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Octolens

Query and analyze brand mentions from Octolens API. Use when the user wants to fetch mentions, track keywords, filter by source platforms (Twitter, Reddit, GitHub, LinkedIn, etc.), sentiment analysis, or analyze social media engagement. Supports complex filtering with AND/OR logic, date ranges, follower counts, and bookmarks.

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Query and analyze brand mentions from Octolens API. Use when the user wants to fetch mentions, track keywords, filter by source platforms (Twitter, Reddit, GitHub, LinkedIn, etc.), sentiment analysis, or analyze social media engagement. Supports complex filtering with AND/OR logic, date ranges, follower counts, and bookmarks.

โฌ‡ 0 downloads โ˜… 0 stars Unverified but indexed

Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.md, references/EXAMPLES.md, references/FILTERS.md, scripts/README.md, scripts/advanced-query.js, scripts/fetch-mentions.js

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 28 sections Open source page

When to use this skill

Use this skill when the user needs to: Fetch brand mentions from social media and other platforms Filter mentions by source (Twitter, Reddit, GitHub, LinkedIn, YouTube, HackerNews, DevTO, StackOverflow, Bluesky, newsletters, podcasts) Analyze sentiment (positive, neutral, negative) Filter by author follower count or engagement Search for specific keywords or tags Query mentions by date range List available keywords or saved views Apply complex filtering logic with AND/OR conditions

API Authentication

The Octolens API requires a Bearer token for authentication. The user should provide their API key, which you'll use in the Authorization header: Authorization: Bearer YOUR_API_KEY Important: Always ask the user for their API key before making any API calls. Store it in a variable for subsequent requests.

Base URL

All API endpoints use the base URL: https://app.octolens.com/api/v1

Rate Limits

Limit: 500 requests per hour Check headers: X-RateLimit-* headers indicate current usage

1. POST /mentions

Fetch mentions matching keywords with optional filtering. Returns posts sorted by timestamp (newest first). Key Parameters: limit (number, 1-100): Maximum results to return (default: 20) cursor (string): Pagination cursor from previous response includeAll (boolean): Include low-relevance posts (default: false) view (number): View ID to use for filtering filters (object): Filter criteria (see filtering section) Example Response: { "data": [ { "id": "abc123", "url": "https://twitter.com/user/status/123", "body": "Just discovered @YourProduct - this is exactly what I needed!", "source": "twitter", "timestamp": "2024-01-15T10:30:00Z", "author": "user123", "authorName": "John Doe", "authorFollowers": 5420, "relevance": "relevant", "sentiment": "positive", "language": "en", "tags": ["feature-request"], "keywords": [{ "id": 1, "keyword": "YourProduct" }], "bookmarked": false, "engaged": false } ], "cursor": "eyJsYXN0SWQiOiAiYWJjMTIzIn0=" }

2. GET /keywords

List all keywords configured for the organization. Example Response: { "data": [ { "id": 1, "keyword": "YourProduct", "platforms": ["twitter", "reddit", "github"], "color": "#6366f1", "paused": false, "context": "Our main product name" } ] }

3. GET /views

List all saved views (pre-configured filters). Example Response: { "data": [ { "id": 1, "name": "High Priority", "icon": "star", "filters": { "sentiment": ["positive", "negative"], "source": ["twitter"] }, "createdAt": "2024-01-01T00:00:00Z" } ] }

Filtering Mentions

The /mentions endpoint supports powerful filtering with two modes:

Simple Mode (Implicit AND)

Put fields directly in filters. All conditions are ANDed together. { "filters": { "source": ["twitter", "linkedin"], "sentiment": ["positive"], "minXFollowers": 1000 } } โ†’ source IN (twitter, linkedin) AND sentiment = positive AND followers โ‰ฅ 1000

Exclusions

Prefix any array field with ! to exclude values: { "filters": { "source": ["twitter"], "!keyword": [5, 6] } } โ†’ source = twitter AND keyword NOT IN (5, 6)

Advanced Mode (AND/OR Groups)

Use operator and groups for complex logic: { "filters": { "operator": "AND", "groups": [ { "operator": "OR", "conditions": [ { "source": ["twitter"] }, { "source": ["linkedin"] } ] }, { "operator": "AND", "conditions": [ { "sentiment": ["positive"] }, { "!tag": ["spam"] } ] } ] } } โ†’ (source = twitter OR source = linkedin) AND (sentiment = positive AND tag โ‰  spam)

Available Filter Fields

FieldTypeDescriptionsourcestring[]Platforms: twitter, reddit, github, linkedin, youtube, hackernews, devto, stackoverflow, bluesky, newsletter, podcastsentimentstring[]Values: positive, neutral, negativekeywordstring[]Keyword IDs (get from /keywords endpoint)languagestring[]ISO 639-1 codes: en, es, fr, de, pt, it, nl, ja, ko, zhtagstring[]Tag namesbookmarkedbooleanFilter bookmarked (true) or non-bookmarked (false) postsengagedbooleanFilter engaged (true) or non-engaged (false) postsminXFollowersnumberMinimum Twitter follower countmaxXFollowersnumberMaximum Twitter follower countstartDatestringISO 8601 format (e.g., "2024-01-15T00:00:00Z")endDatestringISO 8601 format

Using the Bundled Scripts

This skill includes helper scripts for common operations. Use them to quickly interact with the API:

Fetch Mentions

node scripts/fetch-mentions.js YOUR_API_KEY [limit] [includeAll]

List Keywords

node scripts/list-keywords.js YOUR_API_KEY

List Views

node scripts/list-views.js YOUR_API_KEY

Custom Filter Query

node scripts/query-mentions.js YOUR_API_KEY '{"source": ["twitter"], "sentiment": ["positive"]}' [limit]

Advanced Query

node scripts/advanced-query.js YOUR_API_KEY [limit]

Best Practices

Always ask for the API key before making requests Use views when possible to leverage pre-configured filters Start with simple filters and add complexity as needed Check rate limits in response headers (X-RateLimit-*) Use pagination with cursor for large result sets Dates must be ISO 8601 format (e.g., "2024-01-15T00:00:00Z") Get keyword IDs from /keywords endpoint before filtering by keyword Use exclusions (!) to filter out unwanted content Combine includeAll=false with relevance filtering for quality results

Find positive Twitter mentions with high followers

{ "limit": 20, "filters": { "source": ["twitter"], "sentiment": ["positive"], "minXFollowers": 1000 } }

Exclude spam and get Reddit + GitHub mentions

{ "limit": 50, "filters": { "source": ["reddit", "github"], "!tag": ["spam", "irrelevant"] } }

Complex query: (Twitter OR LinkedIn) AND positive sentiment, last 7 days

{ "limit": 30, "filters": { "operator": "AND", "groups": [ { "operator": "OR", "conditions": [ { "source": ["twitter"] }, { "source": ["linkedin"] } ] }, { "operator": "AND", "conditions": [ { "sentiment": ["positive"] }, { "startDate": "2024-01-20T00:00:00Z" } ] } ] } }

Error Handling

StatusErrorDescription401unauthorizedMissing or invalid API key403forbiddenValid key but no permission404not_foundResource (e.g., view ID) not found429rate_limit_exceededToo many requests400invalid_requestMalformed request body500internal_errorServer error, retry later

Step-by-Step Workflow

When a user asks to query Octolens data: Ask for API key if not already provided Understand the request: What are they looking for? Determine filters needed: Source, sentiment, date range, etc. Check if a view applies: List views first if user mentions saved filters Build the query: Use simple mode first, advanced mode for complex logic Execute the request: Use bundled Node.js scripts or fetch API directly Parse results: Extract key information (author, body, sentiment, source) Handle pagination: If more results needed, use cursor from response Present findings: Summarize insights, highlight patterns

Example 1: Simple Query

User: "Show me positive mentions from Twitter in the last 7 days" Action (using bundled script): node scripts/query-mentions.js YOUR_API_KEY '{"source": ["twitter"], "sentiment": ["positive"], "startDate": "2024-01-20T00:00:00Z"}' Alternative (using fetch API directly): const response = await fetch('https://app.octolens.com/api/v1/mentions', { method: 'POST', headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json', }, body: JSON.stringify({ limit: 20, filters: { source: ['twitter'], sentiment: ['positive'], startDate: '2024-01-20T00:00:00Z', }, }), }); const data = await response.json();

Example 2: Advanced Query

User: "Find mentions from Reddit or GitHub, exclude spam tag, with positive or neutral sentiment" Action (using bundled script): node scripts/query-mentions.js YOUR_API_KEY '{"operator": "AND", "groups": [{"operator": "OR", "conditions": [{"source": ["reddit"]}, {"source": ["github"]}]}, {"operator": "OR", "conditions": [{"sentiment": ["positive"]}, {"sentiment": ["neutral"]}]}, {"operator": "AND", "conditions": [{"!tag": ["spam"]}]}]}' Alternative (using fetch API directly): const response = await fetch('https://app.octolens.com/api/v1/mentions', { method: 'POST', headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json', }, body: JSON.stringify({ limit: 30, filters: { operator: 'AND', groups: [ { operator: 'OR', conditions: [ { source: ['reddit'] }, { source: ['github'] }, ], }, { operator: 'OR', conditions: [ { sentiment: ['positive'] }, { sentiment: ['neutral'] }, ], }, { operator: 'AND', conditions: [ { '!tag': ['spam'] }, ], }, ], }, }), }); const data = await response.json();

Example 3: Get Keywords First

User: "Show mentions for our main product keyword" Actions: First, list keywords: node scripts/list-keywords.js YOUR_API_KEY Then query mentions with the keyword ID: node scripts/query-mentions.js YOUR_API_KEY '{"keyword": [1]}'

Tips for Agents

Use bundled scripts: The Node.js scripts handle JSON parsing automatically Cache keywords: After fetching keywords once, remember them for the session Explain filters: When using complex filters, explain the logic to the user Show examples: When users are unsure, show example filter structures Paginate wisely: Ask if user wants more results before fetching next page Summarize insights: Don't just dump data, provide analysis (sentiment trends, top authors, platform distribution)

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
4 Docs2 Scripts
  • SKILL.md Primary doc
  • references/EXAMPLES.md Docs
  • references/FILTERS.md Docs
  • scripts/README.md Docs
  • scripts/advanced-query.js Scripts
  • scripts/fetch-mentions.js Scripts