621 messages across 45 sessions (119 total) | 2026-03-24 to 2026-05-04
At a Glance
What's working: You treat Claude as a genuine collaborator on substantial, multi-step work — full-cycle data investigations (PAL uniqueness checks, composite PK migrations, parallel SQL benchmark scripts) and iterative visual refinement on visx charts where you push back on log-scale choices and label positioning until the output matches your vision. The MECAT performance tracking system you built — using Claude to design the schema, slash commands, and skills that then aggregate Slack/GitHub/Jira highlights — shows real investment in durable productivity infrastructure rather than one-off prompts. Impressive Things You Did →
What's hindering you: On Claude's side: it tends to over-edit prose, over-specify Jira tickets, and touch sections you didn't ask about (the Quick Start changes, the unwanted co-author attribution, marketing-toned Slack drafts), and it occasionally undersells key findings or misreads domain terms like 'milestones' or 'shoutouts' on the first pass. On your side: ambiguous terminology and missing context (rubric location, file paths, scope of edits) often send Claude in the wrong direction early, and external tool flakiness (Miro auth, missing MCP sources for standups) blocks tasks entirely when there's no fallback configured. Where Things Go Wrong →
Quick wins to try: Try wrapping your most repeated workflows — stop-slop edits, SQL verification file formatting, milestone chart specs — into Custom Skills so the conventions and constraints travel with the command instead of being re-explained each time. For visualization and migration work, ask Claude to propose a written spec (axes, ordering, scales, or migration steps and verification queries) before writing any code, so misalignment surfaces in seconds rather than after a build. Features to Try →
Ambitious workflows: As models improve, expect to hand off your PAL-style schema migrations to an agent that generates SQL, runs it against staging, executes its own integrity and uniqueness checks, invokes Codex review, and iterates on P1s before surfacing a PR. Your highlight collection system is a natural fit for parallel subagents — one per source (Slack, GitHub, Jira, Confluence) running concurrently with isolated retries — and your chart work could move to a visual test harness where the agent screenshots, self-critiques, and iterates on label collisions and scale choices before you ever look. On the Horizon →
621
Messages
+12,525/-854
Lines
73
Files
22
Days
28.2
Msgs/Day
What You Work On
Data Pipeline & SQL Backfill Engineering~12 sessions
Extensive work on PAL/STP/PQ milestone data backfill scripts, SQL verification queries, and data reconciliation investigations. Claude was used for SQL generation, debugging pandas dtype errors, validating composite primary keys, and producing developer handoff documents. Sessions involved iterative SQL refinement, CSV data cleaning, and parallel benchmark script creation.
Building and using a MECAT performance tracking system with automated skills to collect work highlights from Slack, GitHub, and Jira. Claude designed schemas, slash commands, and configs, then ran /collect and /gaps analyses across date ranges. Some friction occurred with JQL configuration, missing source locations, and rubric location confusion.
Data Visualization & Chart Development~6 sessions
Built visx-based chart components (replacing recharts) for SQL Server benchmark articles and milestone data dashboards. Claude created 6-8 chart components with iterative refinement around log scales, ordering, label positioning, and visual polish. Most charts passed builds successfully after user pushback on initial design choices.
Web App Migration & Deployment~5 sessions
Migrated an AWS/Okta app to Vercel with EWMT branding, fixed localhost auth errors, implemented URL namespace updates and graph dropdowns, and managed PRs through merge. Claude handled GitHub repo creation, branch management, and CI fixes, though encountered bundle size issues and missing data files during deploys.
Documentation & Content Authoring~10 sessions
Used Claude for Confluence page creation/updates, applying the stop-slop skill to clean prose, writing Slack announcements, building installation guides, and producing standup summaries. Work spanned ADF-formatted edits, README improvements, and architecture diagrams. Friction often involved tone matching, over-specification, and keeping docs generic versus project-specific.
What You Wanted
Visual Refinement
8
Sql Generation
7
Code Explanation
7
File Compression
4
Data Investigation
4
Data Collection
4
Top Tools Used
Bash
305
Read
240
Edit
233
Mcp Claude Ai Slack Slack Search Public And Private
72
Write
54
Mcp Claude Ai Slack Slack Read Channel
51
Languages
TypeScript
168
Python
146
Markdown
95
JSON
34
CSS
3
HTML
2
Session Types
Multi Task
23
Iterative Refinement
12
Single Task
5
Exploration
3
Quick Question
2
How You Use Claude Code
You work with Claude Code in a highly iterative, conversational style rather than handing off detailed upfront specs. Across 45 sessions and 621 messages, you tend to start with a concise ask, watch what Claude produces, and then steer with small course corrections—often interrupting when the direction drifts. This shows up clearly in the visual refinement work (your top goal at 8 sessions): you pushed back on log-scale chart choices, rejected a carousel tab design that lacked clear step sequencing, and made Claude rebuild mermaid diagrams multiple times until the style matched. You're not afraid to call work 'lazy' or interrupt twice when a search isn't converging (as with the elusive batch_size=3 setting), which keeps Claude from spiraling but also shows up in your friction stats: 15 'wrong approach' and 14 'misunderstood request' incidents, almost always caught early by you rather than after damage is done.
You lean heavily on Claude as an investigative and integration partner across tools—Bash, Read, and Edit dominate, but Slack search (72 calls), Confluence, Jira, and Miro MCPs feature prominently, suggesting you use Claude to stitch together work artifacts (standup summaries, MECAT performance tracking, two-week wins/setbacks reports) more than as a pure code generator. When tools fail (Miro permissions, missing MCPs for standups, Codex not installed), you pivot quickly rather than fight the stack. You also show strong taste-driven editing instincts: the stop-slop skill sessions, the Slack announcement you ended up largely rewriting yourself, and your repeated corrections of marketing-toned or over-specified prose all point to someone who knows exactly what they want the voice and density to be, and uses Claude as a first draft engine.
You give Claude meaningful autonomy on mechanical work—merging PRs, running migrations, building parallel benchmark scripts—but you stay in the loop on judgment calls. Notable tells: you asked Claude to remove a co-author attribution it added unprompted, caught it calling out 'ewmt' specifically when you wanted generic placeholders, and corrected it when 'better shoutouts' was misread as finding more praise for you instead of recognizing teammates. The 17 fully-achieved and 22 mostly-achieved outcomes (vs. only 2 not-achieved) suggest your interrupt-and-redirect rhythm works well, even if it generates more friction events than a spec-heavy approach would.
Key pattern: You iterate in tight loops with strong opinions on taste, interrupting quickly to redirect rather than writing detailed specs upfront.
User Response Time Distribution
2-10s
33
10-30s
75
30s-1m
118
1-2m
83
2-5m
63
5-15m
61
>15m
40
Median: 64.1s • Average: 240.4s
Multi-Clauding (Parallel Sessions)
6
Overlap Events
8
Sessions Involved
4%
Of Messages
You run multiple Claude Code sessions simultaneously. Multi-clauding is detected when sessions
overlap in time, suggesting parallel workflows.
User Messages by Time of Day
Morning (6-12)
333
Afternoon (12-18)
286
Evening (18-24)
2
Night (0-6)
0
Tool Errors Encountered
Command Failed
26
Other
23
User Rejected
10
File Too Large
7
File Not Found
3
Edit Failed
2
Impressive Things You Did
Across 45 sessions spanning data engineering, technical writing, and tooling work, you've built a versatile workflow that combines deep SQL investigation with polished documentation and design output.
End-to-end data investigation
You consistently use Claude for full-cycle data work — verifying PAL uniqueness, designing composite primary keys, building parallel SQL benchmark scripts, and producing developer handoff documents. You catch issues fast (like pushing back on Claude's underselling of PAL datetime mismatches) and follow through to working migrations and validation queries.
Iterative design and visual refinement
You've shown strong taste in driving visual work, from replacing recharts with visx for an article to building three new STP/PAL/PQ milestone charts. You give precise feedback on log-scale choices, label positioning, and ordering until the output matches your vision, treating Claude as a design collaborator rather than a one-shot generator.
Building your own MECAT system
You designed a complete performance tracking system with automated collection skills, rich metadata schema, and custom slash commands like /collect and /gaps. This meta-workflow — using Claude to build tooling that then aggregates Slack, GitHub, and Jira highlights for you — shows sophisticated investment in long-term productivity infrastructure.
What Helped Most (Claude's Capabilities)
Multi-file Changes
11
Correct Code Edits
9
Good Explanations
8
Good Debugging
7
Fast/Accurate Search
5
Proactive Help
2
Outcomes
Not Achieved
2
Partially Achieved
3
Mostly Achieved
22
Fully Achieved
17
Unclear
1
Where Things Go Wrong
Your sessions show recurring friction from ambiguous initial requests, MCP/tool access failures, and Claude misjudging scope or tone on writing and code tasks.
External tool and MCP access failures
You frequently hit authentication and connectivity issues with external integrations (Miro, Confluence sources, missing MCPs), which blocks tasks entirely or forces workarounds. Verifying tool access upfront or having fallback paths configured would prevent these dead-ends.
Miro MCP access denied on two separate sessions, fully blocking the conversions service lookup (not_achieved outcome)
Standup summarization blocked because no MCP server was configured to reach the source location, leaving the task unfinished
Underspecified requests leading to wrong direction
Many sessions show Claude misinterpreting your intent on the first pass because key context (file location, terminology, scope) wasn't stated, leading to wasted effort and interruptions. Front-loading specifics like file paths, target repos, and your definition of domain terms would cut down rework.
Claude searched Confluence for the MECAT rubric when it was a local .md file, requiring you to interrupt and redirect
Claude misinterpreted 'milestones' as row checkpoints rather than STP/PAL/PQ groupings, causing multiple interruptions and chart rebuilds
Overreach on writing and code edits
Claude tends to over-edit prose, over-specify Jira tickets, or touch sections you didn't ask about, forcing you to push back on tone, scope, or unwanted changes. Tightening instructions with explicit 'don't change X' or 'match this brevity' constraints, and asking Claude to propose diffs before applying, would reduce these reversals.
Claude made unwanted changes to the Quick Start section and hardcoded 'ewmt' where you wanted generic placeholders, requiring two interruptions
Claude destroyed your text-annotated GIF version mid-session and you had to recover from a backup file
Primary Friction Types
Wrong Approach
15
Misunderstood Request
14
User Rejected Action
10
Buggy Code
9
Excessive Changes
5
Api Error
2
Inferred Satisfaction (model-estimated)
Dissatisfied
25
Likely Satisfied
129
Satisfied
11
Existing CC Features to Try
Suggested CLAUDE.md Additions
Just copy this into Claude Code to add it to your CLAUDE.md.
Multiple sessions show the user pushing back on marketing tone, over-specification, and Claude calling out specific names like 'ewmt' instead of using generic placeholders.
The user explicitly wished Claude had created a branch upfront, asked for co-author attribution to be removed, and a linter silently reverted a SecureRoute change post-commit.
Multiple SQL sessions show recurring confusion about PAL column names, what 'milestones' means in this codebase, and verification script formatting expectations.
Claude wasted time searching Confluence for the local MECAT rubric, and once destroyed a user's text-annotated video mid-session requiring recovery from backup.
Just copy this into Claude Code and it'll set it up for you.
Custom Skills
Reusable markdown prompts triggered with /command
Why for you: You already use /collect, /gaps, and stop-slop skills heavily. Adding /commit (with branch creation + no co-author rules), /sql-verify (with your block structure), and /confluence-publish would lock in conventions you keep re-explaining across sessions.
mkdir -p .claude/skills/commit && cat > .claude/skills/commit/SKILL.md <<'EOF'
# Commit Skill
1. Create a new branch if on main: `git checkout -b <descriptive-name>`
2. Stage and commit changes
3. Do NOT add Claude co-author trailer
4. Push branch and open PR with gh CLI
5. After merge, verify linter didn't revert auth/SecureRoute changes
EOF
Hooks
Shell commands that auto-run at lifecycle events
Why for you: You had a linter silently revert a SecureRoute commit and pandas dtype bugs that recurred. A PostToolUse hook running your linter/type-check after edits would surface these immediately instead of after a PR review.
Connect Claude to external tools via Model Context Protocol
Why for you: You repeatedly hit Miro permission failures and couldn't find standup posts because no MCP was configured for them. A Jira MCP would also save the wasted highlight-collection runs caused by JQL misconfiguration.
claude mcp add jira -- npx -y @atlassian/mcp-jira
claude mcp add miro -- npx -y @miro/mcp-server
# verify: claude mcp list
New Ways to Use Claude Code
Just copy this into Claude Code and it'll walk you through it.
Front-load context for ambiguous terms
When asking about domain concepts (milestones, shoutouts, rubrics), state the meaning upfront to avoid early misinterpretation.
Several sessions burned cycles because Claude defaulted to a generic interpretation: 'milestones' as row checkpoints instead of STP/PAL/PQ groupings, 'shoutouts' as praise received instead of given, MECAT rubric as a Confluence page instead of a local file. A single sentence of grounding at the start prevents 2-3 rounds of correction.
Paste into Claude Code:
Before we start: in this codebase, 'milestones' = STP/PAL/PQ groupings, the MECAT rubric is at ./docs/mecat-rubric.md, and 'shoutouts' means recognizing teammates' work (not praise I received). Now, here's what I want to do: ...
Use Task Agents for codebase archaeology
When asking Claude to find historical settings or trace where something was introduced, request a dedicated agent to avoid back-and-forth.
The batch_size=3 search ran for many turns with two user interrupts before being abandoned, and the 'python uploader before BCP' investigation required walking git history. Spawning a focused sub-agent for these git/grep deep-dives keeps your main conversation clean and gives the agent a clear bounded task.
Paste into Claude Code:
Use a task agent to search git history and the current codebase for any reference to batch_size=3 in backfill concurrency. Report back where it was set, when it was removed, and the commit that did so.
Plan visuals before building them
For chart and diagram work, ask Claude to propose the spec (axes, ordering, scales, label positions) before writing code.
Your visx and mermaid sessions had recurring rework: misjudged log scale, wrong row ordering, mismatched label positions, mermaid \n splitting nodes. A pre-build spec review catches these in seconds instead of after a render cycle. This is especially valuable given the 8 visual_refinement goals in your top goals.
Paste into Claude Code:
Before writing chart code, give me a spec table with: chart name, x-axis field, y-axis field + scale (linear/log + why), sort order, label position strategy, and color choices. I'll approve or adjust before you build.
On the Horizon
Your workflow spans deep data engineering, SQL migrations, visualization work, and multi-source content aggregation—prime territory for autonomous, parallel agent orchestration that goes beyond single-task assistance.
Autonomous SQL Migration with Self-Verification
Imagine an agent that takes a schema change request (like your PAL composite PK migration) and autonomously generates the migration SQL, runs it against a staging DB, validates uniqueness/integrity with multiple verification queries, runs Codex review, and iterates on P1 issues until clean—all before showing you a PR. The agent could handle backfill scripts, dtype normalization, and edge cases like the STP_FALLBACK datetime equivalence checks without manual back-and-forth.
Getting started: Use Claude Code with subagents and a database MCP server (Postgres/MySQL MCP) plus a custom verification skill that runs Codex CLI in a loop until issues are resolved.
Paste into Claude Code:
I want you to act as an autonomous SQL migration agent. Given a schema change goal, you will: (1) analyze the current schema and data via the DB MCP, (2) generate the migration SQL with rollback, (3) run it against staging, (4) generate and execute at least 5 verification queries (uniqueness, row counts, dtype checks, referential integrity, sample diffs), (5) run Codex CLI review, (6) iterate on any P1/P2 issues autonomously until clean, (7) produce a final PR with migration, verification results, and a developer handoff doc. Start by asking me only for the goal and the staging connection. Today's task: migrate PAL table to a composite PK of (opportunity_id, milestone, event_timestamp) and validate STP_FALLBACK datetime equivalence in the same run.
Parallel Multi-Source Highlight Collection
Spawn parallel subagents—one each for Slack, GitHub, Jira, and Confluence—that simultaneously pull a date range's activity, then a synthesizer agent merges into a MECAT-mapped report, standup bullets, and a teammate-shoutout section. This eliminates the wasted-run, wrong-year, and mid-session-timeout problems by isolating failures per source and retrying independently while you keep working.
Getting started: Use Claude Code's Task tool to launch parallel subagents with the Slack, GitHub, Jira, and Confluence MCP servers, each writing structured JSON to a shared collection directory, then a final synthesis pass.
Paste into Claude Code:
Launch 4 parallel subagents to collect my work activity for the date range I specify. Agent 1: Slack (search my messages and threads I participated in across all channels). Agent 2: GitHub (PRs authored, reviewed, commits, issue comments). Agent 3: Jira (tickets I moved, commented on, or was assigned). Agent 4: Confluence (pages created/edited). Each agent writes structured JSON to .highlights/<source>.json with timestamps, links, and content. After all complete, run a synthesis agent that produces: (a) MECAT-rubric-mapped performance notes, (b) standup-ready bullets, (c) teammate shoutouts based on collaboration signals. If any source fails, retry that agent independently up to 2x without blocking others.
Test-Driven Visualization Iteration Loop
An agent that builds visx/recharts components against a visual test harness—rendering each chart variant, screenshotting it, evaluating against criteria (axis correctness, log vs linear appropriateness, label collision, ordering), and iterating autonomously until visual tests pass. No more rounds of 'fix the label positioning' or 'log scale was wrong'—the agent self-critiques renders before showing you.
Getting started: Combine Claude Code with Playwright MCP for screenshot capture, a vision-enabled evaluation step, and a test harness that renders each chart in isolation.
Paste into Claude Code:
Build an autonomous chart iteration workflow for my visx components. For each chart I request: (1) implement the component, (2) render it in an isolated Playwright page with the actual data, (3) screenshot it, (4) self-evaluate the screenshot against these criteria: appropriate scale (linear vs log based on data range), no label collisions, correct ordering, axis labels readable, total/boundary lines positioned correctly relative to data, color contrast adequate. (5) If any criterion fails, fix and re-render. Repeat up to 5 times per chart. Only show me the chart when self-evaluation passes, with the screenshot and criteria checklist. Start with the SQL Server benchmark dataset in /data and build 6 charts covering throughput, latency distribution, and error rates.
"Claude destroyed the user's text-annotated GIF mid-session and had to recover it from a backup file"
During an MP4-to-GIF conversion task with shrinking size limits, Claude overwrote the user's carefully annotated version, requiring an emergency backup recovery to undo the damage.