Opportunities › Code quality metrics for structural decay detection › Proposed app
Sagline
Spot structural decay in pull requests before refactoring gets costly
- Score
- 65
- confidence 26
- Evidence
- 3
- items on the page
- Rivals
- 1
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2008
- Mature market · AI estimate
ScreensConcept
Desktop app: Teams review erosion scores and drift trends in pull requests, set thresholds and monitor hotspots—code quality review work integrated into desk-based CI/CD. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
PR #482 check
Over limit by 1.2-6.2
About this appAI proposal
Sagline measures cohesive erosion in your codebase: files that slowly take on unrelated jobs, pull in more neighbours and get harder to change. It runs in CI on every pull request and names the specific files where structure is drifting.
Built for engineering teams that review a lot of code, including code written with AI assistants. Bug and complexity checkers pass code that works but fits badly. Sagline looks at how each change affects the shape of the module around it.
Each pull request gets a short comment: which files eroded, by how much, and the trend over recent merges. Teams can set a limit and fail a check, or just watch the trend. Nothing leaves your CI beyond metrics.
- Tagline
- Catch the sag before it becomes a rewrite.
- Voice
- PlainPreciseCalm
- Why this name
- A sag is what a beam does slowly under load, which is how structure decays. Sagline reads as a line that tracks it.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Per-file erosion score
Each file gets a cohesion score from how its functions and data relate. A drop between commits shows up as a named file, not a vague project grade.
No member yet
Pull request comments
A short comment on each PR lists files that eroded, files that improved, and the net change, so reviewers see structure along with the diff.
No member yet
GitHub Actions step
Add one workflow step. It analyses the changed files and the modules they touch, then posts results and a pass or fail status.
No member yet
Decay trend over time
See each module's score across merges, so slow drift becomes visible long before anyone proposes a rewrite.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F2A33Slate Ink · text#F6F3ECPaper · surface#2F6B55Moss · primary#C4542DRust · accent, decay alert#D9CFBCSand · borders, data
Typography
Aa
Sagline
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Catch the sag before it becomes a rewrite.
PricingAI proposal
The material gives no competitor prices. These per-repository tiers are a starting point to test, based on the monetization idea in the opportunity.
Free
Free
- 1 public repository
- PR comments
- 30-day trend
Team
$49 a month
- 10 private repositories
- Blocking checks and baselines
- Full trend history
- Weekly hotspot list
Org
$199 a month
- 50 repositories
- AI-assisted change view
- Shared rules across repos
- Priority support
Would you pay Team · $49 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. PR comments and an optional README badge show scores to every reviewer and contributor. Public repositories on the free tier show the Sagline name to the maintainers and contributors who see it.
- Do they become customers?
- Contributors who see the comment may be developers who can bring it to their employer, but a badge is only exposure. Test whether comment viewers install it on their own repos.
- First customers
- Engineering leads at 10 to 50 person software teams on GitHub who use AI coding assistants heavily and review many pull requests each week.
- Where to reach them
- Hacker News: Share the metric method and results on open-source repos in a technical post, since the problem was raised there.
- GitHub Marketplace: List the Action so teams searching for code quality checks can install it.
- Engineering leader communities: Offer free hotspot reports to leads in return for feedback on whether the scores match their experience.
- First milestone
- Five teams keep the check on for 30 days and two pay for private repositories.
Why people already pay for this
- Public claimExisting code quality tools focus on bugs and complexity but do not address cohesive erosion
- Public claimStructural decay accumulates until it becomes expensive to change, requiring big refactors or rewrites
- Public claimA metric can identify specific files where structural erosion is occurring
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Code analysisTree-sitter parsers in a CI runnerParses many languages into syntax trees so cohesion can be computed per file without running the code.
- This app · Git integrationGitHub App and ActionsNeeded to read diffs, post PR comments and set check statuses.
RisksAI inference
- TechnicalThere is no agreed definition of cohesive erosion. A metric that does not match what developers feel as decay will be ignored, so it must be validated on real repositories.
- CommercialTeams may treat structure as a nice-to-have and not pay. Free linters and in-house scripts are a cheap alternative.
- TechnicalSupporting several languages with accurate cohesion scoring multiplies effort. The first version must pick one or two.
- PlatformDepends on GitHub APIs and Actions. Other hosts such as GitLab need separate work, and policy changes could affect the app.
- DataTeams may be wary of sending private source code to a service, so analysis should run in their CI and send only metrics.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.