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ahrefs-python
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage includ
Übersicht
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
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Ahrefs Python SDK Skill
Overview
The Ahrefs API provides programmatic access to Ahrefs SEO data. The official Python SDK (ahrefs-python) provides typed request and response models for all endpoints, auto-generated from the OpenAPI spec.
Key capabilities:
- Site Explorer - Backlinks, organic keywords, domain rating, traffic, referring domains
- Keywords Explorer - Keyword research, volumes, difficulty, related terms
- Rank Tracker - SERP monitoring, competitor tracking
- Site Audit - Technical SEO issues, page content, page explorer
- Brand Radar - AI brand mentions, share of voice, impressions
- SERP Overview - Search result analysis
- Batch Analysis - Bulk domain/URL metrics via POST
Installation
pip3 install git+https://github.com/ahrefs/ahrefs-python.git
Requires Python 3.11+. Dependencies: httpx, pydantic.
API Method Discovery
The SDK has 52 methods across 7 API sections. The built-in search tool is the fastest way to find the right method — it returns matching method signatures, parameters, and return types directly, so there's no need to scan through a large reference.
Python (preferred when already in a Python context):
from ahrefs.search import search_api_methods
# Returns formatted text with method signatures, parameters, and return types
print(search_api_methods("domain rating"))
# Filter by API section and limit results
print(search_api_methods("backlinks", section="site-explorer", limit=3))
CLI (preferred when exploring from the terminal):
# Ensure python3 points to the interpreter where ahrefs-python is installed:
# which python3
# python3 -c "import ahrefs"
python3 -m ahrefs.api_search "domain rating"
python3 -m ahrefs.api_search "backlinks" --section site-explorer --limit 3
python3 -m ahrefs.api_search "batch" --json
python3 -m ahrefs.api_search --sections # list all API sections
IMPORTANT RULES
- ALWAYS use the
ahrefs-pythonSDK. DO NOT make rawhttpx/requestscalls to the Ahrefs API. - ALWAYS pass dates as strings in
YYYY-MM-DDformat (e.g."2025-01-15"). - ALWAYS use
selecton list endpoints to request only the columns you need. List endpoints return all columns by default, which wastes API units and increases response size. - USE context managers (
with/async with) for client lifecycle management. - NEVER hardcode API keys in source code. Use the
AHREFS_API_KEYenvironment variable or your preferred secrets mechanism. - The client handles retries (429, 5xx, connection errors) automatically. DO NOT implement your own retry logic on top of the SDK.
Quick Start
import os
from ahrefs import AhrefsClient
with AhrefsClient(api_key=os.environ["AHREFS_API_KEY"]) as client:
data = client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
print(data.domain_rating) # 91.0
print(data.ahrefs_rank) # 3
SDK Patterns
Client Setup
import os
import ahrefs
with ahrefs.AhrefsClient(
api_key=os.environ["AHREFS_API_KEY"], # or any secrets source
base_url="...", # override API base URL (default: https://api.ahrefs.com/v3)
timeout=30.0, # request timeout in seconds (default: 60)
max_retries=3, # retries on transient errors (default: 2)
) as client:
...
Async client:
import os
from ahrefs import AsyncAhrefsClient
async with AsyncAhrefsClient(api_key=os.environ["AHREFS_API_KEY"]) as client:
data = await client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
For parallel calls, use asyncio.gather:
import asyncio
async with AsyncAhrefsClient(api_key=os.environ["AHREFS_API_KEY"]) as client:
dr_ahrefs, dr_moz = await asyncio.gather(
client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15"),
client.site_explorer_domain_rating(target="moz.com", date="2025-01-15"),
)
Calling Methods
Two calling styles -- both are equivalent:
# Keyword arguments (recommended)
data = client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
# Request objects (full type safety)
from ahrefs.types import SiteExplorerDomainRatingRequest
request = SiteExplorerDomainRatingRequest(target="ahrefs.com", date="2025-01-15")
data = client.site_explorer_domain_rating(request)
Method names follow {api_section}_{endpoint}, e.g. site_explorer_organic_keywords, keywords_explorer_overview.
Responses
Methods return typed Data objects directly.
Scalar endpoints return a single data object (or None):
data = client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
print(data.domain_rating)
List endpoints return a list of data objects. There is no pagination — set limit to the number of results you need. Use select to request only the columns you need:
items = client.site_explorer_organic_keywords(
target="ahrefs.com",
date="2025-01-15",
select="keyword,volume,best_position",
order_by="volume:desc",
limit=10,
)
for item in items:
print(item.keyword, item.volume, item.best_position)
Error Handling
import ahrefs
try:
data = client.site_explorer_domain_rating(target="example.com", date="2025-01-15")
except ahrefs.AuthenticationError: # 401
...
except ahrefs.RateLimitError as e: # 429 -- e.retry_after has the delay
...
except ahrefs.NotFoundError: # 404
...
except ahrefs.APIError as e: # other 4xx/5xx -- e.status_code, e.response_body
...
except ahrefs.APIConnectionError: # network / timeout
...
All exceptions inherit from ahrefs.AhrefsError.
Common Parameters
Most list endpoints share these parameters:
| Parameter | Type | Description |
|---|---|---|
target | str | Domain, URL, or path to analyze |
date | str | Date in YYYY-MM-DD format |
date_from / date_to | str | Date range for history endpoints |
country | str | Two-letter country code (ISO 3166-1 alpha-2) |
select | str | Comma-separated columns to return |
where | str | Filter expression |
order_by | str | Column and direction, e.g. "volume:desc" |
limit | int | Max results to return |
Parameters typed as enums in the API reference (CountryEnum, VolumeModeEnum, etc.) accept plain strings — pass country="us" not CountryEnum("us").
The where parameter takes a JSON string. Use json.dumps() to build it:
import json
where = json.dumps({"field": "volume", "is": ["gte", 1000]})
items = client.site_explorer_organic_keywords(
target="ahrefs.com", date="2025-01-15",
select="keyword,volume", where=where,
)
For full filter syntax (boolean combinators, operators, nested fields), see references/filter-syntax.md.
API Methods
Use search_api_methods("query") or python3 -m ahrefs.api_search "query" to find methods by keyword. Search covers all 52 methods across 7 API sections and returns complete signatures, parameters, and response fields.
Dateimetadaten
name: ahrefs-python description: Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
Originaltext anzeigen
---
name: ahrefs-python
description: Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
---
# Ahrefs Python SDK Skill
## Overview
The Ahrefs API provides programmatic access to Ahrefs SEO data. The official Python SDK (`ahrefs-python`) provides typed request and response models for all endpoints, auto-generated from the OpenAPI spec.
Key capabilities:
- **Site Explorer** - Backlinks, organic keywords, domain rating, traffic, referring domains
- **Keywords Explorer** - Keyword research, volumes, difficulty, related terms
- **Rank Tracker** - SERP monitoring, competitor tracking
- **Site Audit** - Technical SEO issues, page content, page explorer
- **Brand Radar** - AI brand mentions, share of voice, impressions
- **SERP Overview** - Search result analysis
- **Batch Analysis** - Bulk domain/URL metrics via POST
## Installation
```sh
pip3 install git+https://github.com/ahrefs/ahrefs-python.git
```
Requires Python 3.11+. Dependencies: `httpx`, `pydantic`.
## API Method Discovery
The SDK has 52 methods across 7 API sections. The built-in search tool is the fastest way to find the right method — it returns matching method signatures, parameters, and return types directly, so there's no need to scan through a large reference.
**Python** (preferred when already in a Python context):
```python
from ahrefs.search import search_api_methods
# Returns formatted text with method signatures, parameters, and return types
print(search_api_methods("domain rating"))
# Filter by API section and limit results
print(search_api_methods("backlinks", section="site-explorer", limit=3))
```
**CLI** (preferred when exploring from the terminal):
```sh
# Ensure python3 points to the interpreter where ahrefs-python is installed:
# which python3
# python3 -c "import ahrefs"
python3 -m ahrefs.api_search "domain rating"
python3 -m ahrefs.api_search "backlinks" --section site-explorer --limit 3
python3 -m ahrefs.api_search "batch" --json
python3 -m ahrefs.api_search --sections # list all API sections
```
## IMPORTANT RULES
- ALWAYS use the `ahrefs-python` SDK. DO NOT make raw `httpx`/`requests` calls to the Ahrefs API.
- ALWAYS pass dates as strings in `YYYY-MM-DD` format (e.g. `"2025-01-15"`).
- ALWAYS use `select` on list endpoints to request only the columns you need. List endpoints return all columns by default, which wastes API units and increases response size.
- USE context managers (`with` / `async with`) for client lifecycle management.
- NEVER hardcode API keys in source code. Use the `AHREFS_API_KEY` environment variable or your preferred secrets mechanism.
- The client handles retries (429, 5xx, connection errors) automatically. DO NOT implement your own retry logic on top of the SDK.
## Quick Start
```python
import os
from ahrefs import AhrefsClient
with AhrefsClient(api_key=os.environ["AHREFS_API_KEY"]) as client:
data = client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
print(data.domain_rating) # 91.0
print(data.ahrefs_rank) # 3
```
## SDK Patterns
### Client Setup
```python
import os
import ahrefs
with ahrefs.AhrefsClient(
api_key=os.environ["AHREFS_API_KEY"], # or any secrets source
base_url="...", # override API base URL (default: https://api.ahrefs.com/v3)
timeout=30.0, # request timeout in seconds (default: 60)
max_retries=3, # retries on transient errors (default: 2)
) as client:
...
```
Async client:
```python
import os
from ahrefs import AsyncAhrefsClient
async with AsyncAhrefsClient(api_key=os.environ["AHREFS_API_KEY"]) as client:
data = await client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
```
For parallel calls, use `asyncio.gather`:
```python
import asyncio
async with AsyncAhrefsClient(api_key=os.environ["AHREFS_API_KEY"]) as client:
dr_ahrefs, dr_moz = await asyncio.gather(
client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15"),
client.site_explorer_domain_rating(target="moz.com", date="2025-01-15"),
)
```
### Calling Methods
Two calling styles -- both are equivalent:
```python
# Keyword arguments (recommended)
data = client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
# Request objects (full type safety)
from ahrefs.types import SiteExplorerDomainRatingRequest
request = SiteExplorerDomainRatingRequest(target="ahrefs.com", date="2025-01-15")
data = client.site_explorer_domain_rating(request)
```
Method names follow `{api_section}_{endpoint}`, e.g. `site_explorer_organic_keywords`, `keywords_explorer_overview`.
### Responses
Methods return typed Data objects directly.
**Scalar endpoints** return a single data object (or `None`):
```python
data = client.site_explorer_domain_rating(target="ahrefs.com", date="2025-01-15")
print(data.domain_rating)
```
**List endpoints** return a list of data objects. There is no pagination — set `limit` to the number of results you need. Use `select` to request only the columns you need:
```python
items = client.site_explorer_organic_keywords(
target="ahrefs.com",
date="2025-01-15",
select="keyword,volume,best_position",
order_by="volume:desc",
limit=10,
)
for item in items:
print(item.keyword, item.volume, item.best_position)
```
### Error Handling
```python
import ahrefs
try:
data = client.site_explorer_domain_rating(target="example.com", date="2025-01-15")
except ahrefs.AuthenticationError: # 401
...
except ahrefs.RateLimitError as e: # 429 -- e.retry_after has the delay
...
except ahrefs.NotFoundError: # 404
...
except ahrefs.APIError as e: # other 4xx/5xx -- e.status_code, e.response_body
...
except ahrefs.APIConnectionError: # network / timeout
...
```
All exceptions inherit from `ahrefs.AhrefsError`.
### Common Parameters
Most list endpoints share these parameters:
| Parameter | Type | Description |
|-----------|------|-------------|
| `target` | `str` | Domain, URL, or path to analyze |
| `date` | `str` | Date in YYYY-MM-DD format |
| `date_from` / `date_to` | `str` | Date range for history endpoints |
| `country` | `str` | Two-letter country code (ISO 3166-1 alpha-2) |
| `select` | `str` | Comma-separated columns to return |
| `where` | `str` | Filter expression |
| `order_by` | `str` | Column and direction, e.g. `"volume:desc"` |
| `limit` | `int` | Max results to return |
Parameters typed as enums in the API reference (`CountryEnum`, `VolumeModeEnum`, etc.) accept plain strings — pass `country="us"` not `CountryEnum("us")`.
The `where` parameter takes a JSON string. Use `json.dumps()` to build it:
```python
import json
where = json.dumps({"field": "volume", "is": ["gte", 1000]})
items = client.site_explorer_organic_keywords(
target="ahrefs.com", date="2025-01-15",
select="keyword,volume", where=where,
)
```
For full filter syntax (boolean combinators, operators, nested fields), see `references/filter-syntax.md`.
## API Methods
Use `search_api_methods("query")` or `python3 -m ahrefs.api_search "query"` to find methods by keyword. Search covers all 52 methods across 7 API sections and returns complete signatures, parameters, and response fields.
Quelle prüfen
Preis und Betriebskosten
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- Preis unbestätigt
- Ausführen
- Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
- Lizenz
- MIT
- Preis unbestätigt
- Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.
Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →
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Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.
Vor Installation prüfen: Automatische Installation vermeiden
Lizenz: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.
Mit einer kleinen Aufgabe beginnen
- 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
- 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
- 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.
Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.
Quelle und Nutzungshinweise
Erfasst
Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.
- Quell-Repository
- OpenClaudia/openclaudia-skills
- Lizenz
- MIT
- Version
- 1.0.0
- Letzter GitHub-Push
- 25. Aug. 2026
- Verzeichnis aktualisiert
- 5. Sept. 2026
- Anleitungspfad
- skills/ahrefs-research/SKILL.md @ 221b37d7ab95
Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.
Qualität
72/100
Stark
Vertrauen
67/100
Nur Sandbox
Audit
78/100
Prüfung nötig
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Ergebnisse
- —
Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.
Agent-Zugang
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Weitere Details
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"recent_failure_rate": null,
"install_attempts": 0,
"install_success_rate": null,
"risk_blocked": 0,
"setup_required": 0,
"avg_output_quality": null,
"production_outcomes": 0,
"last_outcome_at": null,
"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"security",
"agent-skill"
],
"known_risks": [
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"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": 78,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"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": 72,
"label": "Strong"
},
"supply": {
"track": "Marketing and growth automation",
"scenario": "Marketing and growth",
"maintenance": "2mo since push",
"risk": "Needs review"
},
"alternative_skills": [],
"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",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
],
"agent_contract": {
"task_input": "Use ahrefs-python 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: 75/100 Strong shortlist",
"Audit: 78/100 Needs review",
"Safety: 38/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "openclaudia-ahrefs-python (ahrefs-python)",
"install_command": "npx skills add OpenClaudia/openclaudia-skills --skill ahrefs-python",
"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": "openclaudia-ahrefs-python",
"task": "Use ahrefs-python 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/openclaudia-ahrefs-python",
"api": "https://www.openagentskill.com/api/agent/skills/openclaudia-ahrefs-python",
"audit": "https://www.openagentskill.com/skills/openclaudia-ahrefs-python/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=openclaudia-ahrefs-python&task=Use%20ahrefs-python%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ahrefs-python%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ahrefs-python%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/openclaudia-ahrefs-python/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/openclaudia-ahrefs-python"
}
}Für Ersteller
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- OpenClaudia
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
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[](https://www.openagentskill.com/skills/openclaudia-ahrefs-python/audit)
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