
Self Improvement Ci
- 13 installs
- 272 repo stars
- Updated June 12, 2026
- pskoett/pskoett-skills
Helps with ai & agent building tasks.
About
self-improvement-ci is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- self-improvement-ci
- AI & Agent Building
- AI-coding skill
Self Improvement Ci by the numbers
- 13 all-time installs (skills.sh)
- +1 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #11,389 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 13 |
|---|---|
| repo stars | ★ 272 |
| Last updated | June 12, 2026 |
| Repository | pskoett/pskoett-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
Self-Improvement CI
Install
gh skill install pskoett/pskoett-skills self-improvement-ciFallback using the Agent Skills CLI:
npx skills add pskoett/pskoett-skills/skills/self-improvement-ciPurpose
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
1. GitHub Actions enabled for the repository 2. GitHub CLI authenticated (gh auth status) 3. gh-aw installed for authoring/validation:
gh extension install github/gh-awCI Contract
The CI skill must:
1. Read only PR-scoped data (checks, workflow outcomes, existing learning entries) 2. Avoid direct code modifications in CI 3. Emit machine-readable learning output 4. 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: trueRecurrence 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: 1. Copy the template into .github/workflows/self-improvement-ci.md 2. Customize tool access, outputs, and policy thresholds 3. Validate:
gh aw compile --validate --strict4. Trigger test run manually:
gh aw run self-improvement-ci --pushHeal Handoff Intake
self-healing-ci appends Handoff blocks to .learnings/HEALS.md entries that meet the promotion rule. On each run:
1. Read .learnings/HEALS.md (read-only) and collect entries with a Handoff block 2. Map each to a candidate: pattern_key from the HEAL's Pattern-Key, suggested_rule from the Distilled Rule, recurrence fields from the entry metadata 3. Mark promotion_ready: true when the promotion rule holds, and include the candidate in the output schema alongside simplify-and-harden-ci candidates 4. 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 ingest
simplify_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
Workflow Example (Non-Active)
This is an example template only. Keep it outside .github/workflows so nothing runs automatically.
When you are ready to enable CI automation: 1. Copy this template into .github/workflows/self-improvement-ci.md 2. Adjust thresholds and promotion policy 3. Validate with gh aw compile --validate --strict
---
on:
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
workflow_dispatch:
permissions:
contents: read
actions: read
pull-requests: read
tools:
github:
toolsets: [pull_requests, actions]
safe-outputs:
add-comment:
max: 1
strict: true
---
Run Self-Improvement CI for this pull request.
Rules:
1) Collect failure and warning signals from relevant checks.
2) Ingest `simplify_and_harden.learning_loop.candidates` when available.
3) Deduplicate findings by `pattern_key`.
4) Emit structured YAML under key `self_improvement_ci` with recurrence metadata.
5) Recommend promotion when recurrence thresholds are met.
6) Do not modify repository files in CI.