agentic-dev 0.14.0

Vendor-neutral tooling for making repositories agent-ready and preparing safe, reproducible environments for coding agents.

The goal is simple: make Claude Code, Codex, and similar agents spend less time rediscovering a repository and more time making correct changes with the right local tools.

Assess. Explain. Plan. Build agent-ready repositories.

A strong model is not enough by itself. Good agentic development also needs fast deterministic search, semantic code navigation, dependency awareness, current documentation, reproducible runtimes, isolated Git workflows, and a verification loop. This repo wires those pieces together without forcing one language, framework, or package manager on every project.

The problem

Out of the box, coding agents often fall back to a costly loop:

grep -> read large file -> grep again -> infer relationships -> edit -> hope

That works on small tasks, but it scales poorly. The agent can waste context reconstructing symbol relationships, miss call paths, use stale external APIs, install the wrong runtime, or let multiple agents collide in the same checkout.

Agentic Dev creates a local toolchain where each job has a better tool:

exact search          -> rg / fd
structural code       -> ast-grep
symbols/references    -> Serena
call graph / impact   -> CodeGraph
current external docs -> Context7
whole-repo snapshot   -> Repomix
runtime versions      -> mise
Python environments   -> uv
verification          -> compiler / linter / tests
isolation             -> Git worktrees

The model still reasons. Deterministic tools retrieve and verify. Agent Skills add a fourth layer: reusable engineering workflows that are loaded only when the repo or current task makes them useful.

What this repo provides

The agentic CLI is the public command surface:

• agentic setup — run once per workstation. Installs the common foundation, optional language/toolchain support, and can wire supported tools into coding agents.
• agentic repo init — run per repository. Detects languages, frameworks, package managers, runtime declarations, build/test commands, code-intelligence prerequisites, and relevant Agent Skills.
• agentic ready assess|explain|plan — measure a repository against the Agent Ready Specification: requirement-based maturity, concrete evidence, and a read-only remediation plan.
• the rest of agentic manages skills, capabilities, trust, worktrees, verification, execution, providers, remotes, integrations, and local metrics.

The repository initializer follows this rule:

Detect -> augment -> never blindly overwrite.

It preserves existing AGENTS.md, CLAUDE.md, project version files, package-manager conventions, and build wrappers.

Quick start

1. Install Agentic Dev

For normal use, install the Python distribution once a stable release is published:

uv tool install agentic-dev
agentic --version

For prereleases, use:

uv tool install --prerelease allow agentic-dev

Until the first stable PyPI release is published, install from source using the development instructions below.

The wheel includes the workstation/repository bootstrap helpers and policy template, so agentic setup and agentic repo init work from a PyPI installation.

For development from source:

git clone https://github.com/szaher/agentic-dev.git
cd agentic-dev
./install.sh
exec "${SHELL:-zsh}"

The source installer puts agentic and its helper scripts in ~/.local/bin. Pre-rename helper aliases are retained for one transition release, but new automation should use only:

agentic setup
agentic repo init

Existing user state is migrated non-destructively on first CLI startup. See docs/MIGRATION.md.

After workstation setup, verify the environment:

agentic doctor

Machine-readable form for AgentFlow/automation:

agentic doctor --json

2. Bootstrap the Mac once

Recommended for a workstation that already contains multiple repositories:

agentic setup \
  --scan-root ~/saad/projects \
  --configure-agents

A minimal setup is simply:

agentic setup

To preinstall support for selected ecosystems:

agentic setup --languages python,go,rust,node

Or a broader workstation:

agentic setup --all-languages --configure-agents

3. Initialize each repository

cd ~/saad/projects/my-project
agentic repo init .

Read-only inspection first:

agentic repo init . --check

Stable machine-readable repository inspection:

agentic repo inspect . --json
agentic repo inspect . --task "review API compatibility" --json

This reports repository facts/evidence, package managers, polyglot build/test/lint/typecheck commands, skills, capabilities, and native agent integrations without modifying the repository.

Project dependency installation is deliberately opt-in:

agentic repo init . --install-project-deps

4. Add context-aware Agent Skills

The initializer now recommends a small set of skills after repository discovery:

agentic repo init .

Add task context to improve the recommendation:

agentic repo init . \
  --task "review the API change for backwards compatibility"

Or use the skills CLI directly:

agentic skills suggest .
agentic skills suggest . --task "debug controller reconciliation failures"
agentic skills list
agentic skills status .

The CLI explains why a skill matched and lets you accept the recommended set, choose specific entries, select all, or select none. Skills are local/untracked by default; use --shared or --skills-shared only when the team intentionally wants to commit them.

See docs/SKILLS.md.

5. Install native agent integrations

The same repo acts as a Claude Code marketplace, Codex marketplace/plugin, and Pi package:

agentic integrations install all
agentic integrations status

You can install individually:

agentic integrations install claude
agentic integrations install codex
agentic integrations install pi

The adapters stay intentionally small. They expose the agentic control plane; repo/task-specific engineering skills are still selected on demand rather than permanently loading the whole catalog.

See docs/INTEGRATIONS.md.

6. Add optional capabilities only when needed

Browser access is intentionally not part of the default install.

Preview capability recommendations:

agentic capabilities suggest . \
  --task "verify the checkout flow in the browser"

Then explicitly enable only the capability you want:

# Recommended default for UI automation/testing
agentic capabilities enable browser-automation --target both --mode isolated

# Frontend console/network/performance debugging
agentic capabilities enable browser-debug --target both --mode isolated

# Advanced autonomous browser workflows
agentic capabilities enable browser-agent

The browser providers are:

• Playwright MCP for structured deterministic UI automation;
• Chrome DevTools MCP for network, console, tracing, and frontend diagnostics;
• Browser Use for broader autonomous browser workflows.

Authenticated/existing-browser access is an explicit mode rather than a default:

agentic capabilities enable browser-automation \
  --target claude \
  --mode existing-browser

See docs/CAPABILITIES.md for the capability model and browser provider details.

7. Add security checks under explicit trust profiles

Security scans are optional capabilities:

agentic capabilities suggest . --task "security review before merge"

agentic capabilities enable secret-scan
agentic capabilities enable dependency-vulnerability
agentic capabilities enable iac-misconfiguration
agentic capabilities enable sbom

agentic capabilities enable sast --profile development

Run them with normalized JSON output:

agentic capabilities run secret-scan . --json
agentic capabilities run dependency-vulnerability . --json
agentic capabilities run sast . --profile development --json

Trust profiles make higher-risk modes explicit:

agentic trust list
agentic trust show safe
agentic trust set development
agentic trust set production-read --repo --path .

See docs/SECURITY.md and docs/TRUST.md.

Agent configuration

--configure-agents configures the tools that can be configured non-interactively and writes a managed tool-routing block into the user-level instruction files when the corresponding agent is installed:

Claude Code: ~/.claude/CLAUDE.md
Codex:       ~/.codex/AGENTS.md

Existing content outside the managed block is preserved.

The managed policy tells agents when to use rg, ast-grep, Serena, CodeGraph, Context7, Repomix, and the verification toolchain. The source template is templates/global-agent-policy.md.

Context7 setup may require authentication, so it remains explicit:

npx ctx7 setup --claude
npx ctx7 setup --codex

Agent Ready assessment

Measure how ready a repository is for coding agents against the versioned Agent Ready Specification:

agentic ready assess .                          # maturity + every requirement with evidence
agentic ready explain .                         # why the repository has its level
agentic ready explain context.agent_instructions
agentic ready plan . --target optimized         # read-only remediation plan

Maturity: Foundational (level 1 of 4)
Target:   Structured — not met

Levels
  ✓ 0 Unaware
  ✓ 1 Foundational   5 required, 4 pass, 1 n/a
  ✗ 2 Structured     11 required, 7 pass, 3 fail, 1 n/a
      blocking: constraints.documented, context.agent_instructions, context.agent_instructions.commands
  ✗ 3 Optimized      3 required, 1 fail, 1 unknown, 1 n/a
      blocking: constraints.architecture_boundaries, context.task_workflows
  ✗ 4 Autonomous     5 required, 4 fail, 1 unknown

Maturity is requirement-based, not a score. A level is reached only when all of its required rules, and those of every lower level, pass. pass, fail, unknown (team policy that cannot be inferred), and not-applicable are distinct. Assessment is deterministic, offline, uses no LLM, and never modifies the repository. The spec is pinned into the package by version and sha256.

See docs/READINESS.md.

Smart repository detection

The repo initializer recognizes common project signals for:

• Python
• Go
• Rust
• Node.js / TypeScript / JavaScript
• Deno
• Java and Kotlin
• C / C++
• Ruby
• PHP
• Swift
• Terraform
• Bash / Shell
• Lua
• Zig
• Angular, Svelte, and Vue
• Docker/Podman usage
• Helm and Kustomize
• Protobuf/Buf
• Bazel
• GitHub Actions
• Ansible

It also detects package-manager and runtime conventions such as:

uv.lock              pyproject.toml       poetry.lock
pnpm-lock.yaml       yarn.lock            package-lock.json
Cargo.toml           go.mod               Gemfile
composer.json        pom.xml              gradlew / mvnw
.mise.toml           .tool-versions       .python-version
.node-version        .nvmrc

For polyglot repositories, Serena is configured with the detected language set rather than treating the project as a single-language codebase.

Skill detection uses repository facts plus optional task text. It can recommend language, technology/repository-type, and workflow skills such as Python/Go/Rust/TypeScript engineering, Kubernetes operator development, API design, migrations, testing, debugging, review, refactoring, performance analysis, CI, containers, and Terraform. It does not automatically activate every match.

Safety defaults

The scripts are intentionally conservative.

• They are designed to be rerunnable.
• Existing AGENTS.md and CLAUDE.md files are not overwritten.
• Local generated intelligence state is added to .git/info/exclude, not the tracked .gitignore.
• Project dependencies are not installed unless --install-project-deps is passed.
• Container runtimes are detected but Docker Desktop/Podman Desktop are not auto-installed.
• Repository-declared versions are respected where possible instead of replacing them with global defaults.
• --check performs repository discovery without modifying the repo or installing support.

Useful switches:

agentic repo init . --no-codegraph
agentic repo init . --no-serena
agentic repo init . --no-instructions
agentic repo init . --no-install-language-deps
agentic repo init . --no-runtime-install

Why these tools

The core tools are intentionally complementary rather than redundant.

The full catalog, including language servers and auxiliary tools installed on demand, is in docs/TOOLS.md.

Worktrees for concurrent agents

Do not run several write-capable agents in the same working tree. Use the built-in manager:

agentic worktree create feature-a \
  --agent codex \
  --task "implement feature A"

agentic worktree list
agentic worktree status feature-a
agentic worktree clean feature-a --delete-branch

Dirty worktrees are refused unless --force is explicit. Agent/task metadata is stored locally and is not added to repository changes.

See docs/WORKTREES.md.

Change-aware verification

Plan the smallest relevant verification set from the current diff:

agentic verify
agentic verify --json
agentic verify plan --base main --symbol TrainingReconciler --json

Run the selected checks:

agentic verify run
agentic verify run --continue-on-failure --json

When available, CodeGraph contributes affected-test and symbol-impact evidence. Enabled security capabilities are folded into the plan when relevant.

See docs/VERIFICATION.md.

Execution backends

The default remains direct host execution, but verification can run through isolated environments:

agentic execution status

agentic execution configure container \
  --image python:3.13 \
  --network none \
  --repo

agentic verify run \
  --backend container \
  --image python:3.13 \
  --profile development \
  --json

Supported backends are host, Docker/Podman containers, Dev Containers, and Dagger Workspace execution.

See docs/EXECUTION.md.

Infrastructure capability packs

Database, Kubernetes/OpenShift, cloud, and observability access are opt-in and trust-gated:

agentic infra status --json

agentic capabilities enable database-read --profile development
agentic infra database schema sqlite --sqlite-file ./app.db --profile development --json

agentic capabilities enable cluster-read --profile production-read
agentic infra cluster run --profile production-read get pods

agentic capabilities enable cloud-read --profile production-read
agentic infra cloud identity aws --profile production-read --json

agentic capabilities enable observability-read --profile production-read
agentic infra observability status --profile production-read --json

Cluster/cloud write require custom profiles with explicit cluster.write or cloud.write; no built-in profile grants them.

See docs/INFRASTRUCTURE.md.

Repository layout

agentic-dev/
├── install.sh
├── pyproject.toml
├── src/agentic_dev/
│   ├── cli.py
│   ├── capabilities.py
│   ├── detect.py
│   ├── skills.py
│   ├── readiness/          # Agent Ready assessment + pinned spec bundle
│   └── builtin_skills/
├── integrations/
│   ├── claude/
│   ├── codex/
│   └── pi/
├── scripts/
│   ├── agentic-setup.sh
│   ├── agentic-setup-linux.sh
│   └── agentic-repo-init.sh
├── templates/
│   └── global-agent-policy.md
├── docs/
│   ├── ARCHITECTURE.md
│   ├── AGENT_CONFIGURATION.md
│   ├── API.md
│   ├── CAPABILITIES.md
│   ├── INTEGRATIONS.md
│   ├── READINESS.md
│   ├── SECURITY.md
│   ├── SKILLS.md
│   ├── TRUST.md
│   └── TOOLS.md
├── tests/
│   └── smoke.sh
└── .github/workflows/
    └── ci.yml

Design notes

See docs/ARCHITECTURE.md for the problem model, architecture, and why machine setup and repository setup are separate concerns.

See docs/API.md for the stable JSON contracts intended for AgentFlow, CI, plugins, and other automation.

See docs/AGENT_CONFIGURATION.md for global instruction hierarchy and MCP/tool setup.

See docs/INTEGRATIONS.md for Claude Code plugins, Codex plugins/marketplaces, and the Pi package.

See docs/CAPABILITIES.md for optional capability packs. See docs/SECURITY.md for local security scanning. See docs/TRUST.md for trust and permission profiles.

See docs/ROADMAP.md for Roadmap v2, which takes the project from the current v0.13 capability control plane toward a stable v1.0 agentic development platform. The completed v0.5–v0.13 roadmap is archived at docs/roadmaps/ROADMAP-v0.5-v0.13.md.

Development

Run the local checks:

make check

The CI workflow performs syntax checks and a read-only repository smoke test on macOS.

License

MIT. See LICENSE.

External providers

Extend the environment without patching core:

agentic providers add ./my-provider
agentic providers list
agentic providers verify my-provider
agentic providers doctor --json

# explicit updates only
agentic providers update my-provider --yes
agentic update --yes

Providers can contribute Agent Skills and trust-gated command capabilities through the versioned agentic-provider.json protocol. Git providers record their resolved commit SHA and can require a Git-verified signed commit. Installed content is SHA-256 checked before it is loaded.

See docs/PROVIDERS.md.

Linux / WSL and remote development

The workstation bootstrap now dispatches natively on macOS or Linux/WSL:

agentic setup --configure-agents

SSH development profiles describe remote machines without storing passwords or private-key contents:

agentic remote add gpu-lab gpu.example.com \
  --user saad \
  --identity-file ~/.ssh/id_ed25519 \
  --workdir /srv/project

agentic remote test gpu-lab --json

See docs/REMOTE.md.

Local measurement and evaluation

Metrics are off by default and remain local:

agentic metrics status
agentic metrics enable

agentic metrics summary --since 7d --json

The built-in instrumentation measures execution failures/durations, verification duration and first passing test timing, change size, worktree sessions, recommendation uptake, capability outcomes, and CodeGraph use. AgentFlow or native agent integrations can add local structured events such as:

agentic metrics record agentflow.stage \
  --session-id run-123 \
  --field stage=review \
  --field outcome=passed

agentic metrics record context.used \
  --session-id run-123 \
  --field source=serena \
  --field useful=true

Export is explicit and file-only:

agentic metrics export --output ./agentic-metrics.jsonl --since 7d

There is no network telemetry/export path.

See docs/METRICS.md.

Roadmap v2

The active roadmap is centered on four outcomes:

1. Make repositories agent-ready using the Agent Ready maturity model as a versioned specification, with assessment, remediation, and CI regression checks.
2. Make agent configurations portable through first-class Skill, Agent, AgentTeam, MCPServer, Plugin, Pack, WorkflowRef, and EnvironmentProfile artifacts plus local/federated catalogs.
3. Make task sessions minimal and reproducible with lockfiles, content-addressed storage, context budgeting, trust/policy, credentials, and unified execution backends.
4. Make the stack governable and measurable through deep AgentFlow integration, organization policy, evaluation, and optional telemetry.

The product boundary is deliberate:

agent-ready     -> defines the readiness standard
agentic-dev     -> assesses and prepares the environment
AgentFlow       -> governs and orchestrates development workflows

See the full Roadmap v2.

Brand assets

The canonical logo, mark, README banner, palette, and usage rules live in assets/brand/ and docs/BRAND.md.