CD linkedin-data
LinkedIn API integration with a single superagnt API key (Bearer token). Fetch company profiles, jobs, people, posts, and professional network insights. Use this skill when users want LinkedIn data for sales, recruiting, or enrichment. For other social data platforms, use the social-data skill (https://clawhub.ai/superagnt/skills/social-data).
LinkedIn API integration with a single superagnt API key (Bearer token).
As a process D 37/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
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medium Broad scope
meta-requests-env-secretSKILL.md:1Skill asks the runtime to inject credential env vars into its sandbox: SUPERAGNT_API_KEY — verify each one is needed for the stated purposerequired_environment_variables: SUPERAGNT_API_KEY
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:845High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"name": "supe…_V2",
quoted
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 345 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
body-longSKILL.md body ≈ 9462 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "required_environment_variables"
Process rating: all ten parameters 37/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Execution cost. Instruction body is 9462 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Consistency. The Hermes dialect needs category and tags
- 100Steps. 13 steps
- low 10 top-level sections: this looks like several domains in one skill
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 345: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.