pskoett/pskoett-ai-skills

Monitors context window health during large, long-running, multi-session, or explicitly context-sensitive work. Uses bounded external anchors, one cold-context review when self-recovery is uncertain, and a clean handoff on real drift. Do not auto-activate merely because a medium task has an intent frame and plan.

Control-plane workflow for coordinating multi-agent, multi-session project work from a single Codex, GitHub Copilot, or agent-app control session. Use this skill whenever the user asks to orchestrate agents, create or steer worker sessions, run a workflow-like effort, fan out audits/research/migrations, coordinate parallel implementation streams, monitor other project sessions, or compare this control-session pattern to Claude Code dynamic workflows. This skill is especially relevant when the current session can spawn persistent project sessions and those sessions can spawn their own subagents, creating a two-level orchestration hierarchy.

[Beta] Creates permanent eval cases from promoted learnings and runs regression checks against them. Turns failures into test cases that prevent silent regression. This is the outer loop's regress-test step. Use when a learning is promoted and has a clear pass/fail condition, or on cadence to verify promoted rules still hold.

[Beta] CI-only eval regression runner using gh-aw (GitHub Agentic Workflows). Runs all eval cases in .evals/ on a schedule or per-PR, reports pass/fail results, and can block merges on regressions. Also creates new eval cases from promoted patterns flagged by learning-aggregator-ci. Use when: you want automated regression testing of promoted rules in CI/headless pipelines. For interactive eval creation and runs, use eval-creator.

Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.

[Beta] Cross-session analysis of accumulated .learnings/ files. Reads all entries, groups by pattern_key, computes recurrence across sessions, and outputs ranked promotion candidates. This is the outer loop's inspect step — it turns raw learning data into actionable gap reports. Use on a regular cadence (weekly, before major tasks, or at session start for critical projects). Can be invoked manually or scheduled.

[Beta] CI-only learning aggregation workflow using gh-aw (GitHub Agentic Workflows). Scans .learnings/ files on a schedule, groups entries by pattern_key, identifies promotion-ready patterns, and posts a gap report as a PR or issue comment. Use when: you want automated cross-session pattern detection in CI/headless pipelines without interactive prompts. For interactive use, use learning-aggregator.

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.

[Beta] Session-start scan that surfaces relevant learnings, recent errors, and eval status before work begins. Bridges the outer loop back into the inner loop by making accumulated knowledge visible at task start. Activated via SessionStart hook or manually before major tasks.

Active runtime recovery for coding agents: when something breaks mid-task, diagnose the root cause, write a fix, VERIFY by re-running the broken thing, then file a HEAL- entry to .learnings/HEALS.md with proof. Use whenever a command, test, build, or lint fails or exits non-zero; on missing tooling, dependency/lockfile mismatch, wrong runtime version, venv or permission errors, port conflicts, dirty git state, or a missing .env; when the agent needs a helper or one-off script that doesn't exist yet; when an external API, tool, or MCP errors or rate-limits; or when a test flakes. Search HEALS.md by Pattern-Key first — most heals are recurrences, so increment Recurrence-Count instead of duplicating. Verify is mandatory: mark pending-verify honestly if sandboxed, abandoned if the fix can't be made to work. Pairs with self-improvement (which promotes recurring heals to durable memory) but owns the verify-before-persist discipline self-improvement doesn't.

CI-only self-healing workflow using gh-aw (GitHub Agentic Workflows) for active runtime recovery on pull requests and scheduled runs. When a CI check fails (test, build, lint, deploy, scan), this skill diagnoses the failure from CI logs, proposes a verified patch as a PR comment or follow-up commit, and commits a HEAL entry to .learnings/HEALS.md. Verify-before-persist discipline preserved: a HEAL is only verified if a re-run check passes in the same workflow; otherwise it ships as pending-verify for human follow-up. Recurrent heal patterns across PRs accumulate Recurrence-Count and append a Handoff block at ≥3 to flag promotion via self-improvement-ci. Use this skill when: you want headless heal-loop execution in CI/scheduled pipelines, you want recurring failure patterns captured automatically, or you want PRs that surface non-obvious environmental / tooling fixes without human triage. For interactive/local sessions, use self-healing instead.

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.

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.

Pipeline orchestrator that classifies incoming coding tasks and routes them through the correct combination of skills at the right depth. Implements two feedback loops: the inner loop (detect, verify, recover) runs within a session via plan-interview, intent-framed-agent, context-surfing, verify-gate, self-healing (active recovery on failure), simplify-and-harden, and self-improvement. The outer loop (inspect, encode, regress-test) runs across sessions via learning-aggregator, harness-updater, and eval-creator. pre-flight-check bridges the two by surfacing accumulated knowledge — past heals and learnings — at session start. Handles standard, orchestrated-batch, CI, and outer-loop pipeline variants. Does not replace individual skills; dispatches to them.

Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.