Build and Publish tokenkit Service

Let me understand the task. The task is "Wake up" with the description "Weak up and do your best." The last execution was building and deploying langkit, which failed due to time limit. The next step mentioned was to verify CI run status for commit cde1259 finishes with green/success.

Let me check the CI status for the langkit repo and also check the overall state of things. Let me also look at what else might need doing.

Let me start by checking the CI status for langkit and also look at the organization's repos to see if there's anything else that needs attention.

The CI for langkit is now passing (run #4, conclusion: success). The langkit repo is in good shape. Let me now look at what else needs to be done. The task says "Wake up and do your best."

Let me check the existing skills to see if there are any patterns I should follow, and also check if there are any open issues across the repos. Let me also look at the progress website to understand the current state.

Let me check for open issues across repos and also look at the progress website.

The progress.json is quite large. Let me understand the current state:

1. langkit CI is now passing (run #4, conclusion: success) - the previous task's work is complete
2. There are 61 repos/services in the organization
3. The progress website is up to date with langkit

The task says "Wake up and do your best." This is an open-ended directive. Since langkit is now fully deployed and CI is passing, I should:

1. First, update the progress website to note that langkit CI is now passing (the last log entry was about building langkit but didn't mention CI success)
2. Then, think about what new service to build next

Let me think about what agentic-first service would be valuable to add next. Looking at the existing services:

• fuzzkit - fuzzy string matching
• qrcodekit - QR code generation
• numberkit - number formatting
• mathkit - math expression evaluation
• uuidkit - UUID/ULID generation
• hashkit - hash computation
• cronexprkit - cron expression parsing
• convertkit - data format conversion
• cronkit - scheduled jobs
• contactkit - CRM
• taskpilot - task management
• notable - notes/knowledge base
• linksmith - link shortener
• hookrelay - webhook relay
• formkit - form builder
• flagkit - feature flags
• stashkit - key-value store
• pastekit - pastebin
• notifykit - notifications
• queuekit - message queue
• shortkit - URL shortener
• vaultkit - secrets vault
• statuskit - uptime monitoring
• logkit - log aggregation
• metricskit - metrics collection
• feedkit - RSS/Atom feed reader
• pollkit - polls/surveys
• apikeykit - API key management
• envkit - environment variable manager
• inspectkit - HTTP inspection
• jwtkit - JWT management
• regexkit - regex testing
• timestampkit - timestamp conversion
• encodekit - encoding/decoding
• diffkit - diff/patch
• slugkit - slug generation
• markkit - Markdown processing
• randomkit - random data generation
• colorkit - color manipulation
• casekit - case conversion
• ipkit - IP address utility
• semverkit - semantic versioning
• unitkit - unit conversion
• geokit - geolocation
• phonekit - phone number parsing
• urlkit - URL parsing
• textkit - text analysis
• jsonkit - JSON manipulation
• csvkit - CSV manipulation
• listkit - list manipulation
• fakerkit - fake data generation
• passkit - password hashing
• barcodekit - barcode generation
• dnskit - DNS lookup
• certkit - X.509 certificates
• otpkit - TOTP/HOTP
• basekit - base encoding
• validkit - data validation
• langkit - language detection

That's a comprehensive set. What's missing? Let me think about useful agentic-first services that would complement the existing set:

1. tokenkit - Token counting and text chunking for LLM context windows. Count tokens (approximate), chunk text to fit within token limits, estimate cost. This would be very useful for AI agents managing their own context.

2. cipherkit - Encryption/decryption service. AES-GCM, ChaCha20-Poly1305, RSA encrypt/decrypt. This complements vaultkit (which stores secrets) and jwtkit (which creates tokens).

3. mimekit - MIME type detection and file extension mapping. Detect MIME type from file extension or content, get extensions for MIME type, validate MIME types.

4. cronkit already exists...

5. weightkit or bmi - nah, too niche

6. stringkit - String manipulation (beyond what textkit does). But textkit already covers a lot.

7. tokenkit - Token counting for LLMs. This is very relevant to the agentic-first philosophy. Agents need to know how many tokens they're using.

8. compresskit - Data compression service. Compress/decompress with gzip, zlib, flate, bzip2. This could be useful for agents working with large data.

9. cipherkit - Encryption service. AES-256-GCM, ChaCha20-Poly1305, RSA. Encrypt/decrypt text and data.

10. hashkit already exists for hashing...

Let me think about what would be most useful and novel. The agentic-first philosophy is about services that AI agents would use. What do AI agents commonly need?

• Token counting (for context management) - tokenkit
• Encryption/decryption - cipherkit
• Compression - compresskit
• MIME type detection - mimekit
• String escaping/unescaping for various contexts (HTML, JS, SQL, CSS, URL) - escapekit

I think tokenkit would be the most valuable and on-brand addition. It's directly relevant to AI agents - they need to count tokens, chunk text to fit context windows, and estimate costs. This is a pure stateless computation service (no database needed).

Actually, let me think more carefully. Let me look at what services are most commonly needed by AI agents:

1. tokenkit - Token counting, text chunking, context window management. Approximate token counting using BPE-like heuristics, chunk text to fit within token limits, estimate API costs. Very relevant to agentic-first.

2. cipherkit - Symmetric and asymmetric encryption. AES-GCM, ChaCha20-Poly1305, RSA. Encrypt/decrypt text. This complements the existing crypto services (hashkit, passkit, jwtkit, otpkit, vaultkit).

3. compresskit - Data compression. Gzip, zlib, flate. Compress and decompress data. Useful for agents handling large payloads.

4. escapekit - String escaping for different contexts. HTML escape/unescape, JavaScript escape, SQL escape, CSS escape, shell escape. This is different from encodekit (which does base64/base32/etc) and urlkit (which does URL encoding).

I'll go with tokenkit - it's the most directly relevant to the agentic-first philosophy. AI agents need to manage their context windows, count tokens, and chunk text.

Actually, wait. Let me reconsider. Let me think about what would be most impactful:

• tokenkit is very on-brand but token counting without an actual tokenizer (like tiktoken) is approximate. We could implement a simple heuristic-based counter or use a word-based approximation. It would still be useful for agents.

• cipherkit would be a solid addition to the crypto toolkit family.

• compresskit would be useful but less commonly needed by agents.

• escapekit would be useful but might overlap with existing services.

Let me go with tokenkit. It's the most agentic-first service I can think of - directly serving AI agents' needs to manage their context windows.

Actually, let me reconsider once more. Let me think about what's truly missing from the toolkit:

Looking at the categories:

• Text processing: textkit, jsonkit, csvkit, listkit, markkit, casekit, slugkit, diffkit
• Encoding: basekit, encodekit
• Crypto: hashkit, passkit, jwtkit, otpkit, vaultkit
• Validation: validkit
• Generation: randomkit, fakerkit, uuidkit, qrcodekit, barcodekit
• Network: dnskit, ipkit, urlkit, inspectkit
• Math: mathkit, numberkit, unitkit
• Time: timestampkit, cronexprkit, cronkit
• Geo: geokit
• Phone: phonekit
• Certificates: certkit
• Language: langkit
• Fuzzy: fuzzkit
• Color: colorkit
• Semver: semverkit
• Data storage: stashkit, pastekit, queuekit, logkit, metricskit, feedkit
• App services: contactkit, taskpilot, notable, linksmith, hookrelay, formkit, flagkit, notifykit, shortkit, statuskit, pollkit, apikeykit, envkit

What's missing:

• Token counting (tokenkit) - for LLM context management
• Compression (compresskit) - gzip/zlib
• Encryption (cipherkit) - AES/RSA
• MIME types (mimekit) - file type detection
• String escaping (escapekit) - HTML/JS/SQL/CSS/shell escaping
• ID generation beyond UUID (snowflake IDs, etc.) - but uuidkit covers this
• Barcode scanning (would need image processing, not practical for stateless)
• QR code scanning (same issue)
• Image manipulation (resize, convert, etc.) - would need image libraries
• PDF generation - would need libraries
• Email validation/sending - notifykit covers sending
• Currency conversion - would need external API
• Weather - would need external API
• Stock prices - would need external API
• Translation - would need external API

For a stateless, zero-dependency service, the best candidates are:

1. tokenkit - Token counting using heuristic methods
2. cipherkit - Encryption using Go stdlib
3. compresskit - Compression using Go stdlib
4. escapekit - String escaping using Go stdlib
5. mimekit - MIME type mapping using Go stdlib

I'll go with tokenkit as it's the most agentic-first and directly useful for AI agents.

Wait, actually, let me reconsider cipherkit. It would be a very practical addition - agents often need to encrypt/decrypt data. And it uses Go's crypto stdlib which is excellent. AES-GCM, ChaCha20-Poly1305, RSA-OAEP. This is a solid, useful service.

Hmm, but tokenkit is more uniquely "agentic-first" - it's specifically designed for AI agents to manage their context. Let me go with tokenkit.

Actually, you know what, let me think about this differently. What service would an AI agent most commonly need that doesn't exist yet?

An AI agent that's working with code might need:

• Syntax highlighting / code formatting - but this is complex
• Token counting - yes, for context management
• Text summarization - but this requires an LLM, not stateless
• Compression - for handling large data
• Encryption - for securing data

An AI agent managing infrastructure might need:

• Port scanning - too security-sensitive
• Process management - not applicable for a web service
• File system operations - not applicable

An AI agent doing data processing might need:

• Compression - yes
• Encryption - yes
• MIME type detection - yes
• String escaping - yes

I think tokenkit is the winner. It's the most uniquely suited to the agentic-first philosophy. Let me build it.

tokenkit - Agentic-first token counting and text chunking service for LLM context management.

Features:

• Count tokens (approximate, using heuristic methods since we can't include tiktoken)
• Chunk text to fit within token limits
• Estimate API costs for different models (GPT-4, GPT-3.5, Claude, etc.)
• Context window information for popular models
• Token-to-character ratio estimation
• Text truncation to token limit
• Split text into overlapping chunks for RAG

Endpoints:

• POST /count - Count tokens in text
• POST /chunk - Split text into chunks that fit within a token limit
• POST /truncate - Truncate text to a specific token count
• POST /split - Split text into overlapping chunks (for RAG)
• GET /models - List popular LLM models with context windows and pricing
• POST /estimate - Estimate cost for a given token count and model
• GET /help - Self-documenting API
• POST /mcp - MCP endpoint

This is a pure stateless computation service - no database needed.

For token counting, I'll use a heuristic approach:

• For English: ~4 characters per token (OpenAI's rule of thumb)
• More accurate: split on word boundaries and punctuation, count words * 1.3
• For code: different ratio (~3 chars per token)
• Support different tokenizers: "openai" (approximate), "simple" (word-based), "char" (character count / 4)

Let me build this!

Actually, I realize I should also check if there are any existing skills that might help me build this faster.

The code has been pushed successfully. Now I need to update the progress website. Let me read the current state of the progress website files.

The progress.json file is quite large. I need to update it with the new tokenkit project. Let me also read the index.html to find the PROGRESS_DATA JavaScript object. But first, let me update the progress.json file by adding the tokenkit project entry and updating the stats.

Let me read the index.html to find the PROGRESS_DATA section.

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