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Agent skill

Content Angle Ranker

by Affitor in Affitor/affiliate-skills

Rank content angles by engagement data, competition level, and platform fit.

MITAuto-check passedWriting & Content

Install Content Angle Ranker

skills CLI
$ npx skills add Affitor/affiliate-skills --skill content-angle-ranker -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Affitor/affiliate-skills content-angle-ranker --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Affitor/affiliate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/content-angle-ranker .claude/skills/content-angle-ranker && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
content-angle-ranker
GitHub stars
699
Token cost
~4.8k tokens
SKILL.md length
1,463 words
Files
1
Skills in repo
50
Repo updated
First seen
Licence
MIT

At a glance

Rank content angles by engagement data, competition level, and platform fit.

  • Works in 5 steps: Gather Engagement Data → Generate Angle Candidates (8-12) → Score Each Angle → …
  • The user has a keyword
  • SKILL.md covers Stage, When to Use, Input Schema and Workflow, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Content Angle Ranker is an agent skill from Affitor/affiliate-skills. Rank content angles by engagement data, competition level, and platform fit. Data-driven angle selection instead of guesswork. Use this skill when the user has a keyword or product and needs to decide WHAT to create, which angle to take, which format to use, or which platform to target. Triggers on: "what angle should I use", "rank content ideas for [keyword]", "best angle for [product]", "which content idea will perform best", "help me pick an angle", "what should I write about", "content angle for [topic]"…

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

It sits in Writing & Content, covering Content strategy. It works with TikTok. The repository describes itself as: 50 AI agent skills for affiliate marketing. Research trending content, write data-backed posts, generate infographics, build landing pages, deploy — full flywheel with social… The licence is MIT.

When your agent uses it

  • The user has a keyword
  • Product and needs to decide WHAT to create
  • Which angle to take
  • Which format to use

Example prompts

  • “what angle should I use”
  • “rank content ideas for [keyword]”
  • “best angle for [product]”
  • “/content-angle-ranker”

Requirements

  • Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Gather Engagement Data
  2. Generate Angle Candidates (8-12)
  3. Score Each Angle
  4. Rank and Add Difficulty/Time Estimates
  5. Self-Validation

What it can do on your machine

Read from SKILL.md and the folder at commit e43bfae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

    From compatibility in the SKILL.md frontmatter.

Context cost

Content Angle Ranker loads about 4.8k tokens when it runs. Until then it costs about 197 tokens; SKILL.md has 1,463 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~197
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Affitor/affiliate-skills at commit e43bfae, republished under its MIT licence (© Affitor). 1,463 words, ~4,814 tokens.

Download SKILL.mdSave it as .claude/skills/content-angle-ranker/SKILL.md (or your agent's skills folder).
name
content-angle-ranker
description
Rank content angles by engagement data, competition level, and platform fit. Data-driven angle selection instead of guesswork. Use this skill when the user has a keyword or product and needs to decide WHAT to create, which angle to take, which format to use, or which platform to target. Triggers on: "what angle should I use", "rank content ideas for [keyword]", "best angle for [product]", "which content idea will perform best", "help me pick an angle", "what should I write about", "content angle for [topic]", "rank my content ideas", "which approach will get the most views", "data-driven content planning", "angle ranker", "content scoring", "which hook should I use", "compare these content ideas", "prioritize my content angles", "what video should I make".
compatibility
Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent
license
MIT
version
1.0.0
tags
affiliate-marketing, research, content-creation, data-driven, ranking, angle-selection
metadata.author
affitor
metadata.version
1.0
metadata.stage
S1-Research

Content Angle Ranker

You have a keyword. You know the niche. But what specific content should you create? Which angle, format, and hook will actually perform? This skill answers that question with data — not gut feeling.

It takes engagement data (from trending-content-scout or live research) and ranks 8-12 content angle candidates by a weighted score combining platform fit, competition level, engagement prediction, and creator fit. The output is a prioritized list with a clear recommendation and direct handoff to content creation skills.

Think of it as /plan-ceo-review from gstack, but for content strategy: "What is the 10-star version of this content?" — except the answer is backed by engagement data.

Stage

This skill belongs to Stage S1: Research — but it bridges directly into S2: Content Creation.

When to Use

  • After trending-content-scout ran — use its data to pick the best angle
  • User has a product/keyword but doesn't know what content to create
  • User has multiple content ideas and wants to prioritize by data
  • User wants to know: "If I only have time for ONE piece of content, what should it be?"
  • Before running any S2 content skill (viral-post-writer, tiktok-script-writer, etc.)

Input Schema

yaml
keyword: string               # (required if no scout_data) "AI video tools"
product: object                # (optional) Affiliate product being promoted
  name: string                 # "HeyGen"
  description: string          # What it does
  url: string                  # Product URL or affiliate link
  reward_value: string         # Commission info — never shown in content
platform: string               # (required) Target platform for content creation
                               # "youtube" | "tiktok" | "linkedin" | "x" | "reddit" | "blog"
creator_strengths: string[]    # (optional) What the user is good at
                               # "storytelling" | "technical" | "humor" | "authority" |
                               # "visual" | "data" | "personal_experience"
audience: string               # (optional) Target audience — "beginners", "developers", "small business owners"
time_budget: string            # (optional) "30min" | "2hours" | "1day" — affects difficulty filter
custom_angles: string[]        # (optional) User's own angle ideas to include in ranking

Auto-detection: If trending-content-scout ran earlier in the conversation, its output is automatically used as the data foundation. No need to pass it explicitly.

Workflow

Step 1: Gather Engagement Data

If trending-content-scout output exists in context:

  • Use pattern_analysis (winning formats, hooks, engagement benchmarks)
  • Use content_gaps as angle candidates
  • Use top_content for competition assessment
  • Skip to Step 2

If no scout data: Run a quick scout internally:

  1. web_search "[keyword] site:youtube.com" → top 10 videos, note formats and view counts
  2. web_search "[keyword] site:tiktok.com" OR web_search "[keyword] tiktok viral" → top TikTok content
  3. web_search "[keyword] site:reddit.com top" → top Reddit discussions
  4. web_search "[keyword] [platform] best performing" → meta-analysis of what works
  5. Extract: dominant formats, popular hooks, view count ranges, gaps

This takes 30-60 seconds and provides enough signal for angle scoring.

Step 2: Generate Angle Candidates (8-12)

Generate 8-12 specific content angle candidates. Each angle must be concrete enough to become a title — not vague ("write about HeyGen") but specific ("HeyGen vs Synthesia: I tested both for 30 days — honest comparison for solo creators").

Sources for angles:

  1. Gap-based angles (from scout data or web_search):

    • Content gaps: topics nobody has covered well
    • Format gaps: popular topic but missing in a specific format (e.g., comparison exists on YouTube but not TikTok)
    • Audience gaps: existing content targets general audience, specific audience underserved
    • Recency gaps: existing content is outdated, fresh version needed

  2. Pattern-based angles (from winning formats):

    • Take the winning format and apply it to the keyword
    • Combine the best hook type with the topic
    • Replicate the structure of the highest-engagement content with a fresh perspective

  3. Contrarian angles:

    • If all content is positive → honest cons angle
    • If all content targets beginners → advanced user angle
    • If all content is listicles → deep single-product dive

  4. User-provided angles (from custom_angles):

    • Include any angles the user suggested
    • Score them alongside generated candidates — no bias

For each angle, define:

yaml
Angle:
  title: string               # Specific, could be an actual content title
  angle: string               # Brief description of the angle
  format: string              # "comparison" | "review" | "tutorial" | "listicle" | "demo" | "story" | "reaction" | "explainer"
  hook: string                # The actual hook/opening line
  hook_type: string           # "question" | "shock" | "bold_claim" | "demo_first" | "relatable" | "contrarian"
  source: string              # "gap" | "pattern" | "contrarian" | "user_provided"
Step 3: Score Each Angle

Score every angle on 4 dimensions (1-10 each), then calculate a weighted total:

angle_score = (platform_fit × 0.25) + (competition_level × 0.30) +
              (engagement_prediction × 0.30) + (creator_fit × 0.15)

Dimension 1: Platform Fit (weight: 25%)

How well does this format/hook work on the target platform?

FormatYouTubeTikTokLinkedInXRedditBlog
comparison987589
review865499
tutorial9763710
listicle789868
demo8105435
story6910877
reaction7104653
explainer858689

Adjust based on actual scout data if available (if comparisons outperform on a platform where they usually don't, use the real data instead of the default table).

Dimension 2: Competition Level (weight: 30%)

How many similar content pieces already exist? Higher score = LESS competition.

IF scout data available:
  Count how many top_content pieces match this angle's format + similar topic
  10 = zero similar content found (blue ocean)
  7-9 = 1-3 similar pieces (low competition)
  4-6 = 4-10 similar pieces (moderate competition)
  1-3 = 10+ similar pieces (saturated)
  
IF no scout data:
  web_search for the exact angle title → count results
  Fewer results with exact match = higher score

Dimension 3: Engagement Prediction (weight: 30%)

How likely is this angle to get high engagement based on data?

IF scout data available:
  Look at engagement scores of similar formats and hooks in top_content
  If this angle's format has avg_engagement > median → higher score
  If this angle's hook_type has avg_engagement > median → higher score
  Combine: angle uses top format + top hook → 9-10
  Angle uses average format + average hook → 5-6
  Angle uses underperforming format → 3-4

IF no scout data:
  Use platform defaults and general engagement patterns
  Comparisons generally outperform reviews → 8 vs 6
  Bold claim hooks generally outperform questions → 8 vs 6

Dimension 4: Creator Fit (weight: 15%)

How well does this angle match the creator's strengths?

IF creator_strengths provided:
  "storytelling" → story format, relatable hooks → high fit
  "technical" → tutorial format, demo hooks → high fit
  "humor" → reaction format, relatable hooks → high fit
  "authority" → review format, bold claim hooks → high fit
  "visual" → demo format, demo_first hooks → high fit
  "data" → comparison format, explainer → high fit
  "personal_experience" → story format, reaction → high fit
  
  Match count: 2+ matches → 9-10, 1 match → 6-7, 0 matches → 4-5

IF no creator_strengths:
  Default all angles to 7 (neutral)
Step 4: Rank and Add Difficulty/Time Estimates

Sort angles by angle_score descending.

For each angle, estimate:

yaml
difficulty: string       # "easy" | "medium" | "hard"
estimated_time: string   # "30 min" | "1-2 hours" | "half day" | "full day"

Difficulty mapping:

  • easy: Listicle, simple reaction, short demo, Twitter thread → 30-60 min
  • medium: Comparison (need 2 products), tutorial with steps, story post → 1-3 hours
  • hard: Deep review with testing, data-driven explainer, long-form video → 3+ hours

If time_budget is provided, flag angles that exceed the budget.

Step 5: Self-Validation

Before presenting output, verify:

  • At least 8 angles generated with concrete titles (not vague descriptions)
  • Scores are differentiated (not all 7.0-7.5 — spread them out)
  • Top angle is clearly justified by data, not arbitrary
  • At least 2 gap-based angles included (differentiation opportunities)
  • Difficulty estimates are realistic
  • Next steps reference specific downstream skills with parameters

If any check fails, fix the output before delivering. Do not flag the checklist to the user.

Output Schema

yaml
output_schema_version: "1.0.0"
keyword: string
platform: string
data_source: string              # "trending-content-scout output" | "quick web_search scan"
angles_generated: number         # 8-12
top_angle:
  title: string
  angle: string
  format: string
  hook: string
  hook_type: string
  score: number
  why: string                    # data-backed reasoning
  difficulty: string
  estimated_time: string
all_angles:
  - title: string
    angle: string
    format: string
    hook: string
    hook_type: string
    platform_fit: number
    competition_level: number
    engagement_prediction: number
    creator_fit: number
    score: number
    difficulty: string
    estimated_time: string
    source: string               # "gap" | "pattern" | "contrarian" | "user_provided"
recommended_next_skill: string   # "viral-post-writer" | "tiktok-script-writer" | etc.
recommended_skill_params:        # ready-to-use parameters for the next skill
  format: string
  hook_style: string
  angle: string

Output Format

markdown
## Content Angle Ranker: [Keyword] on [Platform]

### Data Foundation
📊 Based on: [trending-content-scout output (20 pieces analyzed) | quick web_search scan]

---

### 🥇 #1 Recommended Angle — Score: [X.X]/10

**"[Specific Title]"**

| Dimension | Score | Evidence |
|-----------|-------|----------|
| Platform Fit | X/10 | [Format] works well on [Platform] — [data point] |
| Competition | X/10 | [Number] similar pieces exist — [assessment] |
| Engagement Prediction | X/10 | [Format] + [hook] averages [X] engagement in this niche |
| Creator Fit | X/10 | Matches your strengths in [X] |
| **Total** | **X.X/10** | |

- **Format:** [comparison] | **Hook:** [bold_claim]
- **Opening line:** "[Actual hook sentence]"
- **Difficulty:** [medium] | **Time:** [1-2 hours]
- **Why this wins:** [2-3 sentences of data-backed reasoning]

---

### 🥈 #2 — "[Title]" — Score: [X.X]/10
- Format: [X] | Hook: [X] | Competition: [X/10] | Time: [X]
- **Why:** [1 sentence]

### 🥉 #3 — "[Title]" — Score: [X.X]/10
- Format: [X] | Hook: [X] | Competition: [X/10] | Time: [X]
- **Why:** [1 sentence]

---

### All Angles Ranked

| # | Title | Format | Hook | Plat. Fit | Comp. | Eng. Pred. | Creator | Score | Time |
|---|-------|--------|------|-----------|-------|------------|---------|-------|------|
| 1 | ... | comparison | bold_claim | 9 | 8 | 9 | 8 | 8.7 | 2h |
| 2 | ... | demo | demo_first | 10 | 7 | 8 | 7 | 8.0 | 1h |
| 3 | ... | story | relatable | 9 | 6 | 7 | 9 | 7.5 | 1h |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

---

### ⚡ Quick Win vs Best Bet

| Strategy | Angle | Score | Time | Best For |
|----------|-------|-------|------|----------|
| **Quick Win** | [Easiest high-scoring angle] | X.X | 30min | "I want to ship something today" |
| **Best Bet** | [Highest scoring angle] | X.X | 2h | "I want the best possible content" |
| **Contrarian** | [Highest-scoring contrarian] | X.X | Xh | "I want to stand out from everyone" |

---

### 🚀 Next Steps

**To create Angle #1:**

Skill: [viral-post-writer | tiktok-script-writer | affiliate-blog-builder] Parameters: product: [product name] format: [comparison] hook_style: [bold_claim] angle: "[specific angle description]" platform: [platform]


**Alternative paths:**
- `tiktok-script-writer` — for short-form video version of Angle #1
- `content-pillar-atomizer` — create a blog post, then atomize across all platforms
- `comparison-post-writer` — if the winning angle is a comparison
Show full SKILL.md (633 more words)Show less

Error Handling

  • No scout data and no keyword: Ask user: "What topic or product are you creating content for? And which platform?"
  • Only 1 platform specified + limited data: Generate angles anyway using platform-specific defaults. Note: "Limited data available. Scores are based on general platform patterns. Run trending-content-scout first for data-backed scoring."
  • All angles score similarly (within 0.5 points): Spread them out by double-weighting the most differentiating dimension. Present as: "These angles are closely matched. The tiebreaker is [competition/creator fit/etc.]."
  • User's custom angles score low: Still include them but be honest: "Your angle '[X]' scored [X.X/10] — competition is high and the format doesn't match platform trends. Consider the #1 angle instead, or combine your angle with a [winning format]."
  • Time budget too short for any good angle: Recommend the easiest angle regardless of score, and flag: "With [30 min], your best option is [quick angle]. For higher impact, allocate [2 hours] for [best angle]."

Examples

Example 1: User: "I want to promote HeyGen on TikTok. What angle should I use?" → keyword: "HeyGen", platform: "tiktok" → Quick scout: web_search "HeyGen tiktok" → mostly demo-first content, 30-45s → Generate 10 angles: "HeyGen vs Synthesia comparison", "I made a $2000 video for free with HeyGen", "POV: your boss asks for a video and you use AI", "HeyGen for real estate agents", etc. → Top angle: "I replaced a $2000 video production with HeyGen" — Score: 8.7 (bold_claim hook, demo format, low competition on TikTok, high engagement predicted) → Next: tiktok-script-writer with hook_style: bold_claim, duration: 45s

Example 2: User: "Rank these content ideas for my YouTube channel about email marketing:

  1. ConvertKit vs Mailchimp comparison
  2. How I grew my list to 10K subscribers
  3. Top 5 email marketing mistakes" → platform: "youtube", custom_angles provided → Scout YouTube for email marketing content → Score all 3 + generate 5 additional angles → Result: "How I grew to 10K" scores highest (story format + bold_claim = 8.5) because competition for comparisons is saturated (score: 6.2) and listicles are average (7.1) → The user's story angle wins — with a suggested hook: "I went from 0 to 10K subscribers in 6 months. Here's what nobody tells you."

Example 3: User: "I'm good at storytelling and humor. What should I create about AI writing tools on LinkedIn?" → creator_strengths: ["storytelling", "humor"], platform: "linkedin" → Generate angles weighted toward story format → Top: "I let AI write my LinkedIn posts for a week. My boss noticed." — Score: 9.1 (story format, relatable hook, low competition on LinkedIn for this angle, perfect creator fit)

Feedback & Issue Reporting

When this skill produces unexpected, incomplete, or incorrect output, generate a skill_feedback block (see shared/references/feedback-protocol.md for full schema).

Skill-specific failure modes:

  • All angles score within 0.5 points: Scoring not differentiated enough. Report as wrong_output with the scores.
  • Top angle is generic: "Write a review of X" instead of a specific, titled angle. Report as data_quality.
  • Downstream skill can't use recommended_skill_params: Schema mismatch. Report as chain_break.

Auto-detect triggers:

  • Score range (max - min) < 1.0 across all angles
  • <8 angles generated
  • recommended_skill_params missing required fields for the suggested next skill

Report issues: GitHub Issues | Discussions

References

  • shared/references/social-data-providers.md — API configuration and engagement score formula
  • shared/references/flywheel-connections.md — master flywheel connection map
  • shared/references/platform-rules.md — platform-specific content guidelines
  • shared/references/feedback-protocol.md — issue detection and reporting standard

Flywheel Connections

Feeds Into
  • viral-post-writer (S2) — recommended_angle with format, hook, and parameters
  • tiktok-script-writer (S2) — TikTok-specific angle with duration and hook
  • twitter-thread-writer (S2) — X-specific angle with hook
  • reddit-post-writer (S2) — Reddit-specific angle with subreddit suggestion
  • affiliate-blog-builder (S3) — blog angle with SEO keyword alignment
  • comparison-post-writer (S3) — if winning angle is a comparison
  • content-pillar-atomizer (S2) — angle as the pillar topic to atomize
Fed By
  • trending-content-scout (S1) — engagement data, patterns, gaps, benchmarks
  • niche-opportunity-finder (S1) — niche context for angle generation
  • competitor-spy (S1) — competitor gaps to exploit
  • performance-report (S6) — historical angle performance data
Feedback Loop
  • S6 performance-report shows which angles actually performed → update scoring weights and format preferences for next run → content strategy improves with every cycle
yaml
chain_metadata:
  skill_slug: "content-angle-ranker"
  stage: "research"
  timestamp: string
  suggested_next:
    - "viral-post-writer"
    - "tiktok-script-writer"
    - "affiliate-blog-builder"

© Affitor, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/research/content-angle-ranker of Affitor/affiliate-skills.

Open the folder on GitHubat commit e43bfae

Compare with similar skills

Content Angle Ranker next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Content Angle Ranker compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Content Angle Ranker this skillAffitor/affiliate-skills699—~4.8kAutomated safety check: PassMIT
Caption Writerstevenflanagan1/social-ai-team241—~2.9kAutomated safety check: PassNone
Content Calendarstevenflanagan1/social-ai-team241—~3.1kAutomated safety check: PassNone
Content Enginec0x12c/ai-toolkit106—~986Automated safety check: PassNone
Social Media Managementmanojbajaj95/claude-gtm-plugin1051 repos~3.9kAutomated safety check: PassMIT
Content Enginecohen-liel/hivemind1106 repos~643Automated safety check: PassApache-2.0

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Works with

Categories

Questions about Content Angle Ranker

What does Content Angle Ranker do?

Rank content angles by engagement data, competition level, and platform fit. Content Angle Ranker is an agent skill from Affitor/affiliate-skills. Rank content angles by engagement data, competition level, and platform fit.

When should I use Content Angle Ranker?

Content Angle Ranker fits situations like: the user has a keyword; product and needs to decide WHAT to create; which angle to take; which format to use.

How do I install Content Angle Ranker in Claude Code?

Run `npx skills add Affitor/affiliate-skills --skill content-angle-ranker -a claude-code`. Or copy the skill folder (skills/research/content-angle-ranker in Affitor/affiliate-skills) into .claude/skills/content-angle-ranker in your project. Claude Code loads it when a task matches its description.

How do I install Content Angle Ranker in Codex?

Run `npx skills add Affitor/affiliate-skills --skill content-angle-ranker -a codex`. Or copy the skill folder (skills/research/content-angle-ranker in Affitor/affiliate-skills) into .agents/skills/content-angle-ranker in your project. Codex loads it when a task matches its description.

Can I use Content Angle Ranker in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Affitor/affiliate-skills --skill content-angle-ranker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/content-angle-ranker, .gemini/skills/content-angle-ranker, .github/skills/content-angle-ranker and .opencode/skills/content-angle-ranker in your project.

What does Content Angle Ranker need to run?

SKILL.md names no scripts, command-line tools or credentials: Content Angle Ranker is instructions for the agent only. Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent.

Does Content Angle Ranker access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Content Angle Ranker safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Content Angle Ranker use?

Content Angle Ranker is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Content Angle Ranker use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Content Angle Ranker?

Skills that share tags, products or a category with Content Angle Ranker: Caption Writer (stevenflanagan1/social-ai-team, 241 stars), Content Calendar (stevenflanagan1/social-ai-team, 241 stars), Content Engine (c0x12c/ai-toolkit, 106 stars) and Social Media Management (manojbajaj95/claude-gtm-plugin, 105 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Content Angle Ranker?

Affitor (a GitHub organization) maintains it in Affitor/affiliate-skills, which has 699 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on September 15, 2026.

Source: Affitor/affiliate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.