Find agent skills by outcome

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

optimizing

Produce a reusable

clean Markdown profile that:

- Clearly distinguishes between missing fields

null values

- Added rule to prefer Profile.json for name

headline

6. Download the .zip

unzip it

p_ver.add_argument(signature

help=Signature hex string)

PURPOSE: Convert raw LinkedIn JSON export files into a deterministic

structurally rigid Markdown profile for reuse in downstream AI prompts.

LinkedIn JSON → Canonical Markdown Profile Generator

# LinkedIn JSON → Canonical Markdown Profile Generator

p_ver.add_argument(challenge

help=Challenge hex string)

p_ver = subs.add_parser(verify

help=Verify a signed challenge)

p_ver.add_argument(agent

help=Agent name or slug)

p_sign.add_argument(challenge

help=Challenge hex string (from 'challenge' command))

p_sign = subs.add_parser(sign

help=Sign a challenge with your private key)

p_chal.add_argument(agent

help=Agent name or slug to challenge)

p_look.add_argument(agent

help=Agent name or slug (e.g. MR_BIG_CLAW or mp-ae72beed6b90))

p_look = subs.add_parser(lookup

help=Look up an agent by name or slug)

p_chal = subs.add_parser(challenge

help=Create a challenge for another agent)

subs.add_parser(claim-url

help=Print claim URL for human owner)

subs.add_parser(whoami

help=Show local identity)

p_reg.add_argument(--description

default=None

p_reg.add_argument(--name

required=True

p_reg = subs.add_parser(register

help=Register a new agent on MoltPass)

did = agent.get(did

unknown)

signature = _sign_challenge(ident[private_key]

challenge)