AC omni-x-data-extractor
Extract X (Twitter) data including user profiles, posts, followers, followings, media, and search results. This skill provides comprehensive Twitter data extraction capabilities using the tweeterpy library. Trigger scenarios: - When user asks to get Twitter user information or profile - When user wants to extract tweets from a specific user - When user needs to analyze Twitter followers or followings - When user wants to search for tweets by keywords - When user needs to extract media from Twitter posts Authentication levels: - Guest session (no auth): get_user_profile, get_user_tweets - Authenticated session (auth_token required): get_user_followers, get_user_followings, get_user_media, search_tweets
As a process C 64/100 · Has gaps — weak spots: when it triggers, consistency, running it twice
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 713 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
Process rating: all ten parameters 64/100
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (omni-x-data-extractor) differs from the folder (omni-x)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 62 steps, 1 vague phrases
- 100Failures and branches. 3 branches, has a failure section
- 100Execution cost. Instruction body is 2890 tokens
- 100Progress reporting. Reports progress
- low 11 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)
- -33 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 712: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 62 items
- +3Output format is stated explicitly
- +4Has examples (16 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.