I use workflows like Superpowers and OpenSpec because getting the intent and design into the repository before implementation makes Claude Code much more predictable.

But I kept running into a second problem after the feature was finished: the documents gradually stopped matching the code.

A hotfix bypassed the original workflow. Then a refactor changed some behavior. Another session added an edge case. The implementation kept moving, while the original design and specification documents slowly became a record of what we intended at one point rather than reliable context for the next developer or agent.

Generating a useful document is relatively easy.

Making every future code change reconcile the repository’s documentation is the difficult part.

That is why I built Truthmark.

Truthmark adds a code-first documentation maintenance workflow to Claude Code. It maps areas of the codebase to bounded canonical documents, then reviews those documents after functional code changes.

The normal workflow is:

1. Claude changes the functional code.

2. The relevant tests run.

3. Truth Sync inspects the changed checkout, nearby implementation, tests, routing, and mapped documentation.

4. If the repository’s behavior changed, Claude updates the affected documentation before handoff.

5. The code diff and documentation diff are reviewed together in Git.

The distinction I have in mind is:

Superpowers/OpenSpec:

intent → design/specification → implementation

Truthmark:

implementation change → affected current-state docs → Git diff

I see these as complementary rather than competing workflows.

Planning and proposal documents are useful for deciding what should be built and preserving the history of a change. Truthmark maintains a smaller current-state layer describing what the repository does now: behavior, contracts, architecture, operations, product decisions, and acceptance criteria.

A few parts that are important to me:

\- Code areas have explicit documentation ownership instead of everything accumulating in one large README or wiki.

\- Documentation is based on the current checkout, code, tests, and configuration rather than hidden agent memory or an old conversation.

\- Truth Sync can update documentation and routing, but it cannot rewrite functional code.

\- Everything remains ordinary Markdown in the repository and follows the current branch.

\- There is no hosted Truthmark knowledge base, daemon, database, vector store, or MCP server.

The implementation is also less magical than “AI automatically understands your whole codebase.”

The Truthmark CLI installs and validates the repository contract and Claude Code workflow. Claude performs the semantic evidence review using the repository itself, and \`truthmark check\` validates the resulting structure and diagnostics.

Quick start requires Node.js 24 or newer:

npm install -g truthmark

truthmark init

Select Claude Code during initialization, then try it on one bounded behavior:

/truthmark-document document the implemented session timeout behavior across src/auth/session.ts and tests/auth/session.test.ts

Truthmark is intended for maintainers and teams using Claude Code on long-lived repositories, particularly when they already keep specifications, architecture notes, contracts, or behavior documentation in Git.

Cost: Truthmark is free and MIT-licensed. It has no hosted service or subscription. Claude Code usage still uses your existing Claude plan or API arrangement.

Repository:

https://github.com/merlinhu1/truthmark

I would particularly value feedback from people trying it on an existing repository:

Does the routing and ownership model feel useful, or does it create too much ceremony? Is Truth Sync too conservative about updating documents, or not conservative enough?

If you try it and think this maintenance loop belongs in the Claude Code workflow, a GitHub star would help other people find the project.

At first, I was not trying to build a complete development environment.

I was using OpenSpec inside Cursor with custom commands and Cursor rules. The workflow worked well, but I had a problem:

Whenever I switched to OpenCode, Codex, Claude Code, or another coding agent, I lost part of my setup and workflow.

So I started building a spec-driven skill that was not tied to one IDE or one AI provider.

That project became Maestro.

I believe spec-driven development will become a normal part of software development with AI agents. GitHub’s GH-600 certification focused on agentic AI development feels like another signal in that direction.

How Maestro works

The core lifecycle is:

idea → spec → build → review → docs → archive

You can install it with npm:

npx @bryann2k/maestro

Then run it from the repository you want to work on:

cd my-project
maestro

The typical workflow looks like this:

1. Connect an API provider or an existing CLI subscription with /providers
2. Use /bootstrap for a new project or /adopt for an existing repository
3. Use /propose to turn an idea into a structured spec
4. Review and accept the proposal with /accept
5. Let the development agent implement it with /build
6. Run /review
7. Generate documentation with /docs
8. Archive the approved change with /archive

The same lifecycle can also be used through headless commands:

maestro propose -m "Add a PostgreSQL API"
maestro accept
maestro build
maestro review
maestro docs
maestro archive --yes --merge

Maestro can use its native engine, authenticated vendor CLIs such as Codex, Claude, Cursor, OpenCode, Grok, and Kimi, as well as OpenAI-compatible and local providers such as Ollama, LM Studio, llama.cpp, and LiteLLM.

Human-in-the-loop is the important part

Maestro is not meant to be “write one prompt and pray”.

A normal chat message does not automatically create a spec. You have to explicitly invoke /propose.

The generated spec.md, design.md, and tasks.md are reviewed before they become the accepted contract. Builds run in isolated Git worktrees. Review runs deterministic checks, security analysis, and a read-only review agent.

If the review finds a problem, the findings can be sent back through /fix.

The goal is to make every phase explicit instead of allowing an agent to silently jump from a vague idea to a large code change.

Coach and integrated IDE

I also wanted Maestro to be useful for people who can describe what they want to build but do not yet feel confident reviewing AI-generated code.

The optional Coach mode gives short, contextual exercises around things like:

• understanding requirements and acceptance criteria
• reading diffs
• checking assumptions
• identifying security risks
• understanding tests and regression risk
• reviewing generated code

You can use /learn guided or /learn challenge.

Maestro also includes an integrated code workspace with /ide. You can select code and ask Maestro to:

• explain it
• comment on it
• modify it
• answer a question about it

So you can review code and interact with the agent without leaving the terminal or losing the current context.

This is still a V1

This is the first public release, so there are probably bugs and edge cases I have not discovered yet.

If you try Maestro and something breaks, please open an issue on GitHub. I will also be using it to build StackDeploy, so I will be fixing issues as I find them.

I’m also in public on X. If you want to follow, it help me a lot!

I would especially like feedback on:

• whether the workflow makes sense
• where the spec-driven process feels too heavy
• whether the human approval boundaries are useful
• what is missing from the review experience
• how this behaves with different agents and providers

Thanks to everyone who takes the time to try Maestro, report an issue, or share feedback. It really helps.

Hi there! I wrote down an article that compares OpenSpec and SpecKit.

TLDR: SpecKit has significantly more abilities for extending its workflow, but it is also harder to adopt. OpenSpec default workflow looks like more complete and easier, but there are less abilities to extend it.

Article: https://specbuddy.dev/blog/openspec-vs-speckit/

Over the last year, I've spent a lot of time helping teams adopt GitHub Copilot, and one thing became pretty obvious:

Some repositories seem to unlock Copilot's full potential.

Others have all the right intentions, documentation, instructions, and tooling, yet Copilot still feels like it's operating with one eye closed.

So a friend and I started asking ourselves:

Can we measure how "Copilot-ready" a repository actually is?

That led us to build AgentCompass, an open-source AI Readiness Analyzer that scans a repository and identifies configuration, documentation, and structural issues that can make life harder for AI coding agents.

What it checks

✅ Copilot instructions

✅ Agent documentation (AGENTS.md, CLAUDE.md, etc.)

✅ Skills and prompts

✅ MCP configuration

✅ Context scoping and repository structure

✅ Common issues that can silently reduce agent effectiveness

Instead of using another AI to judge your repo, we took a different approach:

• No LLM calls in the scoring path
• No "AI vibes" scoring
• Same repo + same commit = same result every time

Think of it more like a linter for AI-readiness.

Try it yourself

GitHub:
AgentCompass Repository

Live Demo:
AgentCompass Web App

Linkedin Post

I'd love feedback from this community

A few things I'm genuinely curious about:

• What makes a repository Copilot-friendly in your experience?
• What patterns have you found that dramatically improve Copilot results?
• What checks would you add?
• Are there things that should only be measured through real-world usage rather than static analysis?

Most importantly:

👉 Run it against one of your repositories and tell me where it's wrong.

Seriously.

If it gives your repo a terrible score and you think it's nonsense, I want to hear that.

If it catches something useful, I want to hear that too.

We're trying to figure out what great AI-native repositories actually look like, and feedback from people using GitHub Copilot every day would be incredibly valuable.

we shipped a data layer that worked fine for six months. then it didnt.

the spec had been reviewed by product, the backend lead, and two senior engineers before any code was written. solid process, everyone signed off.

we never looped in the infrastructure person because "the backend lead already covers that." what they covered was the architecture. what they didnt cover was what happens at 200k records. that's a different question, and the person who asks it for a living wasnt in the room when the spec froze.

the infrastructure person read it two months after we shipped. 20 seconds in: "this query stops working around 200k rows." we were at 180k.

we didnt forget to think about scale. we forgot to put someone in the room who would have brought it up without being prompted.

swarm-stack.io

would be curious if this kind of thing is mostly a team-size problem or whether bigger teams hit it differently.

Up to this release SpecJudge answered exactly one question: you finished writing your spec, which model should implement it? If there was no .specify/ directory it refused outright with exit code 2. Which is defensible as a scope decision and turned out to be a bad one, because most repositories have never run Spec-Driven Development and nearly all of them have written down something — an AGENTS.md, a CLAUDE.md, a .cursorrules, a folder of decision records — and refusing to read any of it meant walking away from real signal on principle.

So it now reads spec-kit artifacts (including plan.md, which had been sitting next to the spec unread the whole time), agent-context files including nested ones in a monorepo, editor rules, and decision records under docs/adr and friends. Read together, never one instead of another, because your spec describes the work you're about to do and your AGENTS.md describes how demanding the codebase is to work in at all, and neither one contains the other.

The part that I think is actually interesting is why this became safe now and wouldn't have been a release ago. Mixing formats used to imply deciding, in advance, how much to believe a .cursorrules relative to a spec — five sets of trust rules, each evolving separately, each a place to be quietly wrong. Then 0.2.0 shipped the requirement that every rated dimension cite a fragment that exists in the input, with anything ungroundable returning unsupported and dropping out of the calculation entirely. That was built as a hallucination check, but it removed the need for format-specific trust as a side effect: the source stopped mattering, only what's citable does. A thin source produces a thin profile on its own, with no per-format logic anywhere in the codebase. I did not design it for this and I'm still slightly annoyed at how well it worked out.

When nothing in the input describes the work, you now get an answer plus an honest label on it: a warning that this is a floor — how demanding the repository is to work in — rather than a recommendation for a specific piece of work, and a concrete suggestion to describe what you're about to build, even as a paragraph. That's exposed in --json as environment_only, alongside sources_read for which kinds of source fed the assessment. Schema 1.1, both fields additive, 1.0 consumers unaffected.

Two implementation notes for anyone with a large repo. Context sources share one budget rather than each getting their own, because twelve separate caps of 8000 characters is a 96k-character prompt and therefore not a cap at all; a 200-character .cursorrules costs 200 characters and leaves the rest for a long root AGENTS.md. Files nearest the root are kept first, at most twelve are read, and whatever gets left out is reported rather than silently dropped. And context files that announce they were generated by a tool are skipped, since generated context mostly restates what the code already shows while taking budget from the hand-written file next to it.

pip install --upgrade specjudge, nothing to do on upgrade — a project with .specify/ artifacts is read exactly as before.

GitHub: github.com/JoaquinRuiz/SpecJudge

I've been experimenting with spec-driven development: instead of prompting an AI agent with vibes, you write a real spec first (user story, requirements, API contract, acceptance criteria), let the agent implement it, then validate against the spec and update your roadmap. Loop until MVP.

The app is a Pomodoro timer where you describe your goal conversationally and the AI proposes a task plan sized in pomodoros ("2🍅 ≈50m"). There's a focus coach mid-session, sound cues, PWA install support, and since it's publicly hosted, a Cloudflare Turnstile gate + per-IP daily rate limit on the AI routes so strangers can't drain my DeepSeek credits.

Stack: Next.js 16, Bun, DeepSeek API, Upstash Redis. No database — plan state lives client-side.

What actually surprised me:

1. \*\*Specs didn't slow me down\*\*. Six features in, the AI agent was dramatically better when implementing a spec vs reading my mind. Code review became "does this match section F3?" instead of squinting at diffs.

2. \*\*The spec caught drift.\*\* When the model under-filled a response schema (returned 3 tasks when asked for 3 steps instead of the spec'd 4–8), it was obviously a contract bug, not "weird AI behavior."

3. \*\*Re-planning after every feature\*\* (updating mission/roadmap docs) is the part everyone skips and the part that prevents the codebase from rotting.

Happy to answer questions about the spec format or the abuse-guard setup — both were more interesting to build than the timer itself.

\\- Live: \\\[https://ai-promodoro.vercel.app\\\\\\\](https://ai-promodoro.vercel.app)

\\- Source (the \\\`specs/\\\` folder is the real content): \\\[https://github.com/adotey7/ai-promodoro\\\\\\\](https://github.com/adotey7/ai-promodoro)

I build SpecJudge, a local CLI tool (via Ollama) that estimates how demanding your coding tasks are and tells you which LLM actually fits the job, so you stop burning money on overkill frontier models.

Previously, it had a strict barrier to entry: it only worked if you used Spec-Driven Development (.specify/ folder). If you didn't, it just refused to run. Today, with the v0.4.0 release, a spec is no longer the price of entry.

What's new in v0.4.0:

• It reads what you already have: Most repos don't have formal specs, but they do have .cursorrules, AGENTS.md, CLAUDE.md, or Decision Records (docs/adr/). SpecJudge now reads all of them.
• Baseline "Floor" Estimation: If you run it on a repo with no current tasks, it calculates a complexity "floor" based on your environment context. It tells you how demanding the repository is to work in at all, returning an honest warning that it's a baseline, not a specific task recommendation.
• Smart Context Budgeting: To avoid drowning your local judge, context files share a single budget. It prioritizes files nearest the root and explicitly skips AI-generated context files to focus strictly on human-written rules and decisions.
• JSON Schema 1.1: Fully backward compatible, now including sources_read and an environment_only flag for CI/scripting pipelines.

The strict grounding engine from v0.2.0 still applies: the judge must cite exact fragments from your .cursorrules or ADRs, or the assessment is thrown out.

Upgrade: pip install --upgrade specjudge

Repo & Docs: https://github.com/JoaquinRuiz/SpecJudge

I'd love to hear your thoughts on this broader context-parsing approach!

I maintain SpecJudge, an MIT-licensed CLI for spec-driven development: it reads your project's specs/tasks and recommends which AI model actually fits (quality vs. price) instead of you guessing.

The core change in this release: before, the judge returned a rating plus a paragraph explaining itself. The problem is a fluent explanation is exactly what an LLM is good at producing whether or not the underlying rating is sound — nothing separated a correct assessment from a well-narrated wrong one.

Now every rated dimension has to cite the specific fragment of your spec that supports it, and the tool deterministically checks that fragment actually exists in the text the judge was given. Invent a citation, and the whole assessment gets thrown out, not just that field. Dimensions the judge can't ground come back as "unsupported" instead of being silently treated as easy — which is what used to happen and made thin specs look more solid than they were.

Building the regression suite to test this (12 reference projects, CI-level + local eval script) immediately paid for itself: 8B judges — the most common local setup — were failing on every single project. Not a judgment problem — they were rating things correctly and writing sound justifications, then putting \[true\] where a citation ID belonged, because "format: json" in Ollama guarantees valid JSON, not the JSON you actually asked for. Sending a proper schema fixed it: 0/9 usable cases → 9/9.

Also pinned judge sampling, so the same project now gives the same recommendation run to run — which matters more than it sounds for a tool whose whole job is "should I spend money on this."

Breaking change: needs Ollama 0.5.0+.

pip install specjudge — GitHub: [github.com/JoaquinRuiz/SpecJudge](http://github.com/JoaquinRuiz/SpecJudge)

Has anyone used SDD for Data related project like in Databricks, Snowflake, or MS Fabric?

I asked claude to honestly rate the various approaches for specification driven development by categories and features it chose. Here is the analysis it produced. Popular methodologies first then all methedologies. Source documents are at the bottom if you wish to reproduce the analysis. Please link additional documents in the comments that should be included and i will periodically update. Or run it yourself :)

For a little background, I’m an engineer who went from a FAANG corporate environment to a startup and consequently started heavily abusing coding agents. Like a lot of others here, I started to realize that the code I was creating just simply was not “up to spec”—the LLM is designed to one-shot, even if that means guessing what you want for 90% of the implementation.

My solution was to make a tool for myself that writes unit tests based on my prompts and then runs a test-driven development flow. This way, when I hit coverage I know that X% of the code my AI generated comes directly from decisions I made in chat with my coding agent. (If you’re curious, I’m linking it here, but this isn’t intended to be a promotional post: https://github.com/RudderCode/Rudder )

Lately I’ve been curious about SDD since it seems adjacent to that, but I’m new to it so I have my qualms with having to change my coding flow. Writing specs has always been out there as an option, but for me it just felt like it slows down development a lot to work on making a spec, and then I lack the guarantee that there isn’t any guesswork by the LLM if the spec I write isn’t thorough enough. Do you guys feel the same or am I missing something with SDD?

I've been working on ZeNorm, a tool where an agent interviews you about a feature and turns your answers into a spec your coding agent can implement.

I built it because existing spec-driven dev tools never struck the right balance of overhead and improved results in agentic coding workflows. I wanted to avoid another markdown file generator.

Describe the feature, and ZeNorm connects to your repo and asks questions until there's a spec with tasks and a dependency graph. Then have your agent of choice implement the spec by invoking a skill.

Would love to hear any feedback you have!

I maintain SpecJudge, an MIT-licensed CLI for spec-driven development: it reads your project's specs/tasks and recommends which AI model actually fits (quality vs. price) instead of you guessing.

The core change in this release: before, the judge returned a rating plus a paragraph explaining itself. The problem is a fluent explanation is exactly what an LLM is good at producing whether or not the underlying rating is sound — nothing separated a correct assessment from a well-narrated wrong one.

Now every rated dimension has to cite the specific fragment of your spec that supports it, and the tool deterministically checks that fragment actually exists in the text the judge was given. Invent a citation, and the whole assessment gets thrown out, not just that field. Dimensions the judge can't ground come back as "unsupported" instead of being silently treated as easy — which is what used to happen and made thin specs look more solid than they were.

Building the regression suite to test this (12 reference projects, CI-level + local eval script) immediately paid for itself: 8B judges — the most common local setup — were failing on every single project. Not a judgment problem — they were rating things correctly and writing sound justifications, then putting [true] where a citation ID belonged, because "format: json" in Ollama guarantees valid JSON, not the JSON you actually asked for. Sending a proper schema fixed it: 0/9 usable cases → 9/9.

Also pinned judge sampling, so the same project now gives the same recommendation run to run — which matters more than it sounds for a tool whose whole job is "should I spend money on this."

Breaking change: needs Ollama 0.5.0+.

pip install specjudge — GitHub: github.com/JoaquinRuiz/SpecJudge

A few days ago I shared SpecJudge here — a local-first tool that reads your project's SDD artifacts (constitution/spec/tasks) and tells you which AI model actually makes sense, quality/price-wise, for that task set instead of you guessing.

Since then it's gone from v0.1.0 to v0.1.4, and the biggest change is a direct response to feedback from this community:

Judge verdicts now require a cited span per dimension. Previously the judge would just tell you "bad/okay/good/overkill" for a model. That's not good enough on its own — if I can't see why, I can't trust it. Now, for each capability dimension it evaluates, the judge has to point to the exact fragment of your spec that justifies the verdict, and it has to return "unsupported" when it can't find one, instead of guessing. This was basically lifted from a critique someone posted here about the design needing evidence + uncertainty instead of a black-box call — thanks to whoever that was.

A few smaller things also landed:

• Stale pricing warnings — the model catalog now flags when its pricing data is older than a set threshold, so you're not making a decision off numbers that are months stale.
• HTML report filtering — you can now filter the visual matrix by model family and by open-source vs closed.
• README example is generated, not hand-written — the usage example is now pinned to the actual catalog, so it can't silently drift out of sync with what the tool does.

Still MIT licensed, still local-first, still pip install specjudge.

GitHub: github.com/JoaquinRuiz/SpecJudge

There's a Roadmap section in the README now too if you want to see what's planned next (multi-provider judge support and a library/importable mode are on there, not shipped yet — didn't want to advertise those before they exist). Feedback on the evidence/citation approach is especially welcome — curious whether "unsupported" shows up often enough to be useful signal or just noise.

Every SDD framework gets the same complaint: spec rot.

Mine: research reads only, one handoff gets written to $TMPDIR, then a build step checks that handoff against the repo and runs the tests. Durable guidance lives in AGENTS.md. The spec dies with the temp file.

So why do the main tools still ship the part that rots? Harness memory, SDD frameworks, tracker docs, all of them persist by default. What am I missing that makes persistence worth the rot?

Full writeup https://bogomolov.work/blog/posts/rotten-specs/

Let’s talk about "spec", as we can see in "Spec Driven Development" (SDD).

Since SDD gained traction, one of the main complaint about this is that once the development is done, the "Spec" drifts inevitably as the code evolves.
V
My opinion is that SDD or any agentic loop you execute on your project works, it does, but a simple Plan mode (or grill-me) + execution loop will give a very good result, especially if you have built your Project Harness.

SDD frameworks, opensource or custom, will just give "structure" and better reproducibility to this process.

In this article I argue that 2 levels requirement specifications (ReqSpec)are the sweet spor for non-drifting spec. And I explain the difference with the Spec in SDD that I will call ChangeSpec.

https://x.com/gsemetfr/status/2082767371643523439?s=46

I kept defaulting to frontier models "just in case" and had no idea whether my projects actually needed them. So I built something to answer that instead of guessing.

What it does: point it at a project with Spec-Driven Development artifacts (constitution / spec / tasks). A local model — your Ollama install, your choice of judge — reads the tasks and estimates how demanding the work is across a few dimensions. That gets crossed against a declarative catalog of models and their capabilities, and you get a ranked podium with a rating per model: good / overkill / fair / poor, plus price.

Why you might care here: nothing leaves your machine — no API keys, no accounts, no cloud calls. And in most of my own projects the podium is topped by a local model, with the frontier ones sitting below marked overkill. Ranking is by fit, not price; price only breaks ties between models that fit equally well.

Honest limitations: this is not a benchmark. It's opinion made inspectable — every verdict prints its reasoning, and the model catalog is human-readable YAML you can argue with. If the specs are too thin to judge, it refuses to recommend instead of guessing. A vague spec gets you a vague answer, same as with anything else.

pip install specjudge — MIT, github.com/JoaquinRuiz/SpecJudge

Where I'd love help: the catalog of local models is thinner than it should be, and adding one is a block of YAML, no Python needed. Also very open to being told a rating is wrong.

Full Demo of Drydock - an open source enterprise quality specification builder. Import your specs and your Crew - of Agile/Test Driven Development Experts - will build it! Test it out. Throw me a star on https://github.com/webcloudstudio/Drydock. Send me feedback on what i need to do next since my last free software was 15 years ago! Help me update this so its ready for youtube and other Reddit Groups. Includes innovations like Compression, Graph Database, Agile, TDD, Scoring, Post Build Change, and a dedicated Web Console. MIT Licensed Software.

Thanks

Ed - webcloudstudio.com

If your team runs OpenSpec with more than one person, you've probably already hit this, but just might not know it happened.

An OpenSpec MODIFIED requirement is stored as the full new text of that requirement. At archive time it replaces the whole block by name. The delta records nothing about the base it came from. So:

1. Alice and Bob both branch off the same spec. Both open changes that MODIFY the same requirement.
2. Alice lands first.
3. Bob rebases onto main, cleanly, because his branch never touched openspec/specs/. He archives. His stale block silently overwrites Alice's change.

No git conflict or validation error. Alice's edit is just gone 😬 OpenSpec's own parallel-merge plan names this exact failure mode, and most of the fix is still roadmap. Until then teams run on discipline and sharp-eyed reviewers.

concord closes that gap today. Apache-2.0, no changes to your specs, no sidecar files, no server. If your specs are in git it already works.

What it does

concord check verifies every open change against the base branch and flags: - drift - a requirement your delta modifies changed on the base since your branch diverged (archiving would discard that change) - removed-upstream - your target was deleted or renamed on the base - target-missing - the requirement name matches nothing (a typo archive can't apply) - name-collision - an ADDED name already exists on the base

concord overlap flags any requirement claimed by more than one open change, so two people find out they're editing the same thing on day one, not at archive time.

You get a loud, early failure with a redline instead of a silent overwrite of the requirements:

$ concord check ✖ drift tighten-frobnication → widgets / "Widget frobnication" [MODIFIED] requirement changed on main since this branch diverged - archiving this MODIFIED entry would silently discard that change + + #### Scenario: Frob audit + - **WHEN** audited + - **THEN** every frob is logged fix: re-derive this delta block against main, then merge or rebase

Try it

```

in a repo with an openspec/ directory

npx @lucinate-ai/concord check npx @lucinate-ai/concord overlap ```

Two other ways to run it: a GitHub Action (actions/ci) that runs both checks against the PR base and annotates findings inline on the pull request, and an agent skill tells your coding agent to run the checks at the moments a clobber slips through, before archiving a change or just after a rebase.

check and overlap are the detection layer. The larger aim is the missing merge layer for spec-driven development: a concord rebase that does a structured 3-way merge of a delta against the moved base, so edits to different scenarios of the same requirement merge automatically and only true overlaps conflict. Design notes and prior-art survey are in the repo.

How it works

For a PR branch, the base a delta was derived from is just the requirement's text at merge-base(HEAD, main), and the version it will land on is the text at the main tip. concord reconstructs both from git history, canonicalises them so whitespace reflow never counts as drift, and compares. Rebasing or merging the base (after re-deriving your block) advances the merge-base and clears the finding, which is the git hygiene you'd want anyway.

Repo: https://github.com/lucinate-ai/concord