dfrysinger

Use when questions.md is approved and the QRSPI pipeline needs objective codebase and web research — dispatches parallel specialist subagents per question, collates per-question findings into research/summary.md

Use when starting a new QRSPI pipeline run — captures user intent and constraints through interactive dialogue, then synthesizes a problem-framed goals.md

Use when starting any conversation — establishes the QRSPI pipeline for agentic software development, requiring structured progression through Goals, Questions, Research, Design, Phasing, Structure, Plan, Parallelize, Implement, Integrate, Test, with Replan firing between phases

Per-phase implementation orchestrator. In full pipeline mode, resolves symbolic bases from parallelization.md to concrete commits, creates worktrees and stage commits, runs baseline tests, dispatches implementer + reviewer subagents per task per the wave schedule, runs the fix loop, presents the batch gate, and routes to the next route step (typically Integrate). In quick-fix mode, dispatches the single task (or a fix-task batch from fixes/{type}-round-NN/) through the same per-task implementer + reviewer flow, presents the batch gate (with quick-fix-mode menu), and routes to Test.

Per-phase implementation orchestrator. In full pipeline mode, resolves symbolic bases from parallelization.md to concrete commits, creates worktrees and stage commits, runs baseline tests, dispatches implementer + reviewer subagents per task per the wave schedule, runs the fix loop, presents the batch gate, and routes to the next route step (typically Integrate). In quick-fix mode, dispatches the single task (or a fix-task batch from fixes/{type}-round-NN/) through the same per-task implementer + reviewer flow, presents the batch gate (with quick-fix-mode menu), and routes to Test.

Use when starting any conversation — establishes the QRSPI pipeline for agentic software development, requiring structured progression through Goals, Questions, Research, Design, Phasing, Structure, Plan, Parallelize, Implement, Integrate, Test, with Replan firing between phases

Use when research/summary.md is approved and the QRSPI pipeline needs an architecture — proposes approaches, surfaces key architectural decisions with rationale, and defines per-goal acceptance through interactive design discussion

Cross-cutting QRSPI implementer protocol — dispatch shape, mode payloads, status reporting, ID hygiene, and the BLOCKED escape hatch shared by every implementer subagent.

Autopilot brief for a long, unattended Copilot CLI run — draft a sharp `/autopilot` objective (the goal its continuation loop drives toward until the agent calls task_complete) paired with a standing charter re-fed on an `/every` reminder so the run doesn't drift on the how. Use when starting a long autopilot or `/goal` run against a plan doc, when writing or sharpening an autopilot objective, or when keeping an unattended run from drifting over many context compactions.

Fallback for restoring gh CLI authentication inside an agent shell on macOS, and for generating the ~/.ssh/codespaces.auto key gh expects. Use when `gh auth status` reports invalid token, the login keychain isn't visible to the agent process, gh returns a stale token after `gh auth refresh`, or `gh codespace ssh` fails with "Permission denied (publickey)". The preferred fix for the keychain visibility problem is restarting the tmux server under a GUI Terminal session (see remote-agent-stack's tmux-keychain-bootstrap LaunchAgent) — use this skill only when that isn't an option.

Class-level playbook for integrating an app against the GitHub REST + GraphQL APIs — query-complexity ("Resource limits exceeded") fan-out limits, OAuth-App vs GitHub-App scope behavior, read:enterprise / SAML-SSO null-node redaction, pagination correctness, REST rate limits, and turning raw API errors into actionable UX. Use when building or debugging anything that calls api.github.com / api.github.ghe.com (GraphQL or REST), enumerating enterprises/orgs/repos, wiring GitHub sign-in (OAuth App, GitHub App, device-code flow), or seeing GraphQL "Resource limits for this query exceeded", null enterprise/org nodes, or scope/SSO errors.

Compact the current conversation into a structured handoff document for another agent to pick up where this one left off. Use when the user says "hand off to <name>", "do a handoff", "summarize this session for another agent", or wants to pass work to a different Copilot CLI session. If a recipient is named (e.g., "hand off to juliett"), the skill auto-delivers via the mailbox skill after writing the doc.

Preventive playbook for keeping secrets (tokens, API keys, PATs, passwords, private keys, signed URLs, session cookies) out of the chat transcript AND out of git. Covers the "never ask, never echo, never paste" rule for the conversation itself (because chat is sent to the cloud and persisted), plus the pre-commit / pre-push / repo-config layers that stop credentials from reaching GitHub. Use when handling anything credential-shaped — reading a token, configuring a service, writing a script that authenticates, asking the user to authenticate, or about to commit/push code that touches auth.

Autonomous per-session skill reflection — reviews recent work and creates/patches skills WITHOUT asking, mirroring Hermes Agent's background_review.py. Use when invoked by the daily sweep schedule, dispatched as an end-of-task subagent, or when the user says "review for skills" / "learn from this session". Distinct from skill-curator (which consolidates the library on a 7-day cadence); skill-review is the creation/patching loop.

Develop and ship one change at a time through an E2E-first loop — write a durable design doc to the repo's docs/ and rubber-duck it, author deterministic CI guards that encode and enforce the design (drift-proofing future agents), build, then prove it works with a real end-to-end run before review. "Real" scales to the project — a token-burning live model/backend call for agent work, or exercising the running app/UI with screenshot inspection for app and web work. Once it works, run dual-review — handed the full design-and-guard review packet each round, alongside project context (AGENTS.md, README, roadmap) — as its own review-and-fix loop until both reviewers are clean, then one final E2E; only an E2E failure restarts the loop. Use when making essentially any non-trivial code change you intend to ship — the default development loop for app, web, service, agent, pipeline, and SDK work, not just high-risk runtime changes; only docs-only edits and trivial one-liners take the lighter path.

Turn a codebase's prose architecture invariants (constitution/AGENTS.md hard rules, design-spec acceptance criteria, capability matrices) into deterministic, CI-enforced conformance tests — "architecture fitness functions" — so agents can't quietly break global invariants while every local test stays green. Covers the named invariant register, structural (build-time) vs behavioral (runtime) checks, the deterministic-not-LLM enforcement rule, grounding tests in real runtime code, and verifying drift is genuine before flagging it. Use when an agent-written or fast-moving codebase is drifting from its intended architecture, when you want to formalize "the architecture we actually want" and stop regressions in CI, or when reviewing whether a refactor preserved a system's global contracts.

Guardrails for the job-hunt MCP server -- prevents common agent mistakes when tracking job applications. Use this skill whenever the user mentions job search, job applications, job postings, resume tracking, application status, interview scheduling, application pipeline, cover letters for applications, or wants to look up, add, or update anything related to their job hunt. Also trigger when you see mcp__job-hunt__* tools available or when the user asks about their application pipeline or hiring status.