing features from a spec or plan, hardening existing code, fixing a batch of issues, or any multi-file task that benefits from a build-verify-fix cycle.
plan-interview
pskoett/pskoett-ai-skills
Ensures alignment between user and Claude during feature/spec planning through a structured interview process. Use this skill when the user invokes /plan-interview before implementing a new feature, refactoring, or any non-trivial implementation task. The skill runs an upfront interview to gather requirements across technical constraints, scope boundaries, risk tolerance, and success criteria before any codebase exploration. Do NOT use this skill for: pure research/exploration tasks, simple bug fixes, or when the user just wants standard planning without the interview process.
intent-framed-agent
pskoett/pskoett-ai-skills
Open the skill to read its SKILL.md instructions.
simplify-and-harden-ci
pskoett/pskoett-ai-skills
Open the skill to read its SKILL.md instructions.
Popular devops & cloud skills
microsoft-foundry
microsoft/azure-skills
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure
azure-diagnostics
microsoft/azure-skills
Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.
azure-prepare
microsoft/azure-skills
Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.
azure-ai
microsoft/azure-skills
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
azure-deploy
microsoft/azure-skills
Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.
azure-validate
microsoft/azure-skills
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.