social-media-intelligence

Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.

Quality

57%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

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tessl review fix ./agent/src/skills/social-media-intelligence/SKILL.md

Quality

Content

53%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with extensive executable code and a clear logical pipeline, but it is a monolithic ~1295-line file with no bundle references and lacks validation feedback loops for its batch collection operations. Splitting platform-specific material into reference files and adding validation checkpoints would materially improve it.

Suggestions

Move per-platform collection code and JSON schemas into separate files under references/ (e.g., references/twitter.md, references/schemas.md) and link to them from SKILL.md to fix progressive disclosure.

Add explicit validation/feedback checkpoints to the batch collection workflow (e.g., verify schema, check rate-limit headroom, retry-on-failure loops) so workflow clarity is not capped at 3.

Trim the descriptive Section 1 platform overview prose and consolidate repeated import boilerplate to tighten conciseness.

Dimension	Reasoning	Score
Conciseness	The body is packed with dense, actionable code and schemas that earn their place, but Section 1 is largely descriptive/conceptual platform overview prose and the ~1295-line total could be tightened, so it is mostly efficient with some unnecessary explanation rather than lean.	3 / 5
Actionability	Most sections provide copy-paste-ready Python with realistic signatures and docstrings, but there are minor gaps such as the undefined _collect_twitter/_enrich_with_sentiment helpers in section 6.1, the project-specific 'from src.providers.base import get_llm' import, and occasional missing top-level imports, keeping it just below fully executable.	4 / 5
Workflow Clarity	Sections 1-7 form a clear collect -> quantify -> factor -> integrate pipeline, but the batch data-collection operations lack explicit validation checkpoints or validate-fix-retry feedback loops, and the rubric caps workflow clarity at 3 when such feedback loops are missing for batch operations.	3 / 5
Progressive Disclosure	No bundle files exist (references/, scripts/, assets/ are absent) and the entire ~1295-line skill is inlined in SKILL.md, including per-platform collection code and JSON schemas that clearly belong in separate reference files; the numbered section headers provide structure, but the absence of any file-level splitting for a skill this large matches the 'content that clearly belongs in separate files is inlined' anchor.	2 / 5
	Total	12 / 20 Passed

Description

61%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description clearly names a specialized domain and concrete platforms but lacks an explicit 'Use when' trigger clause, capping completeness. It is distinct and reasonably specific but would benefit from listing more concrete actions and adding trigger guidance.

Suggestions

Add an explicit 'Use when ...' clause naming natural trigger phrases (e.g., 'Use when extracting sentiment signals from social media for trading research').

List 2-3 concrete actions beyond extraction (e.g., 'collect, score sentiment, and build quant factors from social media data') to lift specificity.

Include common synonyms such as 'sentiment analysis', 'FinTwit', and 'social trading signals' to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Quotes 'financial signal extraction' and 'sentiment-driven trading strategies' alongside the four named platforms, giving a clear domain plus 1-2 concrete actions, but it does not enumerate multiple distinct actions (only extraction is named), so it falls short of score 4.	3 / 5
Completeness	The 'what' is clear ('financial signal extraction ... for sentiment-driven trading strategies') but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the rubric caps completeness at 3.	3 / 5
Trigger Term Quality	Natural platform keywords 'Twitter/X, Telegram, Discord, and Reddit' plus 'sentiment' give good coverage a user would actually say, but synonyms such as 'sentiment analysis', 'FinTwit', or 'social trading signals' are missing, keeping it below score 5.	4 / 5
Distinctiveness Conflict Risk	The finance-plus-social-media-plus-sentiment-trading niche with named platforms is mostly distinct with minimal conflict risk, though it could still lightly overlap with a general trading-strategy or general social-media-collection skill, so it is not a clean score 5.	4 / 5
	Total	14 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (1296 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: HKUDS/Vibe-Trading
Path: agent/src/skills/social-media-intelligence/SKILL.md
Commit: 1907e47

Reviewed: 1 day ago

Table of Contents

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