3,883 messages across 236 sessions | 2026-01-02 to 2026-02-05
What's working: You're running a disciplined benchmark-driven optimization loop in Go — achieving major performance gains while having the judgment to revert what doesn't work. You're also effectively using Claude as a full-stack engineering partner, moving fluidly between Go performance work, Astro site optimization, and content pipeline automation in the same period.
What's hindering you: On Claude's side, bulk file edits tend to propagate errors rather than validate each change independently, and CSS fixes often trigger unintended side effects that spiral into multi-round corrections. On your side, deployment and infrastructure tasks tend to launch without a pre-flight check — like verifying image asset handling before deploying to Cloudflare — which leads to broken states that eat time to debug.
Quick wins to try: Your most common action is committing and pushing — create a custom slash command skill that handles your typical commit-and-push flow so you stop spending tokens on routine git operations. Also try using hooks to auto-run your Go benchmarks after edits, so you catch regressions immediately without manually triggering them each time.
Ambitious workflows: As models get more capable, you'll be able to run a deploy-and-verify loop where Claude deploys to a preview URL, runs Lighthouse and broken-link checks, and self-corrects until everything passes — eliminating the 'deployed but broken' pattern you hit with Cloudflare. Your Go optimization workflow is also a perfect fit for parallel task agents that each try a different strategy, benchmark-gate the results, and only keep what measurably improves performance.
| Messages | Lines Changed | Files | Days | Msgs/Day |
|---|---|---|---|---|
| 3,883 | +49,407 / -20,982 | 2,020 | 13 | 298.7 |
Focused on Lighthouse performance improvements for an Astro-based website, including responsive images, JPEG fallbacks, cache header fixes, and inline CSS optimization. Claude Code was used for iterative CSS and configuration changes across multiple files, with some friction around spacing fixes that inadvertently broke chart legend visibility.
Automated photo caption generation from EXIF metadata and EXIF stripping across MDX content files. Claude Code edited 11 MDX files in bulk but introduced copy/paste errors in alt text that required manual correction, highlighting challenges with repetitive multi-file content transformations.
Worked on deploying the Astro site to Cloudflare Pages. Claude Code assisted with deployment configuration but the session ended with broken images, indicating incomplete handling of the static asset pipeline in the deployment setup.
Intensive benchmark-driven optimization of a Go codebase, achieving +152% improvement in Patterns/sec and 64% memory reduction through iterative profiling and code changes. Claude Code was used heavily for editing, running benchmarks via Bash, and testing optimization hypotheses, though some attempts caused regressions that had to be reverted.
A brief session where Claude Code explained a complex closure pattern in Rust code (epsi_closure) and then simplified it by removing an unnecessary local variable and adding clarifying comments. This was a quick, fully successful code comprehension and refactoring task.
| Goal | Count |
|---|---|
| Commit And Push | 435 |
| Iterative Code Changes | 184 |
| Performance Optimization | 133 |
| Add Jpeg Fallback | 87 |
| Fix Build Caching | 87 |
| Fix Mobile Caption Spacing | 87 |
| Tool | Count |
|---|---|
| Bash | 4,003 |
| Read | 3,224 |
| Edit | 3,045 |
| Write | 1,162 |
| TaskUpdate | 957 |
| Grep | 864 |
| Language | Count |
|---|---|
| Go | 1,437 |
| CSS | 785 |
| YAML | 162 |
| Markdown | 36 |
| TypeScript | 34 |
| Shell | 6 |
| Type | Count |
|---|---|
| Iterative Refinement | 176 |
You are a prolific, high-volume user who drives Claude Code hard across 236 sessions and nearly 2,100 hours of usage in just over a month. Your dominant workflow is iterative refinement — you don't front-load detailed specifications but instead launch into tasks and course-correct as you go. This is evident in your top goals: "commit_and_push" dominates at 435 instances, meaning you're constantly shipping incremental changes, while "iterative_code_changes" at 184 confirms a tight build-test-adjust loop. Your friction patterns reinforce this — buggy code (213 instances) and wrong approach (179 instances) are your most common pain points, suggesting you frequently let Claude take a first pass, discover issues, and then redirect. For example, during your Lighthouse performance session, you iterated through responsive images, cache headers, and CSS refinements, hitting spacing friction along the way but pushing through to mostly achieve your goals. Similarly, your benchmark optimization session saw you try multiple approaches, revert two that caused regressions, and still land a +152% improvement in Patterns/sec.
Your primary stack is Go (1,437 tool uses) and CSS (785), pointing to a split between backend/systems work and frontend polish on what appears to be an Astro-based personal site or blog. The heavy use of Bash (4,003), Read (3,224), and Edit (3,045) tools shows you lean on Claude as a hands-on coding partner rather than just an advisor — you want it reading files, making edits, and running commands directly. The 522 TaskCreate and 957 TaskUpdate calls suggest you're also leveraging sub-task orchestration for complex multi-step work. Your satisfaction skews positive (548 likely satisfied + 370 satisfied + 133 happy vs. 196 dissatisfied), but the relatively low "fully achieved" rate (only 4 out of 176 measured outcomes) combined with high "mostly achieved" (133) reveals that you tolerate imperfection and keep moving rather than demanding completeness in a single session. You tend to let Claude run with tasks but aren't afraid to interrupt and correct — as seen when you caught copy/paste errors in auto-generated captions that Claude had propagated across 11 MDX files, or when you flagged that a negative-margin CSS fix was hiding a legend item.
Key pattern: You are a rapid-iteration power user who treats Claude as a hands-on coding partner, shipping constantly through tight feedback loops and tolerating partial results rather than demanding perfection upfront.
| Bucket | Count |
|---|---|
| 2-10s | 469 |
| 10-30s | 1,162 |
| 30s-1m | 991 |
| 1-2m | 256 |
| 2-5m | 152 |
| 5-15m | 442 |
| >15m | 48 |
Median: 35.9s · Average: 122.8s
| Overlap Events | Sessions Involved | % of Messages |
|---|---|---|
| 2 | 4 | 1% |
You run multiple Claude Code sessions simultaneously. Multi-clauding is detected when sessions overlap in time, suggesting parallel workflows.
| Period | Count |
|---|---|
| Morning (6-12) | 1,866 |
| Afternoon (12-18) | 2,017 |
| Evening (18-24) | 0 |
| Night (0-6) | 0 |
| Error Type | Count |
|---|---|
| Command Failed | 56 |
| User Rejected | 39 |
| Other | 29 |
| File Changed | 20 |
| File Not Found | 5 |
| Edit Failed | 4 |
You're a power user running 236 sessions over a month, splitting your time between a Go performance project and an Astro site with deep frontend polish.
You drove a rigorous optimization cycle on your Go codebase, achieving a remarkable +152% improvement in Patterns/sec and 64% memory reduction through iterative changes guided by benchmark results. You weren't afraid to revert approaches that caused regressions, showing disciplined engineering judgment in collaboration with Claude.
You used Claude to tackle Lighthouse performance improvements across your Astro site—responsive images, cache headers, inline CSS—while simultaneously iterating on UI refinements. This end-to-end approach of combining infrastructure-level optimizations with pixel-perfect CSS adjustments shows you're treating site quality holistically rather than in silos.
With 522 TaskCreate and 957 TaskUpdate calls, you're heavily leveraging Claude's task system to manage complex, multi-step workflows across your projects. Combined with over 4,000 Bash invocations and 3,000+ file reads, you've built a workflow where Claude functions as a persistent engineering partner executing substantial multi-file changes across both Go and frontend codebases.
| Capability | Count |
|---|---|
| Multi-file Changes | 104 |
| Correct Code Edits | 50 |
| Good Explanations | 22 |
| Outcome | Count |
|---|---|
| Partially Achieved | 39 |
| Mostly Achieved | 133 |
| Fully Achieved | 4 |
Your sessions frequently involve multi-step iterative corrections where Claude's initial approach introduces bugs or incomplete solutions, driving up your friction cost despite eventually reaching good outcomes.
When you ask Claude to make changes across many files, it tends to carry forward mistakes rather than validating each file individually. You could reduce this by asking Claude to verify its changes against source data (like EXIF metadata) before committing, or by processing files in smaller batches with a review step.
- Claude updated 11 MDX files with auto-generated captions but propagated copy/paste errors in alt text across multiple files, requiring you to manually spot and flag each mismatch
- With 213 instances of buggy code friction across your sessions, bulk operations appear to be a recurring source of errors that compound when applied at scale
Your CSS and styling changes frequently trigger unintended side effects that take multiple rounds to resolve, likely because Claude applies fixes without fully reasoning about layout context. You could mitigate this by asking Claude to enumerate potential side effects before applying CSS changes, especially with techniques like negative margins.
- A negative margin CSS fix for chart spacing ended up hiding the Rust legend item, requiring multiple correction iterations before the layout was right
- Lighthouse performance work involved inline CSS and spacing adjustments that created friction, and CSS is your most-used language at 785 tool interactions—suggesting a disproportionate amount of time spent wrestling with styling
When you tackle infrastructure tasks like deployments or performance optimizations, Claude's initial approach often misses critical configuration details, leaving you with broken states that require backtracking. You could improve this by asking Claude to produce a pre-flight checklist (e.g., image asset handling, cache invalidation) before executing deployment or optimization steps.
- Deploying your Astro site to Cloudflare Pages appeared successful but images were broken, indicating Claude didn't account for how static assets would be served in the new environment
- Two stack-conversion optimization attempts for benchmark performance caused regressions and had to be reverted, wasting cycles on approaches that should have been validated with a smaller proof-of-concept first
| Type | Count |
|---|---|
| Buggy Code | 213 |
| Wrong Approach | 179 |
| Api Error | 46 |
| Level | Count |
|---|---|
| Dissatisfied | 196 |
| Likely Satisfied | 548 |
| Satisfied | 370 |
| Happy | 133 |
Content Editing:
When modifying content across multiple files (e.g., MDX posts, templates), always diff each file individually against the original to verify no copy/paste errors were introduced. Never reuse text from one file's content (alt text, captions, titles) in another file.
Why: Sessions showed copy/paste errors propagating across 11 MDX files when batch-editing captions, requiring manual user correction.
CSS / Styling:
After any CSS layout change, verify that no existing elements are visually hidden or clipped. Check for overflow:hidden, negative margins, and z-index issues that could hide sibling elements.
Why: A negative margin fix for chart spacing ended up hiding the Rust legend item, requiring multiple iterations to resolve — a recurring friction pattern with CSS changes.
Deployment:
For deployment tasks, always verify asset pipelines end-to-end: confirm images, fonts, and other static assets resolve correctly in the deployed environment, not just locally. Check build output paths and any CDN/rewrite rules.
Why: Cloudflare Pages deployment succeeded but images were broken, indicating the deployment guidance didn't account for the full asset pipeline.
Performance Optimization:
When optimizing performance, benchmark before AND after each individual change. If a change causes a regression, revert it immediately before trying the next optimization. Never stack multiple untested optimizations.
Why: Two stacked optimization attempts (iterative stack conversions) caused regressions and had to be reverted, wasting significant iteration time.
Project Overview:
This project uses Go as the primary language and Astro (with CSS, YAML, MDX) for the website. Always prefer idiomatic patterns for each.
Why: Language distribution (Go: 1437, CSS: 785, YAML: 162) shows these are the dominant languages but no CLAUDE.md guidance exists for language conventions.
Create reusable prompt workflows triggered by a single /command.
Why for you: Your #1 goal is 'commit_and_push' (435 times!) — a custom /commit skill would standardize your commit message format, run checks, and push in one command instead of repeating instructions every session.
mkdir -p .claude/skills/commit && cat > .claude/skills/commit/SKILL.md << 'EOF'
# Commit and Push Skill
1. Run `go build ./...` and `go test ./...` to verify no regressions
2. Stage all changes with `git add -A`
3. Generate a concise commit message summarizing the changes
4. Commit and push to the current branch
5. If push fails, pull --rebase and retry
EOFAuto-run shell commands at specific lifecycle events (e.g., after edits).
Why for you: You have high friction from buggy code (213 instances) and wrong approaches (179). A post-edit hook running go vet and go build would catch errors immediately instead of discovering them later in the session.
// Add to .claude/settings.json:
{
"hooks": {
"postToolUse": [
{
"matcher": "Edit|Write",
"command": "if ls *.go **/*.go 2>/dev/null | head -1 > /dev/null; then go vet ./... 2>&1 | head -20; fi"
}
]
}
}Claude spawns focused sub-agents for parallel exploration work.
Why for you: You already use TaskCreate (522) and TaskUpdate (957) heavily, showing complex multi-step work. Explicitly asking for sub-agents when doing batch operations (like editing 11 MDX files) would let Claude verify each file independently, catching copy/paste errors before they propagate.
Try prompting: "Use a sub-agent to verify each MDX file's frontmatter and alt text matches its actual image content. Check them independently, don't copy between files."
When editing content across many files, ask Claude to verify each file individually against source data rather than applying templates.
Your EXIF caption session modified 11 MDX files but inherited copy/paste errors because content was applied in a batch pattern. With 36 Markdown files in your workflow, this will recur. Ask Claude to read each source image's actual EXIF data and verify the generated caption matches before moving to the next file. This turns a batch operation into a verified pipeline.
Paste into Claude Code:
For each MDX file in content/photos/, read the corresponding image's EXIF data, generate the caption from THAT specific image's data, and verify the alt text matches the actual image content before moving to the next file. Do not reuse text between files.
Structure performance work as a strict measure-change-measure cycle to avoid stacked regressions.
Your Go performance optimization session achieved +152% Patterns/sec but had two reverted attempts along the way. With 133 performance optimization goals, this is a core workflow. Establishing a rigid protocol — baseline benchmark, single change, re-benchmark, decide — prevents compounding regressions and makes each optimization's impact clear. This also applies to your Lighthouse work on the Astro site.
Paste into Claude Code:
I want to optimize performance. Follow this protocol strictly: 1) Run the benchmark and record baseline numbers, 2) Make ONE optimization change, 3) Re-run the exact same benchmark, 4) If regression, revert immediately, 5) If improvement, commit and record the delta, 6) Repeat. Never stack multiple untested changes.
Your most frequent goal (435 times) is committing and pushing — automate it with a skill or alias.
Nearly half your tracked goals are commit_and_push, which suggests you're spending significant interaction time on a routine operation. Between the Custom Skill suggestion above and adding a CLAUDE.md instruction for your preferred commit style, you could reduce this to a single /commit command. Consider also whether some of these commits could be batched — 482 commits across 236 sessions is about 2 per session.
Paste into Claude Code:
From now on, when I say 'commit' or 'ship it', run tests first, then stage all changes, write a concise conventional commit message (feat/fix/refactor prefix), commit, and push. Don't ask for confirmation unless tests fail.
Your data shows a power user spending 2,000+ hours across Go performance work and Astro site refinement—but with 392 friction events and only 2% of outcomes fully achieved, there's massive room for autonomous agents to close the loop on verification and iteration.
Your Cloudflare deployment broke on images, and Lighthouse improvements needed iterative CSS fixes—both are perfect candidates for an agent that deploys, runs automated checks, and self-corrects. Imagine Claude building your site, deploying to a preview URL, running Lighthouse and broken-link checks, then fixing issues in a loop until all scores pass and zero 404s remain. This eliminates the entire class of 'deployed but broken' friction you experienced.
Getting started: Use Claude Code with a CLAUDE.md that defines your deploy pipeline and acceptance criteria, and let it iterate autonomously using Bash to run builds, deploy previews, and curl-check assets.
Paste into Claude Code
I want you to act as an autonomous deploy-and-verify agent for my Astro site on Cloudflare Pages. Do the following in a loop until everything passes:
1. Run `npm run build` and fix any build errors
2. Deploy to a Cloudflare Pages preview branch using `wrangler pages deploy`
3. Crawl every page of the preview URL and check for broken images, missing assets, and 404s
4. Run Lighthouse CI against the preview URL targeting performance > 95, accessibility > 95
5. If any check fails, diagnose the root cause, apply a fix, and restart from step 1
6. After 3 consecutive passes with no issues, create a commit with a summary of all changes made
Report each iteration's results before proceeding. Never skip verification steps.
Your benchmark session achieved +152% patterns/sec but events/sec stayed flat and two optimization attempts regressed and were reverted—that's exactly the pattern where test-gated autonomous iteration shines. Claude can spawn parallel agent tasks that each try a different optimization strategy, run benchmarks as the acceptance gate, and only keep changes that measurably improve performance without regressions. Instead of serial trial-and-error, you get a breadth-first search across optimization space.
Getting started: Use Claude Code's TaskCreate/TaskUpdate tools (you're already using them heavily at 1,479 calls) to orchestrate parallel optimization branches, each gated by go test -bench results compared against a saved baseline.
Paste into Claude Code
I want to optimize the performance of my Go project using a parallel, test-gated approach. Here's the plan:
1. First, run the full benchmark suite (`go test -bench=. -benchmem ./...`) and save the results as the baseline
2. Identify the 4-5 hottest functions using `go tool pprof` on a CPU profile
3. For each hot function, create a separate task that:
a. Tries an optimization strategy (e.g., reduce allocations, use sync.Pool, avoid interface conversions, pre-allocate slices, inline hot paths)
b. Runs the benchmark suite after the change
c. Compares against baseline: KEEP only if target bench improves >=5% AND no other bench regresses >2%
d. If it regresses, revert immediately and document why
4. After all tasks complete, combine the winning optimizations, run the full suite one final time, and create a commit with before/after numbers in the message
Be rigorous: every change must be validated by benchmarks. No speculative keeps.
Your EXIF-to-caption session hit copy/paste error propagation across 11 MDX files—a classic bulk-transform failure mode where one bad assumption corrupts everything downstream. An autonomous agent can apply transforms file-by-file, validate each result against source-of-truth data (like actual EXIF metadata or image filenames), and flag mismatches before committing. This turns error-prone bulk edits into verified, auditable pipelines.
Getting started: Structure the task so Claude reads ground-truth data first (EXIF via exiftool, filenames, existing frontmatter), builds a validation map, then applies and cross-checks each transform individually before batching the commit.
Paste into Claude Code
I need you to do a bulk content transformation across my MDX blog posts, but with strict per-file validation to prevent error propagation. Follow this exact process:
1. DISCOVERY: Find all MDX files with images. For each image reference, run `exiftool` to extract the actual EXIF metadata (caption, camera, date, GPS) and store it in a validation map keyed by filename.
2. TRANSFORM: For each MDX file, one at a time:
a. Read the current file content
b. Apply the transformation (update alt text, captions, or frontmatter)
c. Cross-check every generated caption/alt against the validation map
d. If any field doesn't match the source EXIF data, flag it and DO NOT write the file
e. If validated, write the file and log: filename, what changed, validation status
3. AUDIT: After all files are processed, print a summary table showing: files modified, files skipped with reasons, and any mismatches found
4. Only after I review the audit summary, create a single commit with all validated changes
Never copy text from one file to use in another—always derive from the EXIF source data.
"Claude tried to fix chart spacing with negative margins and accidentally deleted Rust from existence"
While making CSS tweaks to chart layouts on an Astro site, a negative margin approach ended up hiding the Rust legend item entirely — requiring multiple rounds of fixes. Fitting, perhaps, for a project that's 99.7% not Rust (4 lines out of thousands).