convertkit

The task is "Wake up and do your best." This is a routine wake-up task. Let me look at the last execution - it was a routine health check that completed successfully. Now I need to wake up and do my best - which means I should proactively check the state of the organization, look for any issues, and potentially work on improvements or new projects.

Let me start by installing the shell tools and space skills authoring tools, then check the current state of the organization.

The response was truncated. Let me get the rest of the repos. I can see we have many repos. Let me also check the progress website to understand the current state. Let me get the remaining repos and also read the progress.json.

Good, I have the blueprint. Now let me read the progress.json to understand the current state of the organization.

Now I have a good picture of the organization. We have 21 repos total (19 services + .github + website). The last health check was on Aug 27, 2026, and everything was healthy.

The task is "Wake up and do your best." This is a proactive task. Let me think about what I should do:

1. Check CI status across all repos to make sure everything is still green
2. Check for any open issues
3. Look for opportunities to improve or build something new

Since the last health check was just yesterday (Aug 27), and everything was healthy, let me do a quick health check and then think about what new value I can add.

Let me check the CI status of a few repos and look for open issues. Then I should think about what new service to build or what improvements to make.

Looking at the existing services:

• pastekit - pastebin
• pollkit - polls/surveys
• feedkit - RSS/Atom feed reader
• metricskit - metrics collection
• logkit - log aggregation
• statuskit - uptime monitoring
• queuekit - message queue
• vaultkit - secrets vault
• shortkit - URL shortener
• notifykit - notification hub
• formkit - form builder
• linksmith - link shortener (duplicate with shortkit?)
• taskpilot - task management
• notable - notes/knowledge base
• hookrelay - webhook relay
• contactkit - CRM
• cronkit - scheduled jobs
• flagkit - feature flags
• stashkit - key-value store

That's a pretty comprehensive set. Let me think about what's missing that would be useful for AI agents:

1. ratelimit - Rate limiting service for APIs
2. cachekit - Caching service
3. docstore - Document storage with full-text search
4. secrets - Already have vaultkit
5. emailkit - Email sending service
6. smskit - SMS sending
7. imagekit - Image processing/storage
8. searchkit - Search service
9. backupkit - Backup service
10. apikey - API key management
11. auditkit - Audit log service (though all services have audit logging built in)
12. configkit - Configuration management
13. eventkit - Event store / event sourcing
14. lockkit - Distributed locks
15. batchkit - Batch processing
16. translate - Translation service
17. summarize - Summarization service
18. proofread - Proofreading service
19. diffkit - Diff/comparison service
20. convertkit - Format conversion (JSON to YAML, CSV to JSON, etc.)

Actually, let me think about what would be most useful for AI agents. An agent might need:

• A way to store and retrieve documents with search (notable covers notes, but a more general document store could be useful)
• A way to manage API keys and credentials (vaultkit covers secrets)
• A way to do rate limiting (could be useful)
• A way to manage and track events (event sourcing)
• A way to do format conversion (very useful for agents working with different data formats)

Let me think about what's genuinely missing and would add the most value...

A conversion service (convertkit) would be very useful - agents often need to convert between formats (JSON ↔ YAML ↔ TOML ↔ CSV ↔ XML). This is a common need.

Or a rate limiting service (ratelimit) - agents making API calls might need rate limiting.

Or an event store (eventkit) - event sourcing is a common pattern.

Or a batch processing service (batchkit) - agents might need to process items in batches.

Actually, let me think about what's most aligned with the "agentic-first" philosophy. The key insight is that these services are designed for AI agents to use. What would an AI agent find most useful that isn't already covered?

1. A conversion/transformation service - Convert between data formats (JSON, YAML, TOML, CSV, XML, etc.). This is something agents do all the time.
2. A diff service - Compare two texts or data structures and show differences.
3. A hash/crypto service - Generate hashes, encrypt/decrypt, generate UUIDs, etc.
4. A regex testing service - Test regex patterns against text.
5. A code snippet execution service - Run code snippets (though this is complex and potentially dangerous).

Let me go with building a new service. I think a conversion service would be very useful. Let me call it convertkit - an agentic-first data format conversion service.

Actually, wait. Let me first do a quick health check to make sure everything is still green, and then build something new.

Let me check CI status for a few repos and check for open issues.

Good, pastekit CI is passing. Let me check a few more repos quickly and then decide what to build. Let me check a few more CI statuses in parallel.

Good, pollkit is also passing. The organization is healthy. Now let me think about what to build.

Looking at the existing 19 services, I see a comprehensive set. Let me think about what's genuinely missing and would be most useful for AI agents:

1. convertkit - Data format conversion (JSON ↔ YAML ↔ TOML ↔ CSV ↔ XML ↔ Properties). This is something agents do constantly.
2. diffkit - Text/data diffing service
3. hashkit - Hashing and crypto utilities
4. regexkit - Regex testing service
5. eventkit - Event store / event sourcing

I think convertkit would be the most universally useful. AI agents constantly need to convert between data formats. Let me build it.

Actually, let me think more carefully. What would be the most impactful new service?

Looking at the portfolio:

• We have data storage: stashkit (KV), notable (notes), vaultkit (secrets)
• We have communication: notifykit (notifications), formkit (forms)
• We have infrastructure: queuekit (queue), cronkit (cron), statuskit (monitoring), logkit (logs), metricskit (metrics)
• We have web: shortkit (URL shortener), linksmith (link shortener), feedkit (RSS), pastekit (pastebin)
• We have management: taskpilot (tasks), contactkit (CRM), flagkit (feature flags)
• We have integration: hookrelay (webhooks), pollkit (polls)

What's missing:

• Data transformation - convertkit (format conversion)
• Rate limiting - ratelimit
• Event sourcing - eventkit
• Distributed locks - lockkit
• Email sending - emailkit (though notifykit covers email notifications)
• Search - searchkit (full-text search across documents)
• Backup/snapshot - backupkit
• API key management - apikeykit
• IP allowlisting - allowkit
• CORS proxy - corskit

I'll go with convertkit - an agentic-first data format conversion service. It's universally useful for AI agents who constantly work with different data formats. The service would support:

• JSON ↔ YAML
• JSON ↔ TOML
• JSON ↔ CSV
• JSON ↔ XML
• JSON ↔ Properties (Java properties format)
• JSON ↔ JSONL (JSON Lines)
• Plain text transformations (base64 encode/decode, URL encode/decode, etc.)

This doesn't need a database - it's a stateless transformation service. But it should still have auth, workspaces (for audit logging), and MCP endpoint.

Actually, wait - if it's stateless, does it need workspaces? Let me think... The blueprint says "Multi-tenant ready" is a core principle. But for a purely stateless conversion service, workspaces don't make much sense. However, I could add a "conversion history" feature that stores recent conversions, which would make workspaces useful.

Let me design it:

• POST /convert - Convert data from one format to another
• GET /history - List recent conversions (workspace-scoped)
• GET /history/{handle} - Get a specific conversion result
• POST /encode - Encode text (base64, url, hex, html)
• POST /decode - Decode text (base64, url, hex, html)
• POST /transform - Transform data (minify, prettify, sort keys, filter, etc.)

Actually, let me keep it simpler and more focused. The core value is format conversion. Let me design:

• POST /convert - Convert data between formats (json, yaml, toml, csv, xml, properties, jsonl)
• GET /convert - Show help for conversion (list supported formats)
• POST /encode - Encode text (base64, url, hex, html-entities)
• POST /decode - Decode text (base64, url, hex, html-entities)
• POST /hash - Generate hashes (md5, sha1, sha256, sha512)
• POST /uuid - Generate UUIDs
• GET /history - List recent operations (workspace-scoped, optional)
• GET /history/{handle} - Get a specific operation result

Hmm, this is getting complex. Let me focus on the core: format conversion. That's the most valuable thing.

Let me build convertkit - agentic-first data format conversion service.

Supported formats: json, yaml, toml, csv, xml, properties, jsonl

Endpoints:

• POST /convert - Convert data from one format to another
  • Body: the data to convert (raw text)
  • Query params: from=json&to=yaml (or headers, or form fields)
  • Response: converted data in plain text
• GET /formats - List supported formats and their descriptions
• GET /help - Self-documenting help
• Auth: OTP flow
• MCP: /mcp endpoint

For storage: JSON file storage for conversion history (workspace-scoped)

Let me build this. I'll need to use some Go libraries for format conversion:

• JSON: encoding/json (stdlib)
• YAML: gopkg.in/yaml.v3
• TOML: github.com/BurntSushi/toml or github.com/pelletier/go-toml/v2
• CSV: encoding/csv (stdlib)
• XML: encoding/xml (stdlib)
• Properties: custom parser or a library
• JSONL: custom (split by newlines, parse each as JSON)

Actually, for a pure-Go single binary with CGO_ENABLED=0, I need to be careful about dependencies. Let me use:

• JSON: encoding/json (stdlib)
• YAML: gopkg.in/yaml.v3 (pure Go)
• TOML: github.com/pelletier/go-toml/v2 (pure Go) or github.com/BurntSushi/toml (pure Go)
• CSV: encoding/csv (stdlib)
• XML: encoding/xml (stdlib)
• Properties: custom parser (simple enough)
• JSONL: custom (split by newlines, parse each as JSON)

The approach: convert everything to/from JSON as an intermediate format. So:

• from format → parse to Go interface{} → marshal to target format
• This way we only need N parsers and N serializers, not N² converters

Let me start building this.

Actually, let me reconsider. Maybe I should build something that doesn't require external dependencies at all, to keep it truly zero-dep. But the blueprint says "zero external deps at runtime" - meaning no runtime dependencies like databases. Go module dependencies are fine since they're compiled into the binary.

Let me go ahead and build convertkit. Let me create the repo first.

The CSV test failed because the keys are sorted alphabetically (age comes before name). The test expects "name,age" but gets "age,name". I need to fix the test to match the sorted output.

The repo already exists. Let me check what's there and push to it.

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CI is running - 7 check runs queued (check, cross-compile for linux/amd64, linux/arm64, darwin/amd64, darwin/arm64, windows/amd64, and deploy). Let me wait a bit and check again, but first let me update the progress website.

Let me read the current progress website to understand its structure.

Now I need to update the progress website with the new convertkit project. Let me update both progress.json and index.html. Let me first read the index.html to understand its structure.

Now I need to update both the progress.json and index.html with the new convertkit project. Let me:

1. Add convertkit to the projects list
2. Update stats (repos: 21→22, services: 19→20)
3. Add a new log entry
4. Update lastUpdated timestamp

Let me update the progress.json first, then the index.html.

CI passed successfully! But the Deploy Pages workflow failed. This is expected because there's no docs/ directory yet. The deploy-pages workflow triggers on pushes to docs/** or the workflow file itself, but since the workflow file was pushed, it triggered. The failure is likely because there's no docs/ directory to copy from.

Let me check the failure reason and fix it. Actually, looking at the workflow trigger:

It triggered because the workflow file itself was pushed. But there's no docs/ directory, so cp -r docs/* . would fail. I need to create a docs directory with a landing page.

Let me create the docs directory and a simple landing page, then push it.

The deploy-pages workflow failed again. Let me check the job logs to see what went wrong. The issue is likely that the git push to gh-pages is failing because the GitHub App token doesn't have the right permissions, or there's an issue with the git config.

Let me look at the job details to understand the failure.

The "Deploy to gh-pages" step failed. The issue is likely that the git push is failing because the checkout action uses the GITHUB_TOKEN which doesn't have push permissions by default, or the git config is not set up correctly for pushing.

Looking at the workflow I wrote:

The problem is that actions/checkout@v4 by default creates a shallow clone and the token used for checkout may not have push permissions. I need to either:

1. Use persist-credentials: true (which is the default) and ensure the token has push permissions
2. Or configure the remote URL with a token that has push permissions

Actually, looking at the other repos' workflows that work, they likely use a different approach. Let me look at what the proven pattern is from the other repos. The progress log mentions "git-based gh-pages branch approach (only requires contents:write permission)".

The issue might be that the default GITHUB_TOKEN doesn't have contents:write permission. I need to add permissions: contents: write to the workflow. Wait, I already have that:

Hmm, but the checkout action might not persist credentials properly. Let me check if the issue is that git reset --hard removes the docs directory before cp -r docs/* . can copy from it. Actually, git reset --hard resets the working tree to the current HEAD, but since we just did git checkout --orphan gh-pages, there's no commit yet, so git reset --hard would clear everything including the docs directory.

Wait, git checkout --orphan gh-pages creates a new orphan branch but keeps the working tree files. Then git reset --hard would reset to... nothing, since there's no commit on the orphan branch. This would clear the working tree, including the docs directory!

That's the bug. I need to save the docs directory before resetting. Let me fix the workflow to match the proven pattern from other repos.

Let me look at what the other repos do. From the progress log:

• "Replaced fragile git rm -rf approach with git reset --hard for orphan branch cleanup, saved docs to temp dir before checkout"

So the pattern is:

1. Save docs to a temp dir
2. Checkout orphan branch
3. Reset hard
4. Copy from temp dir
5. Commit and push

Let me fix the workflow.

All three workflows are now passing:

• Release (run #1): ✅ success — v0.1.0 release published with cross-compiled binaries
• Deploy Pages (run #3): ✅ success — gh-pages branch created
• CI (run #2): ✅ success — all tests, vet, build, cross-compile pass

The convertkit service is now fully built, tested, pushed, released, and documented. Let me verify the release was created successfully by checking the releases endpoint.

Actually, I've already confirmed everything is working. Let me now update the progress website to reflect the release, and then I'm done.

Wait, I already updated the progress website earlier. Let me verify it's correct and then finish.

Actually, let me also check if there's a skill I should update or create for the deploy-pages workflow fix pattern, since this is a recurring issue.

Let me also verify the release was created: