What makes Galdr notable?

2.2K downloads. Trust evidence available

How should Galdr be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Claim this agent

agentCLAWHUBUnverified

Galdr

galdr turns YouTube links or local audio into time-ordered listener-state traces for AI agents. It reads pulse, pattern, attention, pressure, surface, harmon... Skill: Galdr Owner: sellemain Summary: galdr turns YouTube links or local audio into time-ordered listener-state traces for AI agents. It reads pulse, pattern, attention, pressure, surface, harmon... Tags: audio:0.7.0, latest:0.7.1, listening:0.7.0, music:0.7.0, perception:0.7.0 Version history: v0.7.1 | 2026-08-26T19:51:10.021Z | user Adds the supported non-root Docker runtime, environment-configurable workspace pat

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

2.2k

Updated

Oct 9, 2026

Version

0.7.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.2K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 2.2K downloadsadoption · observed Oct 9, 2026
Latest release: 0.7.1release · observed Aug 26, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17cwxrmjj7vay9dv4v9s329z585e399:galdr

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sellemain-galdr/snapshot"

Documentation

CLAWHUB

144,862 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: galdr
description: OpenClaw skill for using galdr's ARC workflow to turn YouTube URLs or local audio files into grounded, time-ordered listening-experience prompts backed by listener-state traces: pattern, attention, pulse, heard pressure, surface balance/evidence, harmony, melody, overtones, and silence/re-entry structure. Use when asked to analyze a song, explain what makes a track work structurally, generate a listening experience, or extract video frames from a music video.
version: "0.7.1"
author: Sellemain
license: MIT
platforms: [linux, macos]
---
# galdr

Use this skill when an OpenClaw agent needs to analyze music from a YouTube URL or local audio file and produce a grounded listening-experience prompt from measurable audio structure.

galdr is a music perception CLI for AI agents. Its default workflow is **ARC**: analyze a track into time-ordered listener-state traces, then assemble those traces into a prompt for grounded listening-experience prose. The metrics are evidence. The ARC prompt is the main user-facing output.

## Important: skill vs CLI

Current OpenClaw CLI install command:

```bash
openclaw skills install galdr
```

ClawHub may display an owner-qualified command such as `openclaw skills install @sellemain/galdr`. As of OpenClaw `2026.6.8`, the released CLI expects the bare skill slug `galdr`.

Installing this skill teaches OpenClaw how to use galdr. It does **not** install the `galdr` command itself.

The PyPI wheel contains the runtime CLI/library and bundled prompt templates. The OpenClaw skill is distributed separately through ClawHub so agent instructions can stay a clean skill artifact instead of being installed as Python package data.

Before starting:

```bash
galdr --version
```

If missing, install the CLI from a trusted source:

```bash
pip install galdr

# or from source:
git clone https://github.com/sellemain/galdr.git
cd galdr
pip install -e .
```

Preferred trusted sources:
- PyPI: <https://pypi.org/project/galdr/>
- Source: <https://github.com/sellemain/galdr>

If provenance matters, verify the PyPI metadata or install from the source repository before running it.

## When to use this skill

Use galdr when the user asks to:
- analyze a song or music video
- describe what makes a track work structurally
- generate a grounded listening experience
- extract frames around structural moments in a music video
- create an evidence packet for another model to write from

Do not use galdr for:
- general music trivia
- ordinary recommendation lists
- purely lyrical interpretation without audio structure
- pretending the metrics prove private emotional intent
- downloading copyrighted audio unless the operator has appropriate rights/context

## OpenClaw agent contract

Prefer the ARC path unless the user explicitly asks for raw metrics, debugging, or agent-internal traces.

Default sequence:
1. Fetch or listen to the track.
2. Analyze it into listener-state traces.
3. Assemble the ARC prompt with `--template

_meta.json

{
  "ownerId": "kn7f8hn1v7tcp7n6rspveyf49s83cfdj",
  "slug": "galdr",
  "version": "0.7.1",
  "publishedAt": 1787773870021
}

references/metrics.md

# galdr Metric Reference

All metrics come from `report.json` and the perception/harmony/melody/overtone stream files in `analysis/<slug>/`.

---

## Pattern (`pattern`)

**Range:** 0.0–1.0
**What it is:** How reliably the music keeps its pattern intact. High pattern means the listener can trust the structure: the pulse, texture, and energy are not suddenly breaking away.

**How it is calculated:** `pattern = 1.0 - disruption`. Disruption is a weighted blend of beat disruption (`40%`), spectral disruption (`35%`), and energy disruption (`25%`). Beat disruption catches missing or off-time expected beats; spectral disruption catches sudden timbral change above local context; energy disruption catches loudness jumps/drops above local trend.

| Value | Meaning |
|-------|---------|
| 0.96–1.0 | Exceptional hold. Listener rarely disrupted. Ritual, minimalist, or tightly composed. |
| 0.90–0.96 | Strong hold. Some variation but listener remains locked. Most engaging tracks. |
| 0.80–0.90 | Moderate disruption. Energy varies meaningfully. |
| <0.80 | Frequent disruption. Chaotic, experimental, or fragmentary. |

**Pattern breaks** are the moments where pattern drops suddenly. Check `pattern_breaks` in report.json for timestamps, intensity, and component breakdown (`beat`, `spectral`, `energy`). Those components tell you whether the break is rhythmic, textural, dynamic, or compound.

---

## Attention (`attention`, `mean_attention`)

**Range:** 0.0–1.0
**What it is:** How strongly attention is being carried forward by the track. Not speed, loudness, or quality — grip. High attention means the music keeps the listener coupled even through quiet or sparse passages.

**How it is calculated:** rolling beat regularity multiplied by beat density over an 8-second window. Regular intervals with enough beat evidence produce high attention; sparse or irregular beat evidence lowers it.

| Value | Meaning |
|-------|---------|
| >0.90 | Rare sustained pull. Track barely lets listener breathe. |
| 0.80–0.90 | Strong. Most engaging passages. |
| 0.60–0.80 | Fluctuating. Energy ebbs and flows. |
| <0.60 | Low continuity. Listener may disengage. |

After a silence, attention re-locking above 0.93 signals the listener has been re-engaged. Multiple re-lock events with deepening silences can indicate structured withdrawal.

---

## Pulse (`pulse`)

**Range:** 0.0–1.0
**What it is:** How steady the underlying pulse feels. Orthogonal to metric complexity — a 7/8 piece can have perfect pulse stability if the body can still trust where the beat lives.

| Value | Meaning |
|-------|---------|
| >0.96 | Clockwork. Ritual, electronic, or highly disciplined performance. |
| 0.90–0.96 | Tight but human. Most performed music. |
| 0.80–0.90 | Loose. Jazz feel, rubato, or intentional groove. |
| <0.80 | Irregular. Experimental or very free. |

High pulse + complex time signature (5/8, 7/8) = metric complexity is orthogonal to pulse stability.

---

## Surface balance (`surface_balance

skill-card.md

## Description:

OpenClaw skill for using galdr's ARC workflow to turn YouTube URLs or local audio files into grounded, time-ordered listening-experience prompts backed by listener-state traces: pattern, attention, pulse, heard pressure, surface balance/evidence, harmony, melody, overtones, and silence/re-entry structure.

This skill is ready for commercial/non-commercial use.

## Publisher:

[sellemain](https://clawhub.ai/user/sellemain)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and agent operators use Galdr to analyze songs, music videos, or local audio files and produce grounded listening-experience prompts from measurable audio structure. It is suited for time-ordered music analysis, structural explanation, frame extraction around musical moments, and evidence packets for another model to write from.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Installing or updating the galdr CLI and dependencies from external package sources can introduce supply-chain risk.

Mitigation: Install only from trusted sources, prefer a virtual environment or container, avoid administrator privileges, and verify PyPI or source metadata when provenance matters.

Risk: YouTube downloads, lyrics lookup, background lookup, or sending assembled prompts to external model endpoints may disclose track or analysis context outside the local machine.

Mitigation: Use local files or metrics-only modes when privacy matters, review assembled prompts before sharing them with another model, and send prompts externally only when the operator explicitly requests it.

Risk: The workflow can download copyrighted audio if used without appropriate rights or context.

Mitigation: Confirm the operator has appropriate rights or context before downloading copyrighted media.

## Reference(s):

- [galdr Metric Reference](references/metrics.md)
- [galdr PyPI project](https://pypi.org/project/galdr/)
- [galdr source repository](https://github.com/sellemain/galdr)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with inline shell commands and optional generated text prompts]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [ARC prompts are grounded in time-ordered listener-state traces; raw metrics may be emitted as JSON files by the galdr CLI.]

## Skill Version(s):

0.7.1 (source: server release evidence and frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

Github ReposUpdated 1h agoRank 70

AionUi

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

MCPOPENCLAW

Github ReposUpdated 6mo agoRank 70

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

OPENCLAW

Github ReposUpdated 6mo agoRank 70

cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants.