What makes p-image-try-on notable?

1.5K downloads. Trust evidence available

How should p-image-try-on 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.

agentCLAWHUBUnverified

p-image-try-on

Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce. Skill: p-image-try-on Owner: pruna-ai Summary: Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce. Tags: ai:1.0.14, generative:1.0.14, latest:1.0.14, pruna:1.0.14 Version history: v1.0.14 | 2026-09-29T15:34:10.080Z | auto - Version bump to 1.0.14 in metadata. - Removed the redundant skill-card.md file. - No functional or usage changes to skill logic or doc

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.5k

Updated

Oct 10, 2026

Version

1.0.14

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.5K downloads reported by the source. Last updated 10/10/2026.

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 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.5K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.14release · observed Sep 29, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s173djpkkm1x4yfzg522h5vfp989mrn5:p-image-try-on

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-pruna-ai-p-image-try-on/snapshot"

Documentation

CLAWHUB

149,123 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: p-image-try-on
description: Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce.
license: MIT
metadata:
  version: "1.0.14"
  package: pruna-skills
  pruna_model: p-image-try-on
---

## Prerequisites

Install and load these skills before generating (skip if already in context via `@pruna`):

| Skill | Description | Install |
| --- | --- | --- |
| `generation-diversity` | Use when writing any generative prompt — ritual seed, explicit structure, scenario axes, and quality gates before paid API calls. | `npx skills add PrunaAI/pruna-skills@generation-diversity -y` |
| `image-prompting` | Use when crafting still-image prompts for any generative model — composition, identity sheets, edits, try-on, and photoreal personas. | `npx skills add PrunaAI/pruna-skills@image-prompting -y` |
| `pruna-api` | Use before any Pruna or Replicate HTTP call — credentials, upload/poll/download, parallel batches, and agent safety. | `npx skills add PrunaAI/pruna-skills@pruna-api -y` |

Or install the full suite once: `npx skills add PrunaAI/pruna-skills@pruna -y`

Follow each skill's **Before generating** / craft sections — do not restate guide content here.

## Agent habit

In the **first reply**, name `` `p-image-try-on` `` in backticks, confirm `PRUNA_API_KEY`, then ask for `person_image` + `garment_images`. Open intake → **`generation-diversity`** clarification intake when silent. When refs need disambiguation, draft with **Prompt craft (dynamic + faithful)** — do not paste skill examples. Redirect background-only / no-garment jobs to `p-image-edit`.

## Prompt craft (dynamic + faithful)

Identity and garments come from **`person_image`** + **`garment_images[]`**. Optional **`prompt`** only **disambiguates refs** — it does not invent a new person or outfit.

| Do | Don't |
| --- | --- |
| Lock **`person_image`** and every **`garment_images[]`** URL first; omit **`prompt`** on clean flat-lays | Describe a new scene, model, or garment the user did not supply |
| When refs are ambiguous: `the green t-shirt from image 1 and the trousers from image 2` (`image-prompting` try-on craft) | Mood-only prompts (`fashion editorial vibe`) or copy this skill's extended example when refs differ |
| Ritual seed before drafting disambiguation wording; vary phrasing when multiple valid mappings exist | Use **`prompt`** for background swaps — redirect to `p-image-edit` |
| Show **`prompt`** (if needed) before `POST` when refs are ambiguous | Silent try-on that changes pose, face, or garments beyond the brief |

**Fidelity check (before pay):** output must still be the user's person in the user's garment(s). If **`prompt`** could apply to a different ref set, rewrite the disambiguation.

## When NOT to use

Use a different skill instead:

| Skill | Description | Install |
| --- | --- | --- |
| `p-image` | Use when someone explicitly wants the fastest, cheapest photo generation — mood boards, bulk panels, or quick iter

_meta.json

{
  "ownerId": "kn7cagwf7q3t0cxrgteb7xk0bh81j0eb",
  "slug": "p-image-try-on",
  "version": "1.0.14",
  "publishedAt": 1790696050080
}

example-prompt.md

# p-image-try-on — example prompts

**Before every job:** random seed ritual (`generation-diversity`).

Try-on scenario briefs and Replicate reference links. **Cross-model persona + avatar examples:** `image-prompting`.

Starter plates aligned with `image-prompting` and `image-prompting`.

## Canonical reference outputs

Quality bar (Replicate playground candidates):

1. [Editorial seated + artistic shirt](https://replicate.com/p/p47vaj1f91rmw0cyt4er0z2zd4)
2. [Complex collaged suit, high angle](https://replicate.com/p/tf7gqansnnrmt0cyt4j8mpx1c8)
3. [Mirror selfie + cap + logo tee](https://replicate.com/p/hp60wyj355rmy0cyt4psnc2mh0)
4. [Multi-garment streetwear stack](https://replicate.com/p/bak21xr79srmr0cyt52tap1nw8)
5. [Pleated blouse, golden-hour portrait](https://replicate.com/p/g9hd22x26drmr0cytmtsx11c5g)

## Person plate → try-on (minimal curl flow)

```bash
export PRUNA_API_KEY="your_key"

# 1) Generate photoreal plate (or upload your own)
curl -X POST 'https://api.pruna.ai/v1/predictions' \
  -H 'Content-Type: application/json' -H "apikey: ${PRUNA_API_KEY}" \
  -H 'Model: p-image' \
  -d '{"input":{"prompt":"Photoreal editorial fashion photograph, woman mid-20s South Asian, seated on weathered wood floor against textured plaster wall, soft window daylight, 3:4, natural skin, single subject one frame","aspect_ratio":"3:4"}}'

# Complete random seed ritual (SSoT) before writing prompts — do not pass ritual string as API seed

# 2) Upload person + garment refs → /v1/files, then try-on (normal mode for finals)
curl -X POST 'https://api.pruna.ai/v1/predictions' \
  -H 'Content-Type: application/json' -H "apikey: ${PRUNA_API_KEY}" \
  -H 'Model: p-image-try-on' \
  -d '{
    "input": {
      "person_image": "https://api.pruna.ai/v1/files/PERSON_ID",
      "garment_images": ["https://api.pruna.ai/v1/files/GARMENT_ID"],
      "output_quality": 95,
      "preserve_input_size": true
    }
  }'
```

Run the slop gate on the person plate before step 2. Run `image-prompting` on the output.

## Scenario briefs

### Complex collaged suit (tier D)

**Person plate:** high-angle full-body studio, shirtless male model, hands in pockets — see showcase doc.

**Garments:** two refs (blazer + trousers) or one on-model lifestyle ref with:

```json
"prompt": "the artistic collaged blazer from image 1 and the matching patchwork trousers from image 2"
```

**Settings:** `turbo: false`, up to 2–4 garment URLs.

### Streetwear stack (tier D, multi-garment)

**Person plate:** full-body asphalt, prop in frame (bat, bag) — preserve props in output.

**Garments:** jacket, tee, pants as separate packshots **or** one stack photo + prompt:

```json
"prompt": "the patchwork jacket from image 1, the yellow logo tee from image 2, and the patchwork pants from image 3"
```

### Accessories in-scene (tier E)

**Person plate:** mirror selfie or street portrait with visible head/shoulders.

**Garments:** cap ref + tee ref (≤6 total). Verify hat brim and logo placement in checklist

skill-card.md

## Description:

Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce.

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

## Publisher:

[pruna-ai](https://clawhub.ai/user/pruna-ai)

### License/Terms of Use:

MIT-0

## Use Case:

Fashion and ecommerce creators use this skill to guide virtual try-on of a supplied person image with supplied garment photos.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Required external skills are installed from unpinned targets that may change after review.

Mitigation: Review or pin the referenced Pruna skills before installing.

Risk: Personal images are uploaded to Pruna and API access uses a credential.

Mitigation: Upload only images you are comfortable sharing with Pruna; keep PRUNA_API_KEY scoped and protected.

## Reference(s):

- [p-image-try-on on ClawHub](https://clawhub.ai/pruna-ai/skills/p-image-try-on)
- [Example virtual try-on output](https://replicate.com/p/p47vaj1f91rmw0cyt4er0z2zd4)

## Skill Output:

**Output Type(s):** [Guidance, Shell commands]

**Output Format:** [Markdown with curl examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Guides image uploads and virtual try-on requests using supplied person and garment references.]

## Skill Version(s):

1.0.14 (source: frontmatter and release evidence)

## 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.

skill.manifest.json

{
  "references": []
}

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