Find agent skills by outcome

Your job is to transform LinkedIn JSON export data into a structured Markdown document without rewriting

optimizing

Important: LinkedIn normally takes up to 24 hours to prepare and send your data archive. You will not receive the files instantly. Once you have the files

paste their contents (or the most important ones) directly into the next message.

result = _api_post(/challenges/verify

{

name = agent.get(name

query).encode(ascii

lookup = _api_get(f/verify/{quote(query

safe='')})

result = _api_post(/challenges

{agent_id: agent_id})

name = agent.get(name

query).encode(ascii

lookup = _api_get(f/verify/{quote(query

safe='')})

name = agent.get(name

query).encode(ascii

body = e.read().decode(utf-8

errors=replace)

url = f{API_BASE}/verify/{quote(query

safe='')}

Tries slug/DID first (direct API lookup)

then falls back to name search.

result = _api_post(/agents/register

payload)

return sk.encode().hex()

sk.verify_key.encode().hex()

CRITICAL: we sign challenge_hex.encode('utf-8')

NOT bytes.fromhex().

body = e.read().decode(utf-8

errors=replace)

Data

req = Request(url

data=data

data = json.dumps(payload

ensure_ascii=True).encode(utf-8)

req.add_header(Content-Type

application/json)

body = e.read().decode(utf-8

errors=replace)

def _api_post(path

payload):

Data

msg = data.get(error

data.get(message

environmental_storytelling: The warmly lit but empty cafe suggests a late hour

creating a tranquil and lonely atmosphere in an urban setting. The moonlit water adds to the sense of peace.

setting_details: A multi-story building with a cafe on the ground floor stands next to a body of water under a night sky. A crosswalk is in the foreground

and a distant shoreline is visible across the water.