SKILLEMALL.ai
GitHub ↗ RU Sign in

DD linkedin-hack

Your agent crawls LinkedIn through the browser session you already have — profiles, search, connections, inbox, feed. No official API, no app review. Your cookies never leave the browser: API calls are a fetch() run inside the linkedin.com tab you shared (people-search navigates that tab to a LinkedIn search page and reads the results), and 1.2.1 DELETED the cookie-extraction, session-store and external-replay code from the package rather than leaving it switched off — there is no longer anything to store or steal. Every request, navigation and tab pick is pinned to exactly https://www.linkedin.com, including the LINKEDIN_TARGET_ID override, so a look-alike host cannot borrow your session. Reads and drafts freely; the one write, message-send, needs per-action consent that repeats the exact conversation URN, and without it you get the draft and nothing is sent. The browser relay must be a literal loopback IP, with no override. Daily ceilings count every record requested and are reserved before the request goes out. Built for the TinkerClaw fork — github.com/globalcaos/tinkerclaw. See Permissions, Data Flow & Consent.

ClawHub Agent Skills author: Oscar Serra v1.2.2 MIT-0 4 files · 3 scripts body ≈ 6 463 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

Your agent crawls LinkedIn through the browser session you already have — profiles, search, connections, inbox, feed.

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency

IntegrationGitHubSoftware developmentAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
D
73/100
safety, quality, tests
Safety 60%
100
Quality 40%
33
Run on models
none yet
Process rating
D
45/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. Shorten the description to 1024 characters.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1133 chars, limit 1024
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6463 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note edit-residue the text marks something as outdated (lines 190, 191, 197, 217, 285, 305): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 45/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 47 mutating operations with no state check
  • 40Consistency. Frontmatter name (linkedin-hack) differs from the folder (tinker-linkedin)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web, git, node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6463 tokens
  • 85Steps. 50 steps, 1 vague phrases
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill
  • high The skill tells the model to perform an irreversible action with no human approval
  • low The response is described with custom markup (14 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1133: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 50 items
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 33.

External checks

ClawHub: clean
This skill is high-trust LinkedIn browser automation, but its sensitive access is clearly disclosed, scoped to a shared LinkedIn tab, and includes meaningful user controls.
LLM: benign (high) · VirusTotal: · 17 Sept 2026