Automated learning capture in CI pipelines that deduplicates failure patterns and proposes prevention rules.
Works with
Inspects PR check results and CI failures to identify recurring patterns tracked by stable pattern_key , promoting only when recurrence thresholds are met (3+ occurrences across 2+ distinct runs within 30 days)
Ingests learning candidates from simplify-and-harden-ci and emits machine-readable YAML output without interactive prompts, suitable for headless GitHub Actions workflows
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionself-improvement-ciExecute the skills CLI command in your project's root directory to begin installation:
Fetches self-improvement-ci from pskoett/pskoett-ai-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate self-improvement-ci. Access via /self-improvement-ci in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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npx skills add pskoett/pskoett-ai-skills/skills/self-improvement-ci
Run self-improvement in CI without interactive chat loops:
simplify-and-harden-cipattern_keyUse self-improvement for interactive/local sessions.
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:
pattern_key recurrence signals over single-run conclusionsgh auth status)gh-aw installed for authoring/validation:gh extension install github/gh-aw
The CI skill must:
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
pattern_keyrecurrence_count >= 3>= 2 distinct tasks/runsCLAUDE.mdAGENTS.md.github/copilot-instructions.mdSOUL.md / TOOLS.md when using openclaw workspace memoryExample-only templates live in references/workflow-example.md.
Keep examples outside .github/workflows until you explicitly decide to enable CI automation.
When ready:
.github/workflows/self-improvement-ci.mdgh aw compile --validate --strict
gh aw run self-improvement-ci --push
simplify-and-harden-ci to ingest
simplify_and_harden.learning_loop.candidatesself-improvement memory workflow for durable prevention rulesMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
shadcn/improve
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in self-improvement-ci — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for self-improvement-ci matched our evaluation — installs cleanly and behaves as described in the markdown.
self-improvement-ci reduced setup friction for our internal harness; good balance of opinion and flexibility.
self-improvement-ci has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for self-improvement-ci matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in self-improvement-ci — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend self-improvement-ci for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in self-improvement-ci — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
self-improvement-ci reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend self-improvement-ci for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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