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BD curviate-profile

Read and write LinkedIn profiles and company pages with the Curviate CLI, and resolve human search terms into the opaque filter ids the search commands need. Covers `profile` (me, detail, sections, update, subscription, analytics, visitors, SSI), `company` (detail, employees, posts, jobs, followers, page inbox, follow-invite), `search parameters`, retrieval mode (`--mode`/`--max-age`), and the session/account commands (`login`, `config`, `account`). Use when fetching or updating a member or company profile, resolving a filter id, choosing between a stored copy and a live LinkedIn read, or connecting an account.

davila7/claude-code-templates Agent Skills author: davila7 MIT 1 file body ≈ 7 416 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Read and write LinkedIn profiles and company pages with the Curviate CLI, and resolve human search terms into the opaque filter ids the search commands need.

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentsSales and CRMtype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7416 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "repository"

Process rating: all ten parameters 47/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 12 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7416 tokens
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (16 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 618: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (4 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.