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paw-cra-content-research

On-demand research bundle for the Aria Creative Suite. Use when any agent needs competitor analysis, trend research, or content opportunity scanning to inform visual/video production.

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Price unconfirmed★ 108 GitHub starsRegistry updated · Oct 5, 2026agent-skill

Overview

On-demand research bundle for the Aria Creative Suite. Use when any agent needs competitor analysis, trend research, or content opportunity scanning to inform visual/video production.

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Content Research Workflow

Overview

This workflow produces an actionable research bundle — competitor analysis, trend spotting, and content opportunity identification — that feeds directly into visual and video production. It is a service workflow invoked on-demand by any Aria Creative Suite agent (Strategist, Designer, Video Producer, or Aria herself) when research context is needed to inform creative decisions.

The output is not academic research. Every finding translates into a specific production recommendation: a design brief the Designer can act on, a video format the Video Producer can storyboard, a content angle with hook and platform guidance. If a finding does not lead to a "make this" recommendation, it is context, not output.

Args: Accepts --headless or -H for autonomous execution. Supports scoped research via --scope competitor, --scope trend, --scope content, or --scope all (default).

On Activation

Load available config from {project-root}/.pawbytes/config/config.yaml and {project-root}/.pawbytes/config/config.user.yaml (root level and cra section). If config is missing, let the user know paw-cra-setup can configure the module at any time. Resolve:

{user_name} (null) — address the user by name
{communication_language} (system) — use for all communications
{document_output_language} (system) — use for generated document content
{default_brand} (null) — default brand to research if none specified

Load shared agency memory from {project-root}/.pawbytes/creative-suites/index.md. If a brand context is active or specified, load {project-root}/.pawbytes/creative-suites/brands/{brand-name}/guidelines.md and any existing research in {project-root}/.pawbytes/creative-suites/brands/{brand-name}/research/.

If --headless, complete the full pipeline without interaction using the active brand and scope from args. If interactive, greet and confirm research parameters before proceeding.

Pipeline

1. Research Brief Intake

Parse the research request:

Parameter	Source	Fallback
Brand	Explicit request or `--brand` arg	`{default_brand}` or active brand from index.md
Scope	`--scope` arg or explicit request	`all` (competitor + trend + content)
Focus areas	Explicit questions or topics	Derive from brand guidelines (industry, audience, competitors)
Target platforms	Explicit or from brand guidelines	Instagram, TikTok, YouTube, LinkedIn

If interactive: confirm parameters and ask if there are specific questions or competitors to prioritize. If headless: proceed with available context.

2. Brand Context Load

Load from {project-root}/.pawbytes/creative-suites/brands/{brand-name}/:

guidelines.md — brand identity, voice, visual style, industry, audience
research/ — any prior research reports (avoid redundant work, build on existing findings)

If no brand exists at the expected path, abort with a clear message suggesting brand onboarding through Aria.

3. Competitor Scan (scope: competitor or all)

Load ./references/competitor-scan.md for detailed research guidance.

Use Exa MCP tools to analyze 3-5 competitors across:

Content strategy and posting patterns
Visual style and design language
Video formats and production quality
Platform presence and engagement signals
Messaging and positioning

Production lens: For every competitor insight, note what it means for Designer and Video Producer. "Competitor X uses bold typography overlays on Reels" is more useful than "Competitor X has strong video presence."

4. Trend Analysis (scope: trend or all)

Load ./references/trend-analysis.md for detailed research guidance.

Search for trends relevant to the brand's industry and audience:

Trending content formats (carousel styles, video templates, interactive formats)
Visual and aesthetic trends (color palettes, typography, layout patterns)
Platform-specific trends (TikTok sounds, Instagram features, YouTube formats)
Topical trends and hashtag movements

Classify each trend: Fad (<3 months), Trend (6-18 months), Movement (2+ years), Declining (avoid).

5. Content Opportunity Identification (scope: content or all)

Cross-reference competitor gaps with trending topics to find exploitable angles:

What are competitors NOT doing that audiences want?
Which trends align with the brand's strengths but competitors have not adopted?
What content formats are under-served in this niche?
Where is engagement high but content quality low (opportunity to dominate)?

Produce an angle shortlist — 5-10 specific content angles, each with:

The angle (one sentence)
Why it works (gap + trend alignment)
Suggested format (carousel, reel, long-form video, etc.)
Target platform

6. Production Recommendations

This is the most critical output section. Translate every research finding into briefs that Designer and Video Producer can act on directly.

Load ./references/production-recommendations.md for recommendation templates.

For each recommended angle, produce:

Design briefs (for Designer):

Visual concept description
Reference style (e.g., "minimalist with bold type overlay," "before/after split")
Platform and dimensions
Suggested copy direction

Video briefs (for Video Producer):

Format and duration
Hook concept (first 3 seconds)
Scene structure outline
Audio/music direction
Subtitle style

Platform-specific notes:

Optimal posting context (time, hashtags, caption strategy)
Platform feature usage (Instagram collab, TikTok stitch, YouTube Shorts)

7. Report Generation

Produce research-report.md following the structure in ./references/report-template.md.

The report consolidates all findings into a scannable document with:

Executive summary (key findings in 3-5 bullets)
Competitor landscape
Trend landscape
Angle shortlist (the actionable core)
Production recommendations (the handoff to Designer/Video Producer)
Platform-specific playbooks
Sources with URLs

8. Save to Memory

Write the report to {project-root}/.pawbytes/creative-suites/brands/{brand-name}/research/research-report.md with frontmatter:

---
created: YYYY-MM-DDTHH:MM:SSZ
brand: {brand-name}
scope: {scope}
type: research
---

If scope-specific reports were generated, also save:

competitor-analysis.md (scope: competitor or all)
trend-analysis.md (scope: trend or all)
content-opportunities.md (scope: content or all)

Append to {project-root}/.pawbytes/creative-suites/daily/YYYY-MM-DD.md:

## [Strategist] HH:MM - Content Research Complete
- Brand: {brand-name}
- Scope: {scope}
- Key findings: {2-3 bullet summary}
- Angles identified: {count}
- Report: .pawbytes/creative-suites/brands/{brand-name}/research/research-report.md

9. Handoff

If interactive: present the angle shortlist and ask which angles to prioritize for production. Suggest routing to Designer or Video Producer with the relevant briefs.

If headless: report completion and file locations. The calling agent reads the report from memory.

Research Tools

Primary: Exa MCP

Tool	Use
`web_search_exa`	Competitor discovery, trend scanning, industry analysis
`crawling_exa`	Deep page content extraction from competitor sites
`get_code_context_exa`	Technical/platform documentation lookup

Fallback: Web Search

If Exa MCP is unavailable, use the Web Search tool for the same research queries.

Optional: Agent-Browser CLI

For social media content behind login gates (Instagram feeds, TikTok For You, LinkedIn). Only use if agent-browser is available and auth sessions exist at {project-root}/.pawbytes/creative-suites/.auth/.

Quality Standards

Every finding must cite a source URL
Every insight must connect to a production recommendation
Competitor analysis focuses on content strategy, not corporate profiles
Trend classification distinguishes fads from movements
The angle shortlist is the most important output — it must be specific and actionable
Production recommendations must be detailed enough for Designer/Video Producer to start work without further research

View source ↗

File metadata

name: paw-cra-content-research
description: On-demand research bundle for the Aria Creative Suite. Use when any agent needs competitor analysis, trend research, or content opportunity scanning to inform visual/video production.

View original text

---
name: paw-cra-content-research
description: On-demand research bundle for the Aria Creative Suite. Use when any agent needs competitor analysis, trend research, or content opportunity scanning to inform visual/video production.
---

# Content Research Workflow

## Overview

This workflow produces an actionable research bundle — competitor analysis, trend spotting, and content opportunity identification — that feeds directly into visual and video production. It is a **service workflow** invoked on-demand by any Aria Creative Suite agent (Strategist, Designer, Video Producer, or Aria herself) when research context is needed to inform creative decisions.

The output is not academic research. Every finding translates into a specific production recommendation: a design brief the Designer can act on, a video format the Video Producer can storyboard, a content angle with hook and platform guidance. If a finding does not lead to a "make this" recommendation, it is context, not output.

**Args:** Accepts `--headless` or `-H` for autonomous execution. Supports scoped research via `--scope competitor`, `--scope trend`, `--scope content`, or `--scope all` (default).

## On Activation

Load available config from `{project-root}/.pawbytes/config/config.yaml` and `{project-root}/.pawbytes/config/config.user.yaml` (root level and `cra` section). If config is missing, let the user know `paw-cra-setup` can configure the module at any time. Resolve:

- `{user_name}` (null) — address the user by name
- `{communication_language}` (system) — use for all communications
- `{document_output_language}` (system) — use for generated document content
- `{default_brand}` (null) — default brand to research if none specified

Load shared agency memory from `{project-root}/.pawbytes/creative-suites/index.md`. If a brand context is active or specified, load `{project-root}/.pawbytes/creative-suites/brands/{brand-name}/guidelines.md` and any existing research in `{project-root}/.pawbytes/creative-suites/brands/{brand-name}/research/`.

If `--headless`, complete the full pipeline without interaction using the active brand and scope from args. If interactive, greet and confirm research parameters before proceeding.

## Pipeline

### 1. Research Brief Intake

Parse the research request:

| Parameter | Source | Fallback |
|-----------|--------|----------|
| **Brand** | Explicit request or `--brand` arg | `{default_brand}` or active brand from index.md |
| **Scope** | `--scope` arg or explicit request | `all` (competitor + trend + content) |
| **Focus areas** | Explicit questions or topics | Derive from brand guidelines (industry, audience, competitors) |
| **Target platforms** | Explicit or from brand guidelines | Instagram, TikTok, YouTube, LinkedIn |

If interactive: confirm parameters and ask if there are specific questions or competitors to prioritize. If headless: proceed with available context.

### 2. Brand Context Load

Load from `{project-root}/.pawbytes/creative-suites/brands/{brand-name}/`:

- `guidelines.md` — brand identity, voice, visual style, industry, audience
- `research/` — any prior research reports (avoid redundant work, build on existing findings)

If no brand exists at the expected path, abort with a clear message suggesting brand onboarding through Aria.

### 3. Competitor Scan (scope: competitor or all)

Load `./references/competitor-scan.md` for detailed research guidance.

Use Exa MCP tools to analyze 3-5 competitors across:
- Content strategy and posting patterns
- Visual style and design language
- Video formats and production quality
- Platform presence and engagement signals
- Messaging and positioning

**Production lens:** For every competitor insight, note what it means for Designer and Video Producer. "Competitor X uses bold typography overlays on Reels" is more useful than "Competitor X has strong video presence."

### 4. Trend Analysis (scope: trend or all)

Load `./references/trend-analysis.md` for detailed research guidance.

Search for trends relevant to the brand's industry and audience:
- Trending content formats (carousel styles, video templates, interactive formats)
- Visual and aesthetic trends (color palettes, typography, layout patterns)
- Platform-specific trends (TikTok sounds, Instagram features, YouTube formats)
- Topical trends and hashtag movements

Classify each trend: **Fad** (<3 months), **Trend** (6-18 months), **Movement** (2+ years), **Declining** (avoid).

### 5. Content Opportunity Identification (scope: content or all)

Cross-reference competitor gaps with trending topics to find exploitable angles:
- What are competitors NOT doing that audiences want?
- Which trends align with the brand's strengths but competitors have not adopted?
- What content formats are under-served in this niche?
- Where is engagement high but content quality low (opportunity to dominate)?

Produce an **angle shortlist** — 5-10 specific content angles, each with:
- The angle (one sentence)
- Why it works (gap + trend alignment)
- Suggested format (carousel, reel, long-form video, etc.)
- Target platform

### 6. Production Recommendations

This is the most critical output section. Translate every research finding into briefs that Designer and Video Producer can act on directly.

Load `./references/production-recommendations.md` for recommendation templates.

For each recommended angle, produce:

**Design briefs** (for Designer):
- Visual concept description
- Reference style (e.g., "minimalist with bold type overlay," "before/after split")
- Platform and dimensions
- Suggested copy direction

**Video briefs** (for Video Producer):
- Format and duration
- Hook concept (first 3 seconds)
- Scene structure outline
- Audio/music direction
- Subtitle style

**Platform-specific notes:**
- Optimal posting context (time, hashtags, caption strategy)
- Platform feature usage (Instagram collab, TikTok stitch, YouTube Shorts)

### 7. Report Generation

Produce `research-report.md` following the structure in `./references/report-template.md`.

The report consolidates all findings into a scannable document with:
- Executive summary (key findings in 3-5 bullets)
- Competitor landscape
- Trend landscape
- Angle shortlist (the actionable core)
- Production recommendations (the handoff to Designer/Video Producer)
- Platform-specific playbooks
- Sources with URLs

### 8. Save to Memory

Write the report to `{project-root}/.pawbytes/creative-suites/brands/{brand-name}/research/research-report.md` with frontmatter:

```yaml
---
created: YYYY-MM-DDTHH:MM:SSZ
brand: {brand-name}
scope: {scope}
type: research
---
```

If scope-specific reports were generated, also save:
- `competitor-analysis.md` (scope: competitor or all)
- `trend-analysis.md` (scope: trend or all)
- `content-opportunities.md` (scope: content or all)

Append to `{project-root}/.pawbytes/creative-suites/daily/YYYY-MM-DD.md`:

```markdown
## [Strategist] HH:MM - Content Research Complete
- Brand: {brand-name}
- Scope: {scope}
- Key findings: {2-3 bullet summary}
- Angles identified: {count}
- Report: .pawbytes/creative-suites/brands/{brand-name}/research/research-report.md
```

### 9. Handoff

If interactive: present the angle shortlist and ask which angles to prioritize for production. Suggest routing to Designer or Video Producer with the relevant briefs.

If headless: report completion and file locations. The calling agent reads the report from memory.

## Research Tools

### Primary: Exa MCP

| Tool | Use |
|------|-----|
| `web_search_exa` | Competitor discovery, trend scanning, industry analysis |
| `crawling_exa` | Deep page content extraction from competitor sites |
| `get_code_context_exa` | Technical/platform documentation lookup |

### Fallback: Web Search

If Exa MCP is unavailable, use the Web Search tool for the same research queries.

### Optional: Agent-Browser CLI

For social media content behind login gates (Instagram feeds, TikTok For You, LinkedIn). Only use if `agent-browser` is available and auth sessions exist at `{project-root}/.pawbytes/creative-suites/.auth/`.

## Quality Standards

- Every finding must cite a source URL
- Every insight must connect to a production recommendation
- Competitor analysis focuses on content strategy, not corporate profiles
- Trend classification distinguishes fads from movements
- The angle shortlist is the most important output — it must be specific and actionable
- Production recommendations must be detailed enough for Designer/Video Producer to start work without further research

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Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
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AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 108 stars, 19 forks; issue activity unavailable in current metadata
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Permission surface: secrets or environment access, shell or command execution
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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: pawbytes/skill-suites
License: MIT
Version: Unknown
Last GitHub push: Oct 3, 2026
Registry updated: Oct 5, 2026
Instruction path: src/creative/paw-cra-content-research/SKILL.md @ 547a6df83bd6

Version reported in registry metadata; check source releases before relying on it.

Quality

62/100

Promising

Trust

63/100

Sandbox only

Audit

75/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 108 stars, 19 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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More details

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      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 108 stars, 19 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution",
      "Review status: AI review approval is missing"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 108 stars, 19 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 62,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "8d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "mvanhorn-last30days-skill",
      "name": "Last30days Skill",
      "url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
      "stars": 63666,
      "install_command": "",
      "trust_score": 94,
      "audit_score": 95
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use paw-cra-content-research in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "pawbytes-paw-cra-content-research (paw-cra-content-research)",
      "install_command": "npx skills add pawbytes/skill-suites --skill paw-cra-content-research",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "pawbytes-paw-cra-content-research",
      "task": "Use paw-cra-content-research in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/pawbytes-paw-cra-content-research",
    "api": "https://www.openagentskill.com/api/agent/skills/pawbytes-paw-cra-content-research",
    "audit": "https://www.openagentskill.com/skills/pawbytes-paw-cra-content-research/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=pawbytes-paw-cra-content-research&task=Use%20paw-cra-content-research%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20paw-cra-content-research%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20paw-cra-content-research%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/pawbytes-paw-cra-content-research/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/pawbytes-paw-cra-content-research"
  }
}

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Creator: pawbytes
Source: pawbytes/skill-suites
Indexed by: OpenAgentSkill community index

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/pawbytes-paw-cra-content-research?metric=listed&label=Listed)](https://www.openagentskill.com/skills/pawbytes-paw-cra-content-research?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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Overview

Content Research Workflow

Overview

On Activation

Pipeline

1. Research Brief Intake

2. Brand Context Load

3. Competitor Scan (scope: competitor or all)

4. Trend Analysis (scope: trend or all)

5. Content Opportunity Identification (scope: content or all)

6. Production Recommendations

7. Report Generation

8. Save to Memory

9. Handoff

Research Tools

Primary: Exa MCP

Fallback: Web Search

Optional: Agent-Browser CLI

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