DinoStack

Apply when the user mentions any software development work: implementing features, fixing bugs, reviewing or refactoring code, debugging, testing, deploying, working with agents or subagents, making architecture decisions, setting up projects, managing dependencies, writing scripts, or any task that involves reading, writing, or reasoning about code and systems.

Agentic Engineering Protocol for Hermes - structured delegation model, risk classification, adversarial review loops, code quality gates, git workflow conventions, and named agent definitions. Load this skill when doing AI-assisted software development with Hermes Agent.

Apply when the user mentions any software development work: implementing features, fixing bugs, reviewing or refactoring code, debugging, testing, deploying, working with agents or subagents, making architecture decisions, setting up projects, managing dependencies, writing scripts, or any task that involves reading, writing, or reasoning about code and systems.

Pre-implementation technical design agent. Spawn when you need a structured technical plan before writing code. Reads the codebase, identifies patterns and constraints, evaluates approaches, and produces a concrete plan a Worker can execute directly. Never writes or modifies files.

General-purpose implementation agent. Spawn for any code change: new features, bug fixes, refactors, configuration changes, or script writing. Reads the codebase to understand conventions, implements the change, runs quality gates, and returns a clear summary of what was done. This is the standard Worker for all Elevated-risk implementation tasks.

Cheap per-turn stop-condition check for open-goal loops. Spawned by the conductor ONLY after an Elevated iteration produces a clean Skeptic sign-off, to evaluate the operator-declared goal_condition and return continue-vs-stop only - never for a Low/Trivial iteration (no Skeptic sign-off exists to run after; the conductor evaluates goal_condition directly there instead). Tier 1 (haiku) leaf agent - read-only, no subagent spawning, never runs in place of, before, or concurrently with a Skeptic review. Does NOT review correctness or safety and does NOT raise, waive, or comment on Skeptic findings. Returns BLOCKED only as a structural guard when spawned without a confirmed Skeptic sign-off; the conductor handles this BLOCKED as a fallback to direct evaluation, NOT as the generic Worker-BLOCKED-means-cap_reached-escalation semantics in content/references/subagent-protocol.md - a BLOCKED return here never halts the loop. On any other failure (unavailable, errored, timeout, malformed output) the conductor falls bac

Per-ticket learning extraction agent. Spawned by /ds-implement-ticket Phase 6 clean exit. Reads the resolved findings_log and extracts durable fix-pattern LRN (bug-fix) learnings to .agentic/learnings.md. Emits LRN entries ONLY - KNW (knowledge) capture is learnings-agent's responsibility via mandatory triggers. Tier 1 leaf agent, 30s timeout, soft-fail. Does not touch MEMORY.md, decisions.md, AGENTS.md, or any source/config files.

Session-scoped background learnings capture. Spawned by the conductor when the first mandatory capture trigger fires in a session. Receives learning events as messages, writes structured LRN (bug-fix) or KNW (knowledge) entries to .agentic/learnings.md and optionally to MEMORY.md. Uses dedup, caps, and soft-fail discipline. Does not touch decisions.md, AGENTS.md, findings.md, qa.md, tasks.jsonl, loop-state.json, batch-state.json, context.md, or any source/config files.

Facilitated product discovery before any architecture or implementation work. Spawn when someone arrives with a product or feature idea that is not yet scoped - "I want to build...", "we should add...", "thinking about a tool that...", "here's an idea for..." - or when a project has no vision/requirements docs yet and work is about to start. Also spawn when the user asks to scope a feature, write a PRD, frame a problem, identify target users, run a competitive scan, or draft a product brief or PRFAQ. Decides WHAT to build and WHY, then stages a proposed vision.md and requirements.md for the operator to confirm. Stages proposals to docs/overview/_proposed/ only; never writes the canonical docs/overview/ files. Prefer this over jumping straight to design or code when the underlying problem, users, or scope are still fuzzy.

Dynamic verification agent for runtime testing. Spawn after Skeptic review, before merge, for any change with visible UI or behavioral output. Also invoked when the user says "run QA", "verify in the browser", "check the feature works", "test the acceptance criteria", or "does it work". Verifies changes work in a real browser, runs test suites, validates against acceptance criteria and design specs. Supports scenario methods: browser, api, runtime-required, visual_conformance, accessibility (WCAG via axe-core), perceptual_diff (pixel regression via pixelmatch), and motion (prefers-reduced-motion via Playwright CDP). Iterates all applicable scenarios across each declared viewport. Returns a structured pass/fail report with evidence. Does not fix issues. Appends learned project-specific quirks to .agentic/qa.md for future runs.

Adversarial code reviewer. Spawn when conducting Skeptic Protocol review of Worker output. Evaluates implementation against an adversarial brief, classifies findings as Critical/Major/Minor, and produces a structured sign-off. The spawn prompt must contain four things: (1) the adversarial brief defining the attack surface to probe, (2) Worker output as inline text or file paths, (3) a resolved-issues preflight listing findings addressed in prior rounds, and (4) a Global-context input set (a "## Global-context inputs" block containing the architect plan path, Brief/Plan artifact path, qa_criteria block, per-consumer impact table, related files list, and diff under review). See content/references/skeptic-protocol.md Section 4.5 for the canonical block format.

Per-ticket learnings capture invoked at /ds-implement-ticket Phase 11b. Constrained subset of /ds-wrap that fires automatically on every PR opened. Reads the ticket's findings_log, qa.md diff, merged diff, and conversation summary; appends durable learnings to MEMORY.md, decisions.md, and .agentic/context.md (## Recent Focus only). Does not touch AGENTS.md, qa.md, findings.md, tasks.jsonl, loop-state.json, batch-state.json, or any source/config files. Soft-fails on any error - never blocks Phase 12 or PR completion.

Apply when the user mentions any software development work: implementing features, fixing bugs, reviewing or refactoring code, debugging, testing, deploying, working with agents or subagents, making architecture decisions, setting up projects, managing dependencies, writing scripts, or any task that involves reading, writing, or reasoning about code and systems.

Purpose: Interactive planning dialogue that produces a Brief artifact before architect and engineer

Clean up stale git worktrees and local branches in the current repository. Covers both worktree removal and local branch prune - see `content/references/worktree-lifecycle.md` §Branch prune for the ca

Interactive command to view and change agentic-engineering settings in-session.

Set up and verify a cross-harness agent team so any conductor (Claude, Codex, Gemini, Kimi, or other) can dispatch work across multiple AI harnesses with explicit role assignments.

Render token and wall-time rollups from `.agentic/events.jsonl`. Optionally

Append the agentic-engineering opt-out marker to the project

Standalone, operator-run batch triage of the home-dir feedback store

Static, zero-token command reference for the agentic-engineering skill.

Manage the developer identity used for session telemetry attribution.

Take a ticket (Linear, Jira, or none) from description to merged PR, with full agent orchestration (Architect → Orchestration Planner (conditional) → Engineer → Skeptic) and the CI Test URL posted bac

Scaffold a new project with the standard AGENTS.md hierarchy, CLI tool config, and gitignore.

When a project-affecting decision has been confirmed in conversation, the main agent invokes this command with the decision context passed as `$ARGUMENTS`.

Conductor-facing command to inspect and apply project scaffolding migrations from the canonical manifest (content/project-scaffolding.yml).

Performs a periodic analysis pass over methodology files to surface deletion candidates - rules whose motivating assumptions have expired as Claude has become more capable.

Pull the latest agentic-engineering (DinoStack) release and reinstall selected adapters, or perform a fresh clone-and-install if no existing install is detected. Use this when you want to update an ex

Performs a periodic prose quality pass over methodology files to surface Python-shaped writing and propose cleaner natural-language rewrites.

Orchestrate adversarial review for `$ARGUMENTS`. The main agent drives the loop: spawn Worker, spawn Skeptic, route findings, repeat until sign-off.

Read-only view of the skill-candidate backlog. Displays open and dismissed

Read-only inspection of the agentic-engineering activation resolver.

Map a project's test suite against its source files and surface where verification gaps live. Returns a coverage summary, a gap report ranked by risk, and the specific test files that are highest-leve

Purpose: Reconciles a ticket's tracker column with the actual state of its code. Fires the Done

Purpose: Strategic triage command that takes a ticket list or tracker input,

Handles the full edit-sync-build-commit-push cycle for methodology and tooling files under your agentic-engineering install (resolved at runtime from `~/.agentic/agentic-engineering-config.json` `repo

This command is invoked by the deferred-wrap daemon (`hooks/wrap-daemon.js`), not directly by users. The daemon resumes a cleanly-ended session headlessly (`claude --resume <id> -p "/ds-wrap-deferred"

Use when you want a richer context file than the auto-hook provides — e.g. before handing off complex in-progress work to a future session.

Apply when the user mentions any software development work: implementing features, fixing bugs, reviewing or refactoring code, debugging, testing, deploying, working with agents or subagents, making architecture decisions, setting up projects, managing dependencies, writing scripts, or any task that involves reading, writing, or reasoning about code and systems.

Structured multi-agent software engineering workflow for planning, delegation, review, risk classification, quality gates, and shipping code. Use for software development tasks, implementation planning, code review, debugging, refactoring, and release work.