Claude Code Insights

522 messages across 76 sessions (114 total) | 2026-01-15 to 2026-02-25

At a Glance

What's working: You've built an impressively mature workflow around multi-agent orchestration — delegating sub-tasks, coordinating scoring rounds, and managing complex multi-file changes across your GEODE project and eval pipeline. Your habit of verifying documentation and resume claims against actual code is a standout practice that catches real errors (like mismatched worker counts) before they ship. Impressive Things You Did →

What's hindering you: On Claude's side, it too often takes the wrong approach — fabricating features, proposing changes you explicitly ruled out, or misunderstanding scope — which forces you into corrective steering that burns turns and patience. On your side, your longest and most ambitious sessions consistently blow past the context window, causing complete failures and lost work; the mega-session pattern is your single biggest source of wasted effort. Where Things Go Wrong →

Quick wins to try: Your 30+ automated scoring sessions are still run interactively — try headless mode to run them non-interactively from a script, which also sidesteps context overflow since each run starts fresh. You've already started creating skill files for agent onboarding; lean into custom slash-command skills for your repeating workflows like portfolio deployment checks or eval pipeline lint-fix loops. Features to Try →

Ambitious workflows: As models get better at sustained autonomous loops, your scoring pipeline can become a single command that fans out parallel agents per module, each within its own bounded context, aggregating results and auto-committing a report — no more sub-agent notification floods crashing your session. Your resume-and-portfolio sync workflow is also ripe for full automation: one pass that scans your repos for ground-truth metrics, diffs every claim across your HTML and markdown files, applies corrections, and deploys without you ever seeing a wrong date again. On the Horizon →

522

Messages

+35,560/-1,673

Lines

254

Files

Days

18.6

Msgs/Day

What You Work On

GEODE Project Development ~12 sessions

Large-scale development of the GEODE project including NLRouter implementation, LLM commentary in REPL, Korean natural language input handling, monitoring dashboard visualization, pipeline metrics tuning, status spinner UI, and data labeling/porting. Claude Code was used for multi-file implementation, regression testing, iterative bug fixing, and multi-agent orchestrated builds, though context window limits were a recurring friction point in longer sessions.

Eval Pipeline & Scoring Infrastructure ~14 sessions

Building and refining an evaluation pipeline including PR creation, CI lint fixes, async refactoring, Phase 3 planning, code quality scoring runs, and rubric compliance verification. Claude Code handled code generation, multi-file changes, TODO auditing, and documentation generation, though sessions sometimes stalled due to context overflow from sub-agent notifications and Claude needing user prodding to proceed on identified work.

Portfolio & Resume Tailoring ~14 sessions

Creating and iterating on portfolio HTML pages, tailored resumes, and cover letters for specific job applications (Toss, Nexon, Musinsa), including cross-document consistency checks, date corrections, content accuracy verification against codebases, and deployment via git push. Claude Code was used extensively for precise HTML edits, multi-file content corrections, blog-sourced accuracy validation, and salary negotiation research.

DevOps, Infrastructure & Load Testing ~8 sessions

Setting up k6 load test scripts, debugging 404/race condition issues in polling scripts, tearing down K8s/AWS infrastructure via Terraform, fixing bulk upsert duplicate key errors, and handling CI/CD pipeline failures. Claude Code was leveraged for diagnosing production bugs, creating test configurations, executing multi-round terraform destroy operations, and verifying resource cleanup despite external dependency complications.

Documentation, Presentations & Knowledge Management ~10 sessions

Generating README updates with documentation-code consistency checks, creating LangSmith observability reports, building PowerPoint appendix slides, writing architecture comparison documents, fetching and summarizing blog posts into knowledge base entries, and producing Korean translations of reports. Claude Code was used for multi-file documentation generation, ASCII visualization creation, and iterative refinement based on user style preferences.

What You Wanted

Automated Scoring Runs

Git Operations

Information Retrieval

Bug Fix

Score Code Quality

Code Generation

Top Tools Used

Bash

1174

Read

655

Edit

452

Write

148

Grep

146

Glob

118

Languages

Python

613

Markdown

354

HTML

167

TypeScript

JSON

YAML

Session Types

Multi Task

Iterative Refinement

Single Task

Exploration

Quick Question

How You Use Claude Code

You are a highly prolific orchestrator who uses Claude Code as a workhorse across a remarkably diverse set of tasks — from infrastructure teardown and CI pipeline management to resume tailoring, portfolio HTML tweaks, and competitive programming reviews. Across 76 sessions and 522 messages, your dominant pattern is automated scoring runs (30 sessions), where you kick off multi-agent builds and evaluation pipelines, letting Claude handle large-scale file operations (1,174 Bash calls, 655 Reads, 452 Edits). You lean heavily on the Task/TaskUpdate tools (171 combined uses), indicating you've adopted a sub-agent delegation style where you set Claude loose on complex multi-file work and check in on progress. However, this approach has a clear cost: context window overflow and prompt-too-long errors account for your most persistent friction (24+ incidents), and at least two sessions were completely derailed by it. When Claude goes off-track — which happens notably often with wrong_approach (19 times) and misunderstood_request (11 times) — you tend to correct course directly rather than abandon the task, as seen when you had to twice ask 'why haven't you started yet?' after Claude identified work but didn't proceed.

Your interaction style is iterative and hands-on with high expectations. You don't front-load detailed specifications; instead, you give a goal, let Claude execute, then course-correct with specific feedback — like flagging that slide designs were lower quality than previous work, or catching an incorrect year (2024 vs 2025) that Claude used. You're comfortable working across Python (613 files), HTML (167), and Markdown (354), often in the same session, jumping between code generation, documentation updates, and deployment. Your 65 commits across the period show you're shipping continuously, roughly one commit per session. You're notably versatile in your use cases — one session you're debugging k6 load test 404 errors, the next you're getting salary negotiation advice for Nexon, and the next you're building a Korean NL router with regression test suites. Despite frequent friction (wrong approaches, API errors, context limits), your satisfaction skews heavily positive (150 likely satisfied vs. only 1 frustrated), suggesting you've developed a pragmatic tolerance for Claude's limitations and know how to work around them effectively.

Key pattern: You operate as a high-throughput project orchestrator who delegates ambitious multi-file tasks to Claude, iterates rapidly on output quality through direct correction, and maintains momentum across diverse workstreams despite frequent context window and approach-mismatch friction.

User Response Time Distribution

2-10s

10-30s

30s-1m

1-2m

2-5m

5-15m

>15m

Median: 116.9s • Average: 402.1s

Multi-Clauding (Parallel Sessions)

Overlap Events

Sessions Involved

11%

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)

137

Afternoon (12-18)

177

Evening (18-24)

102

Night (0-6)

106

Tool Errors Encountered

Command Failed

111

Other

User Rejected

File Too Large

File Not Found

File Changed

Impressive Things You Did

Over 76 sessions spanning six weeks, you've built an impressive workflow around automated scoring pipelines, portfolio management, and complex multi-agent systems using Claude Code as a core development partner.

Multi-Agent Orchestration at Scale

You've developed a sophisticated approach to orchestrating large multi-agent builds, particularly with your GEODE project, where you coordinate implementation, linting, and quality scoring rounds through Claude Code. Your use of the Task/TaskUpdate tools (171 combined invocations) shows you're leveraging sub-agent delegation to manage complexity across sprawling codebases — a genuinely advanced usage pattern that most users never reach.

Cross-Document Consistency Verification

You consistently use Claude Code to verify claims across documents against your actual codebase, catching discrepancies like mismatched worker counts and ensuring portfolio descriptions, README files, and resumes all reflect ground truth from the code. This rigorous documentation-code alignment workflow, especially for job applications and portfolio deployment, demonstrates a high standard for accuracy that pays dividends in credibility.

Iterative Pipeline Development with CI

Your eval pipeline work shows a mature development loop — you move from planning through implementation, commit, PR creation, CI failure diagnosis, and lint fixes all within Claude Code sessions. You've produced 65 commits across this period with a strong mostly/fully achieved rate, and you effectively use Bash (1,174 calls) to drive git operations, test execution, and deployment verification as part of a tightly integrated development cycle.

What Helped Most (Claude's Capabilities)

Multi-file Changes

Good Explanations

Good Debugging

Proactive Help

Correct Code Edits

Fast/Accurate Search

Outcomes

Not Achieved

Partially Achieved

Mostly Achieved

Fully Achieved

Unclear

Where Things Go Wrong

Your sessions are frequently derailed by context window overflows from long-running orchestration tasks, Claude taking wrong approaches that require correction, and external API instability disrupting critical workflows.

Context Window Overflow in Long Sessions

You regularly hit context window limits during complex, multi-agent orchestration and long iterative sessions, which has caused complete session failures and lost progress. Breaking large tasks into smaller, scoped sessions and using file-based checkpointing between sessions would help you avoid these dead ends.

A session focused on expert review of an eval pipeline plan was completely blocked by context window overflow from lengthy sub-agent task notifications, resulting in zero useful output despite the time invested.
Your attempt to explore LLM-based alternatives using a research paper failed entirely because the PDF content made the prompt too long, and even compaction attempts couldn't recover the session.

Wrong Approach or Misunderstood Intent Requiring Correction

Claude frequently took incorrect approaches—fabricating features, proposing unwanted system changes, or misunderstanding scope—forcing you to spend extra turns steering it back on track. Providing more explicit constraints upfront (e.g., 'do not modify the system architecture' or 'target the develop branch') could reduce these costly detours.

Claude fabricated information about /insights being a built-in feature and incorrectly claimed /tasks and TaskCreate persist across sessions, wasting your time until you provided evidence to correct it.
Claude proposed system architecture changes when you explicitly said the system wouldn't be modified, and initially misunderstood which PR branch to target and the scope of eval-only work, requiring multiple corrections.

API Errors and External Instability Disrupting Workflows

Anthropic API 500 errors and external service issues repeatedly interrupted your sessions at critical moments, sometimes preventing task completion entirely. When you notice instability, consider deferring non-urgent work and saving progress frequently so you don't lose completed steps to mid-session failures.

Two separate API 500 errors interrupted your Toss resume creation workflow, preventing you from completing the page and forcing you to abandon the task mid-session.
Claude spent significant time investigating a 500 error that was just an Anthropic API outage instead of focusing on the product testing and feedback you actually requested, wasting most of the session on a non-issue.

Primary Friction Types

Wrong Approach

Misunderstood Request

Context Window Exceeded

Prompt Too Long

Buggy Code

User Rejected Action

Inferred Satisfaction (model-estimated)

Frustrated

Dissatisfied

Likely Satisfied

150

Satisfied

Existing CC Features to Try

Suggested CLAUDE.md Additions

Just copy this into Claude Code to add it to your CLAUDE.md.

When working with portfolio, resume, or cover letter documents, always verify claims (metrics, dates, worker counts, awards) against the actual codebase before finalizing. Never fabricate or assume numbers.

Multiple sessions had friction from incorrect claims (wrong worker counts, wrong year/dates, mismatched metrics) that required user correction after the fact.

When context window is getting large, proactively suggest compacting or splitting work into smaller sessions before hitting limits. Never wait until 'Prompt is too long' errors occur.

Context window overflow/exceeded was the single largest friction category (24+ instances across context_window_limit, context_window_exceeded, context_overflow, prompt_too_long), blocking entire sessions multiple times.

After identifying TODO items or next steps, proactively begin implementation immediately without waiting for explicit user instruction to proceed. Do not just list work — do the work.

User had to ask 'why haven't you started yet?' multiple times across sessions; Claude would identify tasks but passively wait instead of executing.

Primary languages for this workspace: Python, HTML, Markdown. When generating code, default to Python unless otherwise specified. For portfolio/resume work, use HTML. Documentation is in Markdown and Korean.

Python (613), HTML (167), and Markdown (354) dominate the codebase across 76 sessions — setting this default avoids ambiguity and wrong-language outputs.

When user references blog posts or external documentation as source of truth (SOT), match those sources exactly — do not add extra visualizations, reinterpret content, or deviate from the style/settings specified in the blog.

Multiple sessions had friction where Claude added unwanted visualizations, didn't match blog settings, or deviated from user-specified SOT documents, requiring rewrites.

When encountering API 500 errors or external service issues, note them briefly and continue with other tasks rather than spending time investigating the server issue itself.

Several sessions were disrupted by Claude investigating API/server 500 errors that were external incidents, wasting time instead of focusing on the actual user request.

Just copy this into Claude Code and it'll set it up for you.

Headless Mode

Run Claude non-interactively from scripts for batch automated tasks

Why for you: Your #1 goal is 'automated_scoring_runs' (30 sessions!). Running scoring pipelines, eval runs, and quality checks in headless mode would eliminate the need for interactive babysitting of repetitive scoring workflows.

claude -p "Run the scoring pipeline on all pending submissions and output results to scores/output.json" --allowedTools "Bash,Read,Write,Edit"

Custom Skills

Reusable slash-command prompts for repetitive workflows

Why for you: You already attempted a /insights skill and created agent onboarding skills. Formalizing your most common workflows — scoring runs, resume generation, portfolio deployment, and commit+push — as skills would save massive repetition across your 76 sessions.

mkdir -p .claude/skills/score && cat > .claude/skills/score/SKILL.md << 'EOF'
# Scoring Run
1. Read the scoring config from configs/eval_config.yaml
2. Run `python run_scoring.py` and capture output
3. Verify all metrics match SOT specs
4. Commit results with message "chore: scoring run $(date +%Y-%m-%d)"
5. If any metric deviates >5%, flag it before committing
EOF

Hooks

Auto-run shell commands at lifecycle events like before committing

Why for you: You had lint_issues friction and CI failures across multiple sessions. A pre-commit hook running linters automatically would catch these before they become PR blockers, and a post-edit hook could verify content accuracy patterns (like date formats).

# Add to .claude/settings.json:
{
  "hooks": {
    "PreCommit": [
      {
        "command": "python -m ruff check . --fix && python -m ruff format .",
        "description": "Auto-lint and format Python before commit"
      }
    ],
    "PostEdit": [
      {
        "command": "grep -rn '2024' portfolio/ resume/ | grep -v node_modules || true",
        "description": "Warn if old year references exist in portfolio/resume files"
      }
    ]
  }
}

New Ways to Use Claude Code

Just copy this into Claude Code and it'll walk you through it.

Break mega-sessions into focused chunks to avoid context overflow

Your biggest blocker is context window limits — split large multi-task sessions into smaller, scoped sessions.

You had 24+ context-related friction events including sessions that completely failed due to overflow. Your multi-agent GEODE builds, iterative portfolio edits, and scoring runs accumulate massive context. Start each session with a single focused goal, use file-based handoff (write state to a markdown file), and begin a fresh session for the next phase. This alone would have saved at least 4 completely blocked sessions.

Paste into Claude Code:

Before we start: I have multiple tasks. Let's do ONLY the first one this session. When done, write a handoff summary to .claude/handoff.md with completed work and next steps, then I'll start a new session.

Front-load verification for content documents

When creating or editing resumes, portfolios, and cover letters, always verify against source code first.

Across your resume/portfolio sessions, you repeatedly hit issues where Claude generated claims that didn't match the actual codebase — wrong worker counts, incorrect dates, missing awards, mismatched metrics. The fix sessions cost extra time. Having Claude do a verification pass BEFORE writing content, rather than after, would catch these issues proactively. You do this work frequently (resume tailoring for Toss, Nexon, Musinsa), so the time savings compound.

Paste into Claude Code:

I need to update my resume for [company]. BEFORE writing anything: 1) Read the relevant source code and extract exact metrics/counts/dates, 2) List all verified facts as bullet points for my approval, 3) Only then create the resume content using those verified facts.

Use automated scoring with headless mode instead of interactive sessions

Your 30 'automated_scoring_runs' sessions should actually be automated — run them in CI or via headless Claude.

Nearly half your sessions (30/66) are scoring runs, yet you're running them interactively. This is your biggest opportunity for time savings. Set up a headless pipeline that runs scoring, validates against SOT specs, and only surfaces results that need human attention. Combined with a custom /score skill for the occasional interactive run, you could reclaim dozens of hours. Your scoring pipeline is mature enough (65 commits, established eval config) to automate confidently.

Paste into Claude Code:

Help me create a shell script called run_scoring.sh that uses claude headless mode to: 1) run the scoring pipeline, 2) validate outputs against SOT specs, 3) write a summary report to scores/latest_report.md, 4) exit with non-zero if any metric deviates more than 5% from expected values.

On the Horizon

Your 76 sessions reveal a power user orchestrating complex multi-agent builds, automated scoring pipelines, and cross-repo workflows — but context window limits and passive execution patterns are throttling what should be fully autonomous operations.

Autonomous Scoring Pipeline with Parallel Agents

Your top goal — automated_scoring_runs (30 sessions) — is still heavily human-supervised, with friction from context overflow and sub-agent notification floods. Imagine spawning parallel Claude Code agents that each score a module independently, aggregate results, and auto-commit a summary report — all triggered by a single command. With Task/TaskUpdate already in your top tools (171 combined calls), you're halfway to a fully orchestrated fan-out pipeline that never hits context limits because each agent owns a bounded scope.

Getting started: Use Claude Code's sub-agent Task tool with explicit scope boundaries per agent, and write results to intermediate files rather than passing through conversation context. Combine with a shell orchestrator script that launches multiple `claude` processes in parallel.

Paste into Claude Code:

I need you to build an autonomous scoring orchestrator. Here's the plan:

1. Read the list of modules to score from `scoring/manifest.json`
2. For EACH module, spawn a sub-agent Task with these instructions: 'Score the module at {path} against the rubric in scoring/rubric.md. Write your score and rationale to scoring/results/{module_name}.json. Do not return the full file contents — only confirm completion.'
3. After ALL sub-tasks complete, read every file in scoring/results/, aggregate into scoring/summary.md with a ranked table and overall stats.
4. Git add, commit with message 'chore: automated scoring run [date]', and push.

Critical constraints:
- Each sub-agent must ONLY read its own module directory and the shared rubric — no cross-module context.
- If any sub-agent fails, log the error in scoring/results/{module_name}.error.json and continue with others.
- The summary must include pass/fail counts, average scores, and the bottom 3 modules needing attention.

Start by examining the current project structure and manifest, then execute the full pipeline.

Self-Healing CI with Test-Driven Iteration

You're spending significant sessions on CI lint failures, bug fixes, and multi-file changes (31 success events) that follow a predictable loop: run tests → read errors → edit code → repeat. Claude Code can own this entire cycle autonomously, iterating against your test suite until green, then submitting a PR with a changelog. Your 1,174 Bash calls show the muscle is there — the missing piece is a structured loop that doesn't require you to say 'why haven't you started yet?'

Getting started: Give Claude Code an explicit iteration budget (e.g., 'up to 10 fix cycles') and a concrete exit condition (all tests pass + lint clean). Use the `--allowedTools` flag to pre-approve Bash, Edit, and Read so it never stalls waiting for permission.

Paste into Claude Code:

You are in autonomous fix mode. Your goal: make ALL tests and linting pass with zero human intervention.

Process:
1. Run the full test suite: `python -m pytest tests/ -x --tb=short 2>&1 | head -100`
2. Run the linter: `ruff check . --output-format=concise 2>&1 | head -50`
3. If everything passes, create a git commit with message 'fix: resolve all test and lint failures' and open a PR to develop. Then STOP.
4. If there are failures, analyze the FIRST error, make the minimal targeted fix, and go back to step 1.

Rules:
- Maximum 10 fix cycles. If not resolved after 10, write a diagnostic report to `fix-report.md` listing remaining issues and your hypotheses.
- Never change test assertions unless the test itself is provably wrong (e.g., testing removed functionality).
- After each edit, briefly log what you changed and why in a running `fix-log.md`.
- If you encounter an import or dependency error, check pyproject.toml/requirements.txt before modifying code.

Start now. Run the tests.

Context-Aware Resume and Portfolio Auto-Refresh

You've spent 10+ sessions manually syncing resumes, cover letters, and portfolio HTML against your actual codebase — catching mismatched worker counts, wrong dates, and stale metrics each time. This entire workflow can be a single autonomous pass: Claude scans your repos for ground-truth metrics, diffs them against every claim in your resume/portfolio files, generates a correction report, applies fixes, and deploys. No more '425-day service period' errors slipping through because a year was wrong.

Getting started: Create a `docs/source-of-truth.yaml` that maps every quantitative claim to its codebase origin. Then let Claude Code run a verification sweep using Grep/Read against that manifest, auto-fixing any drift it finds.

Paste into Claude Code:

Run a full consistency audit and auto-fix across all my application documents.

Phase 1 — Build Source of Truth:
- Scan the codebase for quantitative facts: worker/thread counts (grep for pool, worker, thread configs), performance metrics (any benchmark results, load test outputs in k6/), service dates (git log --format='%ai' --diff-filter=A for first commits, deployment configs for end dates), tech stack (parse pyproject.toml, package.json, requirements.txt, Dockerfiles).
- Write findings to `docs/source-of-truth.yaml` in format: `claim_key: {value: X, source_file: path, evidence: 'line or config snippet'}`

Phase 2 — Audit Documents:
- Read every file in `resume/`, `portfolio/`, and `cover-letters/` directories.
- For each quantitative claim (numbers, dates, percentages, tech names), check against source-of-truth.yaml.
- Generate `docs/consistency-audit.md` with a table: | File | Line | Current Claim | Correct Value | Source | Status |

Phase 3 — Auto-Fix:
- For every MISMATCH found, edit the document file to use the correct value.
- For claims with NO source found, flag them as [UNVERIFIED] in the audit report but do NOT modify them.
- After all fixes, re-run the audit to confirm zero mismatches.

Phase 4 — Deploy:
- Git add all changed files, commit with message 'docs: auto-sync application materials with codebase truth [audit report attached]'
- Push to main.

Start with Phase 1 now.

"Claude confidently told the user that a /tasks CLI command exists and persists across sessions — it doesn't — and the user had to correct it"

When a user asked how to preserve work across session resets, Claude fabricated the existence of a TaskCreate/TaskList persistence feature, essentially gaslighting the user about its own capabilities until the user pushed back and Claude sheepishly corrected itself to recommend the actual file-based approach.