How do I install the Aicrew Dev skill?

Run npx skillmds@latest add aksoftcode/aicrew-dev in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Aicrew Dev safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Aicrew Dev?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Aicrew Dev free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Aicrew Dev?

AKSoftCode (@aksoftcode) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Aicrew Dev by aksoftcode — Agent Skill

Name: Aicrew Dev
Author: AKSoftCode

File contents

aicrew /dev (Codex)

Codex does not support slash commands. Use this skill when the user asks for "/dev" or a full SDLC pipeline.

Token foundation (mandatory)

/dev, /fix, and /quick all share the same 11-capability token-saving stack — only pipeline depth differs. Full reference: ~/Agents/docs/token-foundation.md. Stack: graph-first (codebase-memory-mcp), speculative Scout → verify (SCOUT schema, two-model routing), Karpathy guardrails, layered guardrails (guardrails-taxonomy.md), context-economy read policy, security-guard.py hooks, .ai/state checkpoints, /compact between phases, /handoff on tool switch, optional context-mode + token-optimizer-mcp, caveman default output. For /dev, Scout opens Phase 1 Research before any Glob/Grep/Read; re-scout between phases if context grew.

Default output

Caveman/lean style by default. See ~/Agents/agents/caveman.md and ~/Agents/agents/context-economy.md. /normal or /lean off restores verbose.

Source of truth:

~/Agents/commands/dev.md
Project overrides in .ai/skills/ and repo AGENTS.md (if present)

Token foundation (mandatory — all phases):

Graph-first research: codebase-memory-mcp (search_graph → trace_path → get_code_snippet) before any Grep/Read
Speculative context: Scout pass (cheap model) at start of Phase 1; emit SCOUT: schema; verify before main trace
Layered guardrails: security-guard.py (input) → scope lock (Phase 0) → karpathy-guardrails (Phase 4) → security-reviewer (Phase 6)
Context economy: always on; /compact between phases; /lean amplifies
Two-model routing: Scout on haiku/mini; Research+Implement on sonnet/opus
See: ~/Agents/docs/token-foundation.md

Workflow summary:

Intake: clarify bug/feature/refactor, acceptance criteria, scope, risks, test plan.
Research: Scout pass (graph-first) → verify SCOUT schema → confirm root cause or key code paths.
Brainstorm: 3 alternatives with trade-offs (features/refactors).
Design: interface spec, contract checks, over/under-engineering flags.
Implement: load karpathy-guardrails; TDD first (RED -> GREEN -> REFACTOR per acceptance criterion).
Tests: targeted automated tests + smoke path.
Security: changed files only, no false positives.
Audit: if project defines domain audit checks.
Conclude: summary, tests run, risks, commit message.

Checkpoints: pause for user confirmation at intake, design, and before concluding.

---name: aicrew-devdescription: Run the aicrew /dev pipeline in Codex (intake -> research -> design -> TDD -> tests -> security -> audit -> infra -> conclude).---# aicrew /dev (Codex)Codex does not support slash commands. Use this skill when the user asks for "/dev" or a full SDLC pipeline.## Token foundation (mandatory)`/dev`, `/fix`, and `/quick` all share the same 11-capability token-saving stack — only pipeline depth differs. Full reference: `~/Agents/docs/token-foundation.md`. Stack: graph-first (`codebase-memory-mcp`), speculative Scout → verify (SCOUT schema, two-model routing), Karpathy guardrails, layered guardrails (`guardrails-taxonomy.md`), context-economy read policy, `security-guard.py` hooks, `.ai/state` checkpoints, `/compact` between phases, `/handoff` on tool switch, optional `context-mode` + `token-optimizer-mcp`, caveman default output. For `/dev`, Scout opens Phase 1 Research before any Glob/Grep/Read; re-scout between phases if context grew.## Default outputCaveman/lean style by default. See `~/Agents/agents/caveman.md` and `~/Agents/agents/context-economy.md`. `/normal` or `/lean off` restores verbose.Source of truth:- `~/Agents/commands/dev.md`- Project overrides in `.ai/skills/` and repo `AGENTS.md` (if present)Token foundation (mandatory — all phases):- Graph-first research: codebase-memory-mcp (search_graph → trace_path → get_code_snippet) before any Grep/Read- Speculative context: Scout pass (cheap model) at start of Phase 1; emit SCOUT: schema; verify before main trace- Layered guardrails: security-guard.py (input) → scope lock (Phase 0) → karpathy-guardrails (Phase 4) → security-reviewer (Phase 6)- Context economy: always on; /compact between phases; /lean amplifies- Two-model routing: Scout on haiku/mini; Research+Implement on sonnet/opus- See: ~/Agents/docs/token-foundation.mdWorkflow summary:1. Intake: clarify bug/feature/refactor, acceptance criteria, scope, risks, test plan.2. Research: Scout pass (graph-first) → verify SCOUT schema → confirm root cause or key code paths.3. Brainstorm: 3 alternatives with trade-offs (features/refactors).4. Design: interface spec, contract checks, over/under-engineering flags.5. Implement: load karpathy-guardrails; TDD first (RED -> GREEN -> REFACTOR per acceptance criterion).6. Tests: targeted automated tests + smoke path.7. Security: changed files only, no false positives.8. Audit: if project defines domain audit checks.9. Conclude: summary, tests run, risks, commit message.Checkpoints: pause for user confirmation at intake, design, and before concluding.

Frequently asked questions

Aicrew Dev