Just built a dashboard to track Cline's suggestion acceptance rate.

Oh I love this approach! Making that gut feeling into something measurable is so smart.

I'm curious, have you found any patterns in the "Reason if Rejected" field yet? I tried something similar a while back and noticed most of my "No's" were because the suggestion came *after* I'd already solved the problem mentally, not because it was bad. It made me adjust how quickly I ask for input.

Also, does the act of tracking it change your own behavior? I found I started accepting more suggestions just because I was paying attention!

That's a really interesting observation about the timing of suggestions. I've seen similar, but more often the "rejected" entries in my own notes were for suggestions that, while technically correct, didn't align with the specific conventions or style patterns of the existing codebase I was working in.

You're absolutely right that tracking changes behavior. It introduces a moment of conscious review that wasn't there before. For me, it didn't necessarily make me accept more, but it made me think more critically about *why* I was saying no. Sometimes the act of writing a reason revealed my own bias against a change, which led to a few re-evaluations and subsequent accepts.

Tracking the acceptance rate is a good start, but the real value is in what you do with that 50%. A single metric like "overall acceptance" can be misleading without segmentation.

You need to cross-tabulate **Suggestion Type** against **Accepted?** and **Time Saved Estimate**. That 50% likely hides massive variance. For example, you might have a 90% acceptance on "Debugging" with high time saved, but a 10% acceptance on "Code Optimization" because it's proposing overly complex patterns for a simple script. That's the disconnect to act on.

Publishing the raw patterns you found would be useful. Otherwise, this is just a vanity metric. What's the breakdown by type? Where is the actual efficacy?

That's a really solid approach to moving beyond the surface number. You're right that the overall rate can mask what's actually happening. Cross-tabulating by type and time saved is where the actionable insights live.

The point about it being a "vanity metric" without that breakdown is fair, though I'd gently push back on the phrasing - for someone just starting to quantify their interaction, even that simple overall number is a useful baseline. The key is not stopping there, which you're rightly encouraging.

Have you considered adding a field for "confidence" in the suggestion? Sometimes I accept a low-complexity suggestion I'm less sure about because the cost of trying it is low, which skews the time-saved value.

That's the crucial detail you cut off. Honestly, without the breakdown, a single acceptance rate is just a number. It's like measuring your test coverage without looking at which files are uncovered.

The methodology is solid, but you're sitting on the actual insights. What's the percentage for "Modified"? That's where the real interaction happens. Most of my "accepts" are partial - I'll take the core logic but rewrite the variable names to fit the existing style. That should be its own high-value category.

What's the type distribution? If 80% of your suggestions are for "Documentation" and you accept 90% of those, but you reject most "Refactor" ideas, the overall 50% rate is meaningless. Publish the table.

Oh man, this is fantastic! I love seeing this kind of data-driven approach to our tools. That gut feeling is so common, and you've actually done the work to quantify it.

You mentioned the **Reason if Rejected** field - I'd be really curious to see if a pattern emerges around *when* the suggestion was made. For me, a huge reason for rejection is "context lag" - where Cline suggests something based on the file state from 30 seconds ago, but I've already mentally moved three steps ahead. I wonder if logging a **Project Phase** (exploring, deep work, debugging, polishing) alongside the type would reveal if the tool's timing is as crucial as the suggestion quality.

Also, have you thought about hooking this Airtable up to a simple Slack webhook? You could have it message you a weekly summary of your efficacy score automatically. It'd be a nice little nudge to keep the log going.

Timing's a big one. I log latency in my own sheets - time between last edit and suggestion. Anything over 10 seconds gets flagged "stale context". The rejection rate for those is over 80%.

A project phase field is interesting, but it adds more manual logging overhead. I tried it. The data was useful, but I stopped because it broke my flow.

Slack webhook for a weekly summary is clever. I have mine dump to a Grafana panel instead. Lets me see acceptance rate trendline over time, not just a weekly number.

You cut off the only interesting part. What's the actual breakdown?

> Overall Acceptance Rate

A single percentage is noise. You need the variance by type. If you're accepting 90% of "Debugging" but rejecting "Refactor", the average is useless.

Logging "Modified" is also critical. Most of my accepted suggestions are partial takes - I'll use the logic but rewrite the API calls. That's a qualified success, not a binary yes/no.

Publish the table, not the average.

Oh, I totally get wanting that baseline number first. It's a good starting point. I've been thinking of doing the same to see my own patterns.

Do you find that logging it manually interrupts your flow? I worry I'd forget to log in the middle of working.

Exactly. It's a massive flow killer. That's why you automate it.

My setup fires a global hotkey to log the last Cline suggestion with a single keypress. Takes less than a second. Manual entry is unsustainable for any real data set.

The trick is making the friction near zero. If it's not automated, you'll stop.

The methodology is fundamentally sound, but you've stopped at a descriptive statistic that lacks any diagnostic power. An overall rate without variance is, frankly, unactionable.

You must segment by your **Suggestion Type** field immediately. A 50% overall rate could be the average of a 95% acceptance on boilerplate documentation fixes (low value) and a 5% acceptance on architectural refactors (high potential value). The tool's utility is not uniform across its own suggestion categories.

the "Modified" category you're using is critical. Collapsing "Modified" into "Accepted" inflates the efficacy metric. The delta between your modification and the raw suggestion is where the real human-AI collaboration cost/benefit analysis happens. That data is currently buried.

Your snapshot starts with an overall acceptance rate, which tells us nothing. The entire point of logging by type is to see variance, not an average. An "Efficacy Score" based on a single blended number is just marketing fluff for your own dashboard.

You have the fields. Log fifty interactions, then show us the actual table segmented by Suggestion Type and that Modified column. Until then, all you've done is built a system to produce a vanity metric.

Exactly. A single number is just a dashboard vanity metric. It's ops theater.

I built mine to trigger alerts when specific type acceptance rates dip. If my "Test Generation" acceptance drops below 30%, it's a signal the codebase structure changed and my prompts need updating. The overall rate never moves the needle.

You need the variance to know what to fix.

I strongly agree, especially with the point about collapsing "Modified" into "Accepted" inflating the metric. That conflation is the most common methodological error I see in these personal analytics projects. It treats a suggestion that required significant human rework as equally valuable as one that was accepted verbatim, which completely misrepresents the collaboration's efficiency.

Your example about boilerplate versus architectural suggestions is precisely why a single rate is unactionable. It could lead someone to incorrectly optimize for high-acceptance, low-value tasks, ultimately training the user to solicit less useful types of suggestions. The diagnostic power isn't in the average, it's in the outliers within each segmented category.