social-media-intelligence

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

Quality

63%

Does it follow best practices?

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Securityby

Low

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

Quality

Content

50%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 concrete, near-executable code and quantified thresholds throughout, but it is a monolithic ~1300-line reference manual inlined into SKILL.md with no progressive disclosure or bundle files. Sections 1 and parts of 3–5 pad the token budget with background knowledge Claude already has, and the end-to-end workflow is implicit rather than sequenced with validation checkpoints.

Suggestions

Split the monolith into a lean SKILL.md overview plus one-level-deep reference files (e.g. references/collectors.md, references/sentiment-scoring.md, references/factor-testing.md, references/platform-playbooks.md) so per-platform detail loads only when needed.

Cut or compress Section 1's platform-ecology tables and generic 'Characteristics' notes — Claude already knows what Reddit or FinTwit are; keep only the signal-value judgments that are non-obvious.

Add an explicit numbered end-to-end workflow (collect → validate API responses → score → aggregate → IC/ICIR test) with error-recovery checkpoints for API failures and rate limits, since collection is a batch operation.

Dimension	Reasoning	Score
Conciseness	The ~1300-line body includes several padded, background-explaining sections — the Section 1 platform-ecology tables (FinTwit roles, subreddit user bases), 'Characteristics' notes, and verbose boilerplate docstrings — that explain context Claude already knows or could infer. This is noticeably verbose rather than merely having a few trimmable spots, so it sits at anchor 2 rather than 3.	2 / 5
Actionability	Mostly executable guidance: complete Python snippets for each platform's collector, VADER/FinBERT scorers, IC/ICIR factor tests, JSON schemas, an env-var block, and a SQL table. Minor gaps prevent a 5 — the unified interface calls undefined `_collect_twitter`-style helpers, the LLM scorer imports a non-existent `src.providers.base`, and several snippets omit `import os`.	4 / 5
Workflow Clarity	The section order implies a pipeline (collect → score sentiment → buzz/fear-greed → factor construction → platform-specific analysis), but there is no explicit numbered workflow tying the stages together, and batch data-collection operations have no validation or error-recovery checkpoints (e.g. no verify-step after collection or API-failure retry loop). Per the rubric, missing validation in batch workflows caps this at 3.	3 / 5
Progressive Disclosure	The body has reasonable section structure (7 numbered sections, tables, consistent per-platform layout), but there are no bundle files at all — per-platform collectors, sentiment methodology, and factor math that clearly belong in separate reference files are all inlined in SKILL.md. This matches 'some structure... content that should be separate is inline'; it avoids a 2 only because headers and organization are genuinely good.	3 / 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 is specific and appropriately scoped, clearly naming the domain, the four target platforms, and the trading application. Its main weakness is the complete absence of a 'Use when...' trigger clause, which caps completeness and leaves invocation guidance implicit.

Suggestions

Add an explicit trigger clause, e.g. 'Use when monitoring Twitter/X, Telegram, Discord, or Reddit for trading signals, or when the user mentions social sentiment, FinTwit, or wallstreetbets.'

List the concrete capabilities more fully (e.g. collect posts via platform APIs, score sentiment with VADER/FinBERT, build buzz and fear-greed indicators, test factors via IC/ICIR) to raise specificity.

Include a few natural synonyms users would say — 'FinTwit', 'WSB', 'crypto Telegram channels', 'social sentiment analysis' — to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Names the domain ("social media intelligence", "financial signal extraction") and one concrete action (signal extraction) across four named platforms, matching the 'names domain and 1-2 concrete actions' anchor. It does not list several distinct actions (collecting, scoring sentiment, building factors), so it falls short of anchor 4.	3 / 5
Completeness	The "what" is clear (financial signal extraction from four platforms for sentiment-driven trading), but there is no "Use when..." or equivalent trigger clause, so the "when" is only weakly implied. Per the rubric guideline, a missing explicit trigger clause caps completeness at 3.	3 / 5
Trigger Term Quality	Includes natural terms users would say — "Twitter/X", "Telegram", "Discord", "Reddit", "sentiment", "trading strategies" — giving good keyword coverage. A few natural variations are missing (e.g. "FinTwit", "wallstreetbets"/"WSB", "crypto signals", "social sentiment"), keeping it below anchor 5.	4 / 5
Distinctiveness Conflict Risk	The financial-signals-from-social-media niche with four named platforms is mostly distinct with clear triggers. Minor overlap risk remains with generic sentiment-analysis or data-collection skills, so it is not the fully-conflict-free anchor 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 (1306 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: 251b094

Reviewed: 3 days ago

Table of Contents

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