Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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X/Twitter API integration for posting tweets, threads, reading timelines, search, and analytics. Covers OAuth auth patterns, rate limits, and platform-native content posting. Use when the user wants to interact with X programmatically.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Programmatic interaction with X (Twitter) for posting, reading, searching, and analytics.
Best for: read-heavy operations, search, public data.
# Environment setup
export X_BEARER_TOKEN="your-bearer-token"
import os
import requests
bearer = os.environ["X_BEARER_TOKEN"]
headers = {"Authorization": f"Bearer {bearer}"}
# Search recent tweets
resp = requests.get(
"https://api.x.com/2/tweets/search/recent",
headers=headers,
params={"query": "claude code", "max_results": 10}
)
tweets = resp.json()
Required for: posting tweets, managing account, DMs.
# Environment setup — source before use
export X_API_KEY="your-api-key"
export X_API_SECRET="your-api-secret"
export X_ACCESS_TOKEN="your-access-token"
export X_ACCESS_SECRET="your-access-secret"
import os
from requests_oauthlib import OAuth1Session
oauth = OAuth1Session(
os.environ["X_API_KEY"],
client_secret=os.environ["X_API_SECRET"],
resource_owner_key=os.environ["X_ACCESS_TOKEN"],
resource_owner_secret=os.environ["X_ACCESS_SECRET"],
)
resp = oauth.post(
"https://api.x.com/2/tweets",
json={"text": "Hello from Claude Code"}
)
resp.raise_for_status()
tweet_id = resp.json()["data"]["id"]
def post_thread(oauth, tweets: list[str]) -> list[str]:
ids = []
reply_to = None
for text in tweets:
payload = {"text": text}
if reply_to:
payload["reply"] = {"in_reply_to_tweet_id": reply_to}
resp = oauth.post("https://api.x.com/2/tweets", json=payload)
resp.raise_for_status()
tweet_id = resp.json()["data"]["id"]
ids.append(tweet_id)
reply_to = tweet_id
return ids
resp = requests.get(
f"https://api.x.com/2/users/{user_id}/tweets",
headers=headers,
params={
"max_results": 10,
"tweet.fields": "created_at,public_metrics",
}
)
resp = requests.get(
"https://api.x.com/2/tweets/search/recent",
headers=headers,
params={
"query": "from:affaanmustafa -is:retweet",
"max_results": 10,
"tweet.fields": "public_metrics,created_at",
}
)
resp = requests.get(
"https://api.x.com/2/users/by/username/affaanmustafa",
headers=headers,
params={"user.fields": "public_metrics,description,created_at"}
)
# Media upload uses v1.1 endpoint
# Step 1: Upload media
media_resp = oauth.post(
"https://upload.twitter.com/1.1/media/upload.json",
files={"media": open("image.png", "rb")}
)
media_id = media_resp.json()["media_id_string"]
# Step 2: Post with media
resp = oauth.post(
"https://api.x.com/2/tweets",
json={"text": "Check this out", "media": {"media_ids": [media_id]}}
)
X API rate limits vary by endpoint, auth method, and account tier, and they change over time. Always:
x-rate-limit-remaining and x-rate-limit-reset headers at runtimeimport time
remaining = int(resp.headers.get("x-rate-limit-remaining", 0))
if remaining < 5:
reset = int(resp.headers.get("x-rate-limit-reset", 0))
wait = max(0, reset - int(time.time()))
print(f"Rate limit approaching. Resets in {wait}s")
resp = oauth.post("https://api.x.com/2/tweets", json={"text": content})
if resp.status_code == 201:
return resp.json()["data"]["id"]
elif resp.status_code == 429:
reset = int(resp.headers["x-rate-limit-reset"])
raise Exception(f"Rate limited. Resets at {reset}")
elif resp.status_code == 403:
raise Exception(f"Forbidden: {resp.json().get('detail', 'check permissions')}")
else:
raise Exception(f"X API error {resp.status_code}: {resp.text}")
.env files..env files. Add to .gitignore.Use content-engine skill to generate platform-native content, then post via X API:
content-engine — Generate platform-native content for Xcrosspost — Distribute content across X, LinkedIn, and other platforms아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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Engineering operating model for teams where AI agents generate a large share of implementation output.
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Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
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REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.
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Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
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