yc-reader
Look up Y Combinator companies, batches, and startup ecosystem data using the yc-oss API (read-only). Use this skill whenever the user wants to research YC-backed startups, find companies in a specific batch or industry, check which YC companies are hiring, explore top YC companies, or analyze startup trends by sector or tag. Triggers include: "YC companies in fintech", "who's in the latest YC batch", "YC startups hiring", "top Y Combinator companies", "find YC companies tagged AI", "W25 batch", "S24 companies", "YC stats", "Y Combinator portfolio", "startup research", "which YC companies do X", "venture research on YC", any mention of Y Combinator, YC batch, or YC-backed companies in the context of startup research, venture analysis, or market intelligence. This is a read-only data source — the API is a static JSON dataset updated daily.
What this skill does
# Y Combinator Reader (Read-Only)
Fetches Y Combinator company data from the [yc-oss/api](https://github.com/yc-oss/api), an unofficial open-source API that indexes all publicly launched YC companies. The data is sourced from YC's Algolia search index and updated daily via GitHub Actions.
**This is a read-only data source.** It provides company profiles, batch listings, industry/tag breakdowns, hiring status, and diversity data. No write operations exist — the API serves static JSON files.
**No authentication required.** The API is public and free. Just use `curl` to fetch JSON endpoints.
---
## Step 1: Verify Prerequisites
This skill only needs `curl` (to fetch data) and `jq` (to parse/filter JSON). Both are pre-installed on most systems.
```
!`(command -v curl > /dev/null && echo "CURL_OK" || echo "CURL_MISSING") && (command -v jq > /dev/null && echo "JQ_OK" || echo "JQ_MISSING")`
```
If `JQ_MISSING`, install it:
```bash
# macOS
brew install jq
# Linux (Debian/Ubuntu)
sudo apt-get install jq
```
If `jq` is unavailable, you can still fetch raw JSON with `curl` and parse it inline with Python or other tools — but `jq` makes filtering much easier.
---
## Step 2: Identify What the User Needs
Match the user's request to the appropriate endpoint. See `references/api_reference.md` for full details.
| User Request | Endpoint | Notes |
|---|---|---|
| Overall YC stats | `meta.json` | Company count, batch list, industry/tag lists |
| All companies | `companies/all.json` | Full dataset (~5,700 companies) — large response |
| Top companies | `companies/top.json` | ~91 top-performing YC companies |
| Companies hiring | `companies/hiring.json` | ~1,400 currently hiring |
| Non-profit companies | `companies/nonprofit.json` | YC-backed non-profits |
| Diversity data | `companies/black-founded.json`, `hispanic-latino-founded.json`, `women-founded.json` | Founder diversity |
| Specific batch | `batches/{batch-name}.json` | e.g., `winter-2026.json`, `spring-2026.json`, `fall-2025.json` |
| Single company profile | `batches/{batch-name}/{slug}.json` | e.g., `batches/summer-2009/stripe.json`, `batches/winter-2009/airbnb.json` |
| By industry | `industries/{industry}.json` | e.g., `fintech.json`, `healthcare.json` |
| By tag | `tags/{tag}.json` | e.g., `ai.json`, `developer-tools.json` |
### Batch name format
Batches use `{season}-{year}` format: `winter-2026`, `spring-2026`, `summer-2026`, `fall-2025`. Older batches follow the same pattern back to `summer-2005`. The short form (`w09`, `s21`) also works for the per-company endpoint.
### Industry and tag name format
Use lowercase with hyphens for multi-word names: `real-estate`, `developer-tools`, `machine-learning`.
---
## Step 3: Execute the Request
### Base URL
```
https://yc-oss.github.io/api/
```
### General pattern
```bash
# Fetch and pretty-print
curl -s https://yc-oss.github.io/api/companies/top.json | jq .
# Count companies in a result
curl -s https://yc-oss.github.io/api/batches/winter-2025.json | jq length
# Filter by field (e.g., hiring companies in a batch)
curl -s https://yc-oss.github.io/api/batches/winter-2025.json | jq '[.[] | select(.isHiring == true)]'
# Extract specific fields
curl -s https://yc-oss.github.io/api/companies/top.json | jq '.[] | {name, one_liner, batch, team_size, website}'
# Search by name (case-insensitive)
curl -s https://yc-oss.github.io/api/companies/all.json | jq '[.[] | select(.name | test("stripe"; "i"))]'
```
### Key rules
1. **Use `-s` flag** with curl to suppress progress output
2. **Pipe through `jq`** for readable output and filtering
3. **Avoid fetching `companies/all.json` unless necessary** — it's a large response (~5,700 companies). Prefer more specific endpoints (batches, industries, tags) when possible
4. **Use `jq` select/filter** to narrow results client-side when the API doesn't have a specific endpoint for what the user wants
5. **Batch names are lowercase with hyphens** — `winter-2025` not `Winter 2025` or `W25`
6. **Tag and industry names are lowercase with hyphens** — `developer-tools` not `Developer Tools`
### Common jq filters
| Filter | Purpose |
|---|---|
| `jq length` | Count results |
| `jq '.[0]'` | First company |
| `jq '.[:10]'` | First 10 companies |
| `jq '[.[] \| select(.isHiring == true)]'` | Only hiring companies |
| `jq '[.[] \| select(.status == "Active")]'` | Only active companies |
| `jq '[.[] \| select(.team_size > 100)]'` | Companies with 100+ employees |
| `jq '.[] \| {name, one_liner, batch, website}'` | Select specific fields |
| `jq '[.[] \| select(.name \| test("query"; "i"))]'` | Search by name |
| `jq 'sort_by(-.team_size) \| .[:10]'` | Top 10 by team size |
---
## Step 4: Present the Results
After fetching data, present it clearly for startup/venture research:
1. **Summarize key data** — company name, one-liner, batch, team size, status, and website
2. **Highlight hiring status** — note which companies are actively hiring (growth signal)
3. **Include website URLs** when the user might want to visit the company
4. **For batch listings**, summarize the batch size and notable companies
5. **For industry/tag queries**, highlight trends (how many companies, which are top/hiring)
6. **For research queries**, provide aggregate stats (count, common industries, team size distribution)
7. **Note the data freshness** — the API updates daily, so data is near-real-time
---
## Step 5: Diagnostics
If a request fails:
| Error | Cause | Fix |
|-------|-------|-----|
| `404 Not Found` | Invalid batch, industry, or tag name | Check `meta.json` for valid names |
| Empty array `[]` | No companies match the query | Broaden the search or check spelling |
| `curl: Could not resolve host` | No internet connection | Check network connectivity |
| Large/slow response | Fetching `companies/all.json` (5,700+ entries) | Use a more specific endpoint or add `jq` filters |
To discover valid batch, industry, and tag names:
```bash
# List all batches
curl -s https://yc-oss.github.io/api/meta.json | jq '.batches[].name'
# List all industries
curl -s https://yc-oss.github.io/api/meta.json | jq '.industries[].name'
# List all tags (there are 333+)
curl -s https://yc-oss.github.io/api/meta.json | jq '.tags[].name'
```
---
## Reference Files
- `references/api_reference.md` — Complete endpoint reference with company schema, all endpoint URLs, and research workflow examples
Read the reference file when you need the exact company field schema, valid batch/industry/tag names, or detailed research workflow patterns.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.