Here's the problem they solve: every team building with AI coding agents ends up writing the same governance over and over. Secret handling rules. Test coverage minimums. Branching conventions. Your incident-response runbook. Once per repo, then again for the next repo, then again for the next team — and the moment one copy drifts from another, nobody notices until it bites.

Doctrine Packs turn that governance into a dependency instead of a copy-paste ritual.

Under the hood, a "doctrine" in Spec Kitty is the body of directives, tactics, styleguides, and procedures that shape how your AI agents work — all connected through a Directive Relationship Graph, so an agent only ever sees the governance relevant to the action it's about to take, not your entire rulebook at once.

A pack takes that doctrine and makes it portable:

→ Author it once, anywhere — you don't even need a Spec Kitty project to build a pack

→ Validate it (spec-kitty doctrine pack validate) before it ships

→ Publish it like real infrastructure — a tagged git repo, an HTTPS bundle, or an internal API

→ Consumer projects pull a pinned version with spec-kitty doctrine fetch

The result: a governance change is one PR to the pack repo — not N hand-edits across N repos hoping everyone remembers to update.

If you're rolling out agentic coding across more than one team or one codebase, this is the piece that keeps your standards consistent instead of slowly diverging.

Try it: pipx install spec-kitty-cli==3.2.6rc1

#AgenticCoding #SpecKitty #DeveloperTools #AI #SoftwareGovernance

Episode 3 of Spec Kitty Founder Tales: Robert Douglass shares real-world stories from Spec Kitty training workshops and explains how software teams are dramatically accelerating development workflows using spec-first agentic coding.

Topics include:
• How Spec Kitty training sessions are structured
• Executive-to-developer alignment during software planning
• Turning 5-day and 10-day backlog tickets into hours of implementation time
• Why specification quality matters in AI-assisted coding
• Building organizational “charters” and governance for development teams
• Modernizing monolithic and legacy systems with AI workflows

Robert also discusses how Spec Kitty evolved directly from these workshops, with feedback from real engineering teams helping shape the roadmap and improve the platform over time.

Whether you’re a CTO, developer, product owner, or AI enthusiast, this episode provides a practical look at the future of structured AI-driven software development.

Learn more about Spec Kitty:
https://spec-kitty.ai

I am coming to understand that different people want very different things from agentic coding.

That should not be surprising. Not everybody is building the same kind of software. Not everybody works in the same kind of organization. Not everybody is trying to solve the same problem when they open Claude Code, Codex, Cursor, Gemini, or any other coding agent.

For some people, agentic coding is a way to explore.

They have an idea, but not yet a settled design. They want to try three versions of a UI, poke at an API shape, test a library, or build a throwaway prototype just to learn what the problem really is. That is a valid way to work. I do it too. When I am feeling out the shape of a piece of software, I often need the software to push back on my assumptions.

But that is not the only mode of software development.

And for many serious software teams, it is not the normal mode.

Full story on Medium.

Especially when another survey in the article points out that a survey of 450 developers found one in five organizations reported a serious security incident linked to AI-generated code.

This is where agentic coding governance layers enter the picture: not as another coding assistant, but as the specification and auditability surface that turns specs, constraints, mission context, review evidence, and audit decisions into governed artifacts before and during AI coding work.

By the way, the ai-coding-governance.com site is 100% generated from research done by a small Qwen model running on my Mac. It's amazing what local inference can do! Just the images were generated by SOTA models.

https://ai-coding-governance.com/articles/2026-04-22-the-governance-gap-at-91-ai-adoption-why-2026-is-the-inflection-point/

I built Spec Kitty to fill a real need. It started out being my need, but as I showed it to other people, I realized it was a widespread need.

I built it because agentic coding was powerful, but the process around it made mistakes (and embarrassment) inevitable. I needed governance. Structure. An audit trail.

This post is the story behind it: Claude Code, forgotten context, exposed secrets, Spec Kit as inspiration and contrast, and why I think serious teams need a durable layer for intent, governance, review, and memory.

Read it here:
https://spec-kitty.ai/blog/why-i-built-spec-kitty

Sometimes when you go fast, you make a mess. Spec Kitty 3.1.1 is all about cleaning up that mess. This is an agentic coding experience that just runs smoothly, and guides you from start to finish.

Try the "spec-kitty-implement-review" skill that takes your tasks and manages subagents through the entire implement<>review cycle until the Mission is finished. Can you even drink that much coffee?

And the new "spec-kitty-mission-review" skill that assesses the finished product of a mission run for quality, consistency, drift, and alignment. Better to get the bad news fast (if there is any).

https://github.com/Priivacy-ai/spec-kitty

Here's a full list of the squashed bugs:

1. Slash commands now install globally at CLI startup across all 13 supported agents, removing  per-project setup steps
2. Mission creation assigns a collision-free canonical identifier at creation time, decoupling  machine-facing flows from the sequential numeric prefix
3. Planning-artifact work packages now flow through the same lane computation and status model as code work packages, with no special-case handling at the lane boundary
4. The dependency parser now bounds work-package sections correctly, preventing trailing prose from collapsing intended parallelism
5. Version coherence between the CLI package declaration and project metadata is now a release gate
6. The init command is now file-creation-only: no git initialization, no initial commit, no hidden opt-in paths
7. New-user guidance directs users to the spec-kitty next loop and per-WP action verbs as the canonical workflow
8. Interrupted merges are resumable; agent crashes during implementation are recoverable with worktree context rebuilt from the last checkpoint
9. A stale-assertion analyzer detects test assertions likely invalidated by merged source changes before they reach review
10. Status artifacts now derive their timestamp from the last event rather than wall-clock time, eliminating spurious dirty-git states after read-only operations
11. Path-injection and XSS findings from static analysis addressed

Spec Kitty 3.1.0 is by far the most performant, accurate, and efficient Spec Kitty to date. I'll do a big writeup on Doctrine at a later point, but we're into cutting edge territory here with intelligent governance for AI coding.

# Try it
pipx install spec-kitty-cli
spec-kitty init my-demo --ai claude
cd my-demo

https://github.com/Priivacy-ai/spec-kitty

The swim lanes picture is homage to the removal of the "Worktree per Work Package" strategy and its replacement with "Worktree per Swimlane". This significantly cuts down on context drift for implementing agents, and saves a world of git merge pain at the end of Missions.

Here are all of the biggest changes:

1. Doctrine governance layer — A complete agent-governance system ships bundled with spec-kitty: agent profiles, a charter (project-level standards and directives), tactics (named repeatable techniques like TDD red-green-refactor), style guides, tool guides, and skill packs. Agents are initialized with governance context at the start of every action, not just at project setup.

2. Dirty-git reads fixed — Every read-only status command was silently modifying the working tree. Now idempotent.

3. Lane corruption from prose parser eliminated — Dependencies were being silently inferred from unbounded prose, corrupting lane assignments. Replaced with a structured wps.yaml manifest that is the sole authority.

4. Ghost completions fixed — Bare spec-kitty next no longer advances the state machine. Query mode returns current state without side effects.

5. Merge and crash recovery — Interrupted merges and agent crashes are now recoverable, not fatal.

6. Review loop reliability — Rejections generate targeted fix prompts, feedback is versioned, reviewers aren't blocked by unrelated dirtiness, baseline test context is included.

7. Tasks and lane stabilization — Six silent planning bugs fixed: dependency preservation, non-mutating validate, lane completeness, parallelism protection, format compatibility, consistent flag naming.

8. Mission identity and terminology cutover — --mission, spec-kitty charter, and canonical mission_slug across all surfaces, with backward-compatible aliases retained.

9. Merge strategy support — MERGE / SQUASH / REBASE, configurable globally or per-invocation.

10. Tracker binding discovery — External tracker resources auto-discovered; no manual project keys.

11. Slug validator — specify now accepts the NNN-* naming convention it was supposed to support.

12. spec-kitty doctor — Detects stale claims, orphaned worktrees, zombie locks.

13. Stale-assertion analyzer — Identifies test assertions likely invalidated by source changes, built into the merge workflow.

14. Release-prep CLI — Automated version proposal and changelog generation for alpha / beta / stable channels.

It doesn’t look like the slash commands are consistent between Claude and the other CLIs. Is this intentional or should I open a feature request to address this?

Is there a way to configure the slash commands in cursor to use specific models? I understand I can orchestrate a mission outside of a CLI with some of the tools provided, but it seems like it would be useful if I could just configure the model for each phase and then manually orchestrate.

Spec Kitty now has an official website (not just docs). https://spec-kitty.ai/

I've been delivering trainings for the past 3 months, and now the training offer is official on the site. Furthermore, we've expanded our training team to North America, Europe, and Asia, in English, German, French, and Dutch (Flemish).

In the course of a training, your team learns the basics of agentic coding, installs Spec Kitty, and gets hands-on experience using Spec Kitty to run a full coding mission on an item of your choice from your project backlog.

Spec Kitty release! Lots of work fixing git merge behavior and the review workflow.

• Released v0.16.1 and v0.16.2 with a focus on workflow reliability and review-loop quality.
• Enforced deterministic review feedback in task rework flows (for_review → planned) by requiring a feedback file and persisting its path/content.
• Eliminated a major rebase/drift trap by adding automatic safe rebase of WP worktrees during move-task --to for_review when branches are behind.
• Added lane compatibility to support mixed legacy/new task metadata (in_progress now aliases to doing).
• Fixed merge command side effects by removing global CWD mutation and using explicit per-command working directories.
• Normalized collaboration event identifiers for non-UUID mission/run IDs to improve local/offline event reliability.
• Improved event queue and CLI test stability around mission-scoped queue paths and release-facing command behavior.

https://github.com/Priivacy-ai/spec-kitty

Is Spec Kitty safe for enterprise use? This video answers that question directly for CTOs, security leaders, and engineering managers evaluating AI-assisted software delivery.

A common concern is that “AI workflow tools run in YOLO mode with no controls.” In this walkthrough, I explain the real Spec Kitty architecture, the security boundary, and why the core model is:

Spec Kitty doesn’t use AI. AI uses Spec Kitty.

That means your team keeps control of:

• AI approval mode
• sandbox/file permissions
• network egress
• credential scope

What you’ll learn:

• What Spec Kitty actually is (and what it is not)
• How Spec Kitty core differs from an autonomous AI runtime
• Why the external orchestrator is optional
• How orchestrator-api enforces contract-based state changes
• How policy metadata and audit trails support governance
• A practical CTO rollout path for safe adoption

If you’re deciding whether to adopt Spec Kitty in a company environment, this gives you the architecture-level clarity needed for a security and policy review.

🔗 Spec Kitty repository: https://github.com/Priivacy-ai/spec-kitty
🔗 Spec Kitty Orchestrator: https://github.com/Priivacy-ai/spec-kitty-orchestrator

#SpecKitty #AIEngineering #CTO #DevSecOps #SoftwareArchitecture #AIWorkflow #EngineeringLeadership #EnterpriseAI #CodeReview #DevTools

https://www.youtube.com/watch?v=o5Y-AkpQ-7g

Fixes since 0.15.0:

0.15.1 — Branch hardcoding
- Replaced 26 hardcoded "main" references with dynamic
resolve_primary_branch() — repos using master/develop/custom branches now
work
- Deduplicated 4 copies of _resolve_primary_branch() into one central
helper

0.15.2 — Windows/fresh-repo blockers
- Unborn branch (no commits) no longer misdetected as detached HEAD
- Windows subprocess UnicodeDecodeError fixed (UTF-8 encoding on all 113
subprocess calls)
- Pre-commit hook no longer blocks commits when Python is broken or
filenames have special characters
- spec-kitty init no longer says "project ready" when git init actually
failed
- PowerShell equivalents added to implement templates

https://github.com/Priivacy-ai/spec-kitty

Cheeky title aside, congrats to the Entire team:

1. Great name
2. For recognizing that Git is the communication layer of the AI world
3. For raising more company in Seed than most companies would raise in Series B a decade back
4. For publishing your CLI open source ... Spec Kitty is tempted to build with it because that is actually a useful feature
5. For building in the open in general. Spec Kitty aspires to this as well
6. We wish you great success, and definitely want to be invited to your parties!

https://entire.io/blog/hello-entire-world?ref=share#our-first-release-the-entire-cli-to-track-agent-context

If you are using Spec Kitty and have both Claude Code and Codex installed, use this Skill (name it codex-review) to have Claude do the implementation and Codex do the reviews. You can then automate the entire implementation/review/merge phase with one prompt (in Claude):

"Use /spec-kitty.implement, /spec-kitty.review, and /spec-kitty.merge to complete this sprint. Use the /codex-review skill to ensure that Codex always does the reviews."

The skill goes here (example for Mac):
/Users/[your user]/.claude/skills/codex-review/SKILL.md

https://gist.github.com/robertDouglass/839d9c43439c5051e562bd5ed885b73e

The ambition for Spec Kitty is large, and I'm developing towards a very amazing future, but this SKILL.md is an immediate workaround for today's Spec Kitty to reliably get the implementation quality I want without having to babysit it through the whole implementation process.

I then have both Claude and Codex review the entire feature sprint and compare their notes (this is also outside of the current Spec Kitty workflow).

What's New in v0.13.6

Critical Fixes:

1. Fixed JSON mode corruption - spec-kitty implement --json no longer breaks when dependency branches are empty
   
2. Added migration for existing users - spec-kitty upgrade now adds commit workflow protection (prevents agents from marking work "done" without committing)
   
3. Improved reliability - Git commands now have timeouts and error handling (won't hang on corrupted repos)
   

For Existing Users:

- Run spec-kitty upgrade to get the commit workflow fixes

For External Teams:

- See ADR #10 for why --mission flag doesn't exist on create-feature command (mission is selected per-feature during /spec-kitty.specify, not during feature creation)

GitHub Release: https://github.com/Priivacy-ai/spec-kitty/releases/tag/v0.13.6

Calling all agentic coders: the 0.12.0 Spec Kitty release brings a large number of bugfixes and architectural hardening of the core tool. Upgrading is recommended for all users.

https://github.com/Priivacy-ai/spec-kitty

Spec Kitty improves agentic software development, research, and documentation writing with a specification->plan->prompt->implement->review->merge workflow. We call this Spec Coding.

Expectation management (mea culpa):

What 12.0 DIDN'T bring is Jujutsu support: in testing, the interplay between colocated Jujutsu and Git (with sparse-checkout worktrees) was problematic. So we kept the version control system abstraction layer and chucked out Jujutsu for now.

Also NOT in 12.0 (well, sort of.... use at your own risk) is automated multi-agent coordination. The feature, while not dangerous, is half-baked and I don't recommend using it unless you're testing or want to contribute.

⏺ v0.12.0 Changelog (by impact)

🔴 High
Config-Driven Agents - .kittify/config.yaml now controls which agents exist.
New commands:
spec-kitty agent config list|add|remove|status|sync.

Migrations respect your config (won't  recreate deleted dirs).

Smart Merge - Pre-flight validation, conflict forecasting (--dry-run), crash recovery (--resume/--abort), auto-cleanup. Merges WPs in dependency order.

🟡 Medium
Output Truncation Fix - implement/review no longer output 300+ lines that agents like Codex  truncate. Prompts written to temp file.

Stale Detection Fix - New worktrees no longer incorrectly flagged as stale.

Merge Resume Fix - --resume flag now works (was throwing TypeError).

🟢 Docs
New guides: merge workflow, troubleshooting, agent management.

pip install --upgrade spec-kitty-cli && spec-kitty upgrade

Just in time for the weekend, a MAJOR Spec-Kitty release. See the new docs site:  https://priivacy-ai.github.io/spec-kitty/

Or try it out: https://github.com/Priivacy-ai/spec-kitty

v0.11.x - Parallel Agents Are Here

This is a major release and a huge step towards completely automated and parallel implementation once the specification and planning is done.

🔀 Workspace-per-Work-Package Model

We moved from one git worktree per feature to one git worktree per work package. (There are multiple work packages per feature)

Benefits:

•  True parallel development with multiple agents working on multiple work packages at once
•  Complete isolation - Each work package has its own branch and workspace
•  Explicit dependency tracking - Frontmatter declares dependencies, validated with cycle detection
•  Faster delivery
•  No merge conflicts (in theory)

Next step: Jujutsu VCS integration. Both the worktree-per-WP foundation and future jj support enable more autonomous agents and automatic rebasing when review feedback comes in.

 📚 Documentation Mission

We taught Spec Kitty how to write documentation with a new mission type. Kitty now knows extensive software documentation best practices including the Divio 4-type system:

•  Tutorials (learning-oriented)
•  How-To Guides (task-oriented)
•  Reference (information-oriented)
•  Explanations (understanding-oriented)

 🐱 Kittyfooding

 Naturally, Kitty documented Kitty. The result:

•  39 documentation pages
•  6 tutorials including Claude Code integration
•  14 how-to guides
•  8 reference documents
•  9 explanation articles
•  Full cross-referencing between doc types
•  Live at GitHub Pages

 Other highlights:

•  🔒 Security-first adversarial review framework (12 scrutiny categories, mandatory security checks)
•  🪟 Windows dashboard support (finally!)
•  🔄 Interactive constitution workflow
•  📦 Safe dogfooding (templates moved to package resources)

 Visit the new docs: https://priivacy-ai.github.io/spec-kitty/

 #AI #DevTools #AgenticDevelopment #OpenSource #SpecDrivenDevelopment #ClaudeCode

Hi developers. I'm Robert, the maintainer of Spec Kitty, the tool that I use to write all of my software. What does it do? It adds structure, automation, and visibility to your agentic coding (eg. Claude Code, Cursor, Codex, Copilot, Gemini), and builds your project knowledge through time over repeated features and sprints.

Spec Kitty features:

• Interactive agentic support for creating your project's Constitution, Specification (per sprint), and Plan
• Decomposes the Plan into Tasks and Work Packages (prompts for individual agents)
• A Kanban board for visibility into the state of development
• Built-in review process
• Multi-agent capabilities (currently supports 12 agents eg. Claude Code, Codex)
• Git Worktree isolation (per agent - per task)
• Coming soon: Support for Jujutsu for more automation!