182,379 messages across 22545 sessions | 2025-12-26 to 2026-02-04
At a Glance
What's working: You've developed a powerful workflow around large-scale documentation generation, using Claude as a synthesis engine to turn scattered notes and audit materials into structured developer docs across dozens of files. You're also effectively using Claude as a codebase research tool—asking it to read across your Numero monorepo and produce architectural overviews, which shows a mature, search-driven approach to understanding complex systems. Impressive Things You Did →
What's hindering you: On Claude's side, document retrieval keeps misfiring—pulling from wrong collections and requiring multiple retries to find the right content, which burns time on what should be straightforward lookups. On your side, the ambitious parallel sub-agent sessions you kick off tend to get cut short before completing, meaning you frequently end up with partial results from large generation tasks that needed more structured scoping or checkpointing. Where Things Go Wrong →
Quick wins to try: Try setting up a CLAUDE.md file that maps out your project's doc structure and collection names so Claude stops guessing paths during retrieval. You could also create custom slash commands (Custom Skills) for your recurring doc-generation workflows—like a `/gendocs` command that includes your preferred output format, target directory structure, and sequential file-creation approach instead of spawning unchecked parallel agents. Features to Try →
Ambitious workflows: As models get more capable, you'll be able to wire Claude into your CI pipeline so that every merged PR automatically triggers documentation regeneration—detecting doc drift from code changes and updating architectural overviews and API references without you initiating a session. You should also prepare for self-correcting multi-agent pipelines where Claude monitors its own sub-tasks, retries on failure, and guarantees completion, turning those 'session cut short' outcomes into a thing of the past. On the Horizon →
182,379
Messages
+6,367,058/-1,340,644
Lines
79497
Files
41
Days
4448.3
Msgs/Day
What You Work On
Developer Documentation Generation~2 sessions
Large-scale creation of developer documentation directories from existing notes and audit materials, targeting 34+ files. Claude Code was used to launch parallel sub-agents to explore the codebase and generate structured documentation, though some sessions were cut short before full completion.
Monorepo Architecture Analysis~1 sessions
Architectural overview and synthesis of the Numero monorepo structure based on existing dev docs. Claude Code read through documentation files and produced a structured summary of the system's architecture, helping the user understand the overall codebase organization.
Claude Code Feature Research~1 sessions
Research into Claude Code platform features including session forking, CLAUDE_SESSION_ID, subagents, and hooks. Claude Code used doc search tools to retrieve and summarize information, though there was some friction with path-based retrieval pulling from incorrect collections.
Creation of comprehensive reference documents on specific technical topics such as sessions and subagents, followed by research-oriented follow-up questions. Claude Code was used to synthesize existing knowledge into well-structured reference materials.
The bulk of sessions involved active development primarily in TypeScript and Rust within the Numero monorepo, with significant multi-file changes across the codebase. Claude Code was heavily used for editing, bash operations, and task management, generating over 9,000 commits across the analysis period.
What You Wanted
Information Lookup
159
Documentation Search
39
Documentation Generation
27
Documentation Creation
9
Codebase Understanding
1
Top Tools Used
Bash
423154
Edit
143386
Read
132950
TodoWrite
34606
Write
33519
Task
31034
Languages
Markdown
121606
TypeScript
109580
JSON
31327
Rust
16723
Shell
6927
YAML
2896
Session Types
Exploration
39
Single Task
33
Multi Task
3
Quick Question
1
How You Use Claude Code
You are a documentation-driven power user operating Claude Code at an extraordinary scale — over 22,000 sessions, 182,000 messages, and 9,100 commits across roughly six weeks. Your primary interaction pattern revolves around large-scale knowledge synthesis and documentation generation, where you direct Claude to consume existing notes, audit materials, and codebase artifacts and transform them into structured reference documentation. You frequently launch ambitious, multi-file documentation creation tasks (like a 34-file developer documentation directory), leveraging Claude's parallel agent capabilities via the Task/TaskUpdate tools (over 60,000 combined uses) to orchestrate work across many files simultaneously. Your TodoWrite usage (34,600 invocations) suggests you plan extensively through structured task lists rather than giving one-off commands.
Your interaction style leans heavily toward delegation with high-level directives — you set a broad goal (e.g., "create a docs/development/ directory from existing notes") and let Claude run with it, rather than micromanaging individual steps. The evidence shows you're comfortable with sessions that involve massive Bash execution (423,000 calls) and file reads (133,000), indicating you trust Claude to explore and execute autonomously. However, your sessions frequently end before full completion (33 unclear outcomes), suggesting you either run parallel sessions and move on, or you're working at such scale that individual session completions matter less than aggregate throughput. The friction pattern — Claude pulling from wrong collections and needing retries — shows you encounter retrieval issues but push through them rather than stopping to debug, consistent with a high-velocity workflow.
Your top goals center almost entirely on information lookup and documentation (159 information lookups, 75 documentation-related tasks), with your primary languages being Markdown (122K tool uses) and TypeScript (110K). You're essentially using Claude Code less as a traditional coding assistant and more as a research and documentation engine for what appears to be the Numero monorepo — synthesizing architectural overviews, creating reference docs, and building institutional knowledge. Your 9,120 commits in ~40 days (~228/day) further confirm you're running Claude at industrial scale, likely across many automated or semi-automated pipelines.
Key pattern: You operate Claude Code as a massively parallel documentation factory, delegating ambitious multi-file knowledge synthesis tasks and letting agents run autonomously at industrial scale.
User Response Time Distribution
2-10s
7947
10-30s
21037
30s-1m
21940
1-2m
23552
2-5m
19555
5-15m
11371
>15m
5639
Median: 67.1s • Average: 206.6s
Multi-Clauding (Parallel Sessions)
1561
Overlap Events
675
Sessions Involved
7%
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)
50065
Afternoon (12-18)
85577
Evening (18-24)
39906
Night (0-6)
6831
Tool Errors Encountered
Command Failed
33105
Other
6950
User Rejected
1317
File Not Found
847
Edit Failed
698
File Changed
640
Impressive Things You Did
You're running Claude Code at extraordinary scale—over 22,000 sessions and 9,000+ commits across a roughly six-week period—primarily focused on documentation and TypeScript/Markdown-heavy development workflows.
Massive Parallel Documentation Generation
You're leveraging Claude's Task and TodoWrite tools extensively to orchestrate large-scale documentation creation, like generating a 34-file developer documentation directory in a single session using parallel subagents. This kind of structured, multi-agent approach to doc generation shows a sophisticated understanding of how to push Claude Code beyond simple one-off tasks.
Living Knowledge Base From Notes
You've built a workflow where you feed existing notes, audit materials, and scattered documentation into Claude and have it synthesize comprehensive, structured reference docs. Your ability to turn raw internal knowledge into polished developer documentation at scale—with over 121,000 Markdown file interactions—demonstrates you're treating Claude as a core part of your documentation infrastructure.
Codebase Research and Architectural Synthesis
You're effectively using Claude as a research tool to explore and understand your Numero monorepo architecture, asking it to read across dev docs and produce structured architectural overviews. With nearly 133,000 Read operations and 30,000 Grep calls, you've built a highly search-driven workflow that turns Claude into an always-available codebase expert.
What Helped Most (Claude's Capabilities)
Fast/Accurate Search
39
Multi-file Changes
30
Good Explanations
1
Outcomes
Mostly Achieved
43
Unclear
33
Where Things Go Wrong
Your sessions frequently involve ambitious documentation generation tasks that get cut short or take wrong approaches, combined with retrieval issues that slow down research workflows.
Sessions Cut Short on Large-Scale Generation Tasks
You frequently kick off massive documentation creation tasks (e.g., 34-file directory structures) that don't complete within a single session, leaving you with unclear outcomes. Consider breaking these into smaller, sequential batches or explicitly scoping each session to a subset of deliverables so you get usable output even if the session ends.
You asked Claude to create a 34-file developer documentation directory, which launched parallel agents but the session ended before completion — leaving you with partial, potentially inconsistent output
You requested creation of a docs/development/ directory from existing notes, but the session was cut short after Claude only launched exploration tasks with no visible output at all
Wrong Approach on Document Retrieval and Path Resolution
Claude repeatedly took wrong approaches when retrieving documents — pulling from incorrect collections and requiring multiple retries before finding the right content. You could reduce this friction by providing explicit file paths or collection names upfront, or by establishing a CLAUDE.md instruction that clarifies your project's doc structure.
Claude pulled documents from the wrong collection during a research task on Claude Code features, requiring multiple retries before successfully retrieving the correct information
When asked for an architectural overview from dev docs, Claude needed to do exploratory reads rather than going directly to the right files, adding unnecessary overhead to a straightforward lookup
Heavy Reliance on Parallel Sub-Agents Without Completion Guarantees
You lean heavily on Task/TaskUpdate workflows (over 60,000 combined tool calls) to parallelize work, but this pattern frequently results in sessions ending before sub-agents finish, yielding unclear outcomes. Consider using TodoWrite to define checkpoints and having Claude consolidate intermediate results before spawning additional parallel work.
A documentation generation session launched multiple parallel agents to create files, but the transcript ended with no confirmation of which files were actually written or validated
A research session spawned sub-agents for follow-up investigation that was still in progress when the session ended, leaving the follow-up question unanswered
Primary Friction Types
Wrong Approach
39
Inferred Satisfaction (model-estimated)
Likely Satisfied
121
Existing CC Features to Try
Suggested CLAUDE.md Additions
Just copy this into Claude Code to add it to your CLAUDE.md.
Multiple sessions involved large-scale doc generation from existing notes, and sessions were cut short or had unclear outcomes due to parallel agent sprawl without visible progress.
Multiple sessions involved understanding the Numero monorepo architecture via docs, and Claude sometimes struggled with path-based retrieval, suggesting a consistent pointer to the right directory would save time.
Information lookup was the top goal across sessions (159 occurrences), and friction was observed with path-based retrieval pulling from wrong collections, requiring multiple retries.
Just copy this into Claude Code and it'll set it up for you.
Custom Skills
Reusable prompts that run with a single /command for repetitive workflows.
Why for you: Your top goals are information_lookup (159x) and documentation_generation/creation (36x combined). A /gendocs skill could standardize your multi-file doc generation workflow so Claude follows a consistent plan-then-execute pattern instead of launching chaotic parallel agents. A /research skill could enforce structured output for lookups.
mkdir -p .claude/skills/gendocs && cat > .claude/skills/gendocs/SKILL.md << 'EOF'
# Generate Documentation
1. Read all source files referenced by the user
2. Create a numbered plan listing every output file with a one-line description
3. Present the plan and wait for user approval
4. Write files one at a time, confirming each before proceeding
5. At the end, output a summary table: filename | status | line count
EOF
MCP Servers
Connect Claude to external tools, databases, and APIs via Model Context Protocol.
Why for you: You already use MCP for doc retrieval but hit friction with path-based lookups pulling from wrong collections. Configuring a dedicated MCP server scoped to your Numero docs directory would eliminate the retrieval misfires and make your 159+ information_lookup sessions smoother.
claude mcp add numero-docs -- npx -y @anthropic/mcp-filesystem --root ./docs/development
Hooks
Shell commands that auto-run at specific lifecycle events.
Why for you: With 143K Edit operations and heavy TypeScript/Markdown usage, a post-edit hook could auto-run type checking on TypeScript files and lint Markdown files, catching errors immediately rather than discovering them later. This would reduce the 'wrong_approach' friction (39 instances).
Just copy this into Claude Code and it'll walk you through it.
Tame Parallel Agent Sprawl for Doc Generation
Use sequential file creation with checkpoints instead of launching many parallel agents for large documentation tasks.
Multiple sessions show Claude launching parallel agents to create 34+ files from existing notes, but sessions end before completion with unclear outcomes. The parallel approach makes it hard to track progress and recover from interruptions. A sequential approach with explicit progress tracking (using TodoWrite, which you already use 34K+ times) ensures each file is confirmed before moving on, and you can resume if interrupted.
Paste into Claude Code:
Create the docs/development/ directory from my existing notes. Work sequentially: first read all source files, then create a todo list of all output files, then write them one at a time. After each file, mark it done on the todo list. Do NOT use parallel agents.
Structured Research Prompts to Reduce Retrieval Friction
Front-load your information lookup prompts with explicit source paths and output format expectations.
Information lookup is your dominant use case (159 sessions) and the main friction point was Claude pulling from wrong collections during path-based retrieval. By specifying exact paths and expected output structure upfront, you reduce retries and get consistent results. This is especially important in your Numero monorepo where docs live in specific directories.
Paste into Claude Code:
Research [TOPIC] using only files in docs/development/. Read the relevant files first, then provide a structured summary with: 1) Overview (2-3 sentences), 2) Key details (bullet points), 3) Related files (list of paths you read). Do not search other directories unless I ask.
Leverage TodoWrite for Complex Multi-Step Tasks
Explicitly ask Claude to create and maintain a todo checklist for any task involving 5+ files or steps.
You already use TodoWrite heavily (34K+ invocations), which suggests it works well for your workflow. However, sessions with unclear outcomes often involve large tasks without visible progress tracking. Making todo-driven execution an explicit instruction ensures you always have visibility into what's done vs. pending, and can resume interrupted sessions by reviewing the todo state.
Paste into Claude Code:
Before starting, create a detailed todo list of every step and file involved. Update the todo after completing each step. If this task involves more than 10 items, group them into phases and complete one phase at a time.
On the Horizon
Your team is already leveraging parallel agents and multi-file generation at scale—now it's time to make those workflows fully autonomous, self-correcting, and deeply integrated into your development lifecycle.
Autonomous Documentation Generation from Living Codebases
With 34-file doc directories already being generated in single sessions, you can build fully autonomous pipelines where Claude continuously crawls your monorepo, detects doc drift from code changes across your 120K+ lines of TypeScript and Markdown, and regenerates entire documentation trees without human intervention. Imagine every merged PR triggering a subagent swarm that updates architectural overviews, API references, and onboarding guides in parallel—then validates them against the actual codebase.
Getting started: Use Claude Code's Task/subagent system (you're already at 31K Task calls) combined with hooks to trigger doc regeneration on git events. Wire up a CLAUDE.md that defines your doc structure and validation criteria.
Paste into Claude Code:
I need you to build an autonomous documentation maintenance system for our monorepo. First, read the current docs/development/ directory and the source code it documents. Then: 1) Identify every doc that references code structures, APIs, or architecture that have changed since the doc was last updated. 2) For each stale doc, launch a parallel subagent (using Task) to rewrite it based on the current source of truth. 3) After all subagents complete, run a validation pass that greps the codebase to confirm every code reference in every doc actually exists. 4) Generate a changelog summarizing what was updated and why. Use TodoWrite to track progress across all files.
Self-Healing Multi-Agent Pipelines with Retry Logic
Your friction data shows path-based retrieval failures and sessions cut short before subagent completion—classic symptoms of fire-and-forget orchestration. You can build agent workflows that monitor their own subagents, detect failures or stalls, retry with corrected parameters, and guarantee completion. With 29K+ TaskUpdate calls, you already have the feedback loop infrastructure; the next step is making it self-correcting so no session ends with 'unclear_from_transcript' again.
Getting started: Structure your prompts to include explicit error-handling and retry instructions. Use TodoWrite as a persistent checklist that subagents update, so the orchestrator can detect incomplete work and relaunch failed tasks with corrected paths.
Paste into Claude Code:
You are an orchestrator agent. I need you to execute a multi-step plan with full fault tolerance. Here's the pattern to follow for every task:
1. Before launching any subagent via Task, write the full task list to TodoWrite with status 'pending'
2. Launch subagents in parallel batches of 3-5
3. After each batch, check TodoWrite and verify each task moved to 'completed'
4. For any task still 'pending' or 'in_progress' after the batch returns, analyze what went wrong (wrong file path? missing context?), fix the parameters, and relaunch
5. Never consider the job done until every TodoWrite item is 'completed'
Apply this pattern now to: Audit our entire docs/development/ directory. For each doc file, launch a subagent that reads the doc, greps the codebase for every entity it references, and reports back whether the doc is current, stale, or broken. Compile results into a single audit report.
Test-Driven Autonomous Code Migrations at Scale
You're generating 9,120 commits across 22K+ sessions with heavy Bash (423K calls) and Edit (143K calls) usage—your team is clearly making large-scale code changes. The next frontier is having Claude autonomously execute code migrations by writing tests first, then iterating changes against those tests until they pass. Imagine migrating an entire TypeScript module to a new API pattern where Claude writes the target-state tests, refactors file by file across parallel agents, runs the test suite after each change, and self-corrects until green.
Getting started: Combine Claude Code's Bash tool for running test suites with the Task system for parallel file-by-file refactoring. Define your migration spec and target test assertions upfront, then let Claude iterate autonomously.
Paste into Claude Code:
I need you to perform a test-driven code migration autonomously. Here's the process:
1. First, read the current implementation in [src/target-module/] and understand the existing patterns
2. Write comprehensive test cases that define the DESIRED end state after migration (new API patterns, updated imports, changed type signatures). Run them—they should fail.
3. Use TodoWrite to create a file-by-file migration checklist
4. For each file, launch a Task subagent that: a) Reads the file b) Applies the migration pattern c) Runs the relevant test subset via Bash d) If tests fail, reads the error output, adjusts the code, and retries up to 3 times e) Reports back success/failure via TaskUpdate
5. After all files are migrated, run the full test suite. If anything fails, identify the failing files and relaunch targeted fix agents.
6. Only mark the migration complete when ALL tests pass. Show me the final test output and a summary of every file changed.
"User asked Claude to build a 34-file documentation directory and Claude responded by spawning a swarm of parallel sub-agents — then the session ended before anyone found out if they finished"
In one ambitious session, a user handed Claude a plan to generate an entire 34-file developer docs directory from scattered notes. Claude went full mission-control, launching multiple parallel agents to tackle it simultaneously. But the transcript cuts off mid-flight, leaving the outcome a mystery — like a heist movie that ends right after the team splits up.