Install
$ agentstack add skill-prunaai-pruna-skills-p-image ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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 in backticks, confirm PRUNAAPIKEY is set (or stop with signup links from pruna-api`), then ask for prompt / aspect ratio. When drafting the prompt, follow Prompt craft (dynamic + faithful) — do not paste skill examples.
Prompt craft (dynamic + faithful)
Every input.prompt must be fresh and specific, and must keep the user's request. Diversity never overrides the brief.
| Do | Don't | | --- | --- | | Run the generation-diversity random seed ritual; state it; rotate ≥2 free axes (camera, lighting, setting texture, render category) | Copy curl examples from this skill (otter DJ, corgi cowboy, …) or reuse a prior session's prompt | | Lock user-required facts first (subject, product, brand cues, must-keep props, readable text if asked) | Swap the subject for a “cooler” scene that ignores the request | | Expand with concrete nouns, frozen action, materials, placement (image-prompting golden rules) | Vague mood-only strings (cool product vibe, neon) | | Show the drafted prompt + aspect_ratio before POST when the user has not locked wording | Silent regen with a different subject than approved |
Fidelity check (before pay): if you remove the user’s named subject/product/setting from the prompt, the job is wrong — rewrite. Free axes only fill what the brief left open.
When showing a drafted prompt, still name ` p-image ` (guides help craft; this tool owns the call).
Pruna note: p-image has no prompt upsampling — concrete language is the whole craft. Avoid dense readable typography unless the user explicitly asked for copy on a surface.
When NOT to use
Use a different skill instead:
| Skill | Description | Install | | --- | --- | --- | | p-image-edit | Use when someone wants to edit an existing photo — change outfits or backgrounds, compose from reference images, or apply prompt-driven edits. | npx skills add PrunaAI/pruna-skills@p-image-edit -y | | p-image-try-on | Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce. | npx skills add PrunaAI/pruna-skills@p-image-try-on -y |
HTTP (curl)
Create (async — recommended)
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": "Disco ball reflections on an otter DJ scratching vinyl at a packed 1970s roller rink, fish-eye lens, glitter confetti mid-air, funky energy",
"aspect_ratio": "9:16"
}
}'
Poll and download: follow pruna-api.
Complete the random seed ritual from generation-diversity before writing prompts — do not pass the ritual string as API seed. Optional api_seed only when the user requests reproducibility.
Create (sync — quick test only)
curl -X POST 'https://api.pruna.ai/v1/predictions' \
-H 'Content-Type: application/json' \
-H "apikey: ${PRUNA_API_KEY}" \
-H 'Model: p-image' \
-H 'Try-Sync: true' \
-d '{"input":{"prompt":"Corgi cowboy lassoing a runaway taco truck through Monument Valley dust storm, pulp western poster energy, dynamic diagonal composition","aspect_ratio":"16:9"}}'
Generation flow
Follow generation-diversity still-image prompt flow every time:
- Lock brief — user subject, product, format, any copy-on-surface.
- Ritual seed — fresh string; derive free axes (camera, lighting,
render_category_tag,aspect_ratiowhen unset). - Draft explicit prompt — Prompt craft (dynamic + faithful) +
image-promptinggolden rules; fidelity check before pay. - Confirm — show
prompt+aspect_ratiounless wording is locked. - POST — async curl below; poll via
pruna-api; runp-imagequality checklist before upscale/video.
Aspect ratio: pass aspect_ratio in input; if output dimensions do not match (e.g. asked 16:9, got portrait), retry once with explicit horizontal wide / vertical wording in the prompt.
Mood board / batch: new ritual per independent still; different aspect_ratio per panel when format not locked.
Hero approved → tweak: hand off to p-image-edit on the hero URL — do not text-to-image re-roll the same subject.
Required input
prompt(string)
Common optional fields
aspect_ratio:1:1,16:9,9:16,4:3,3:4,3:2,2:3,custom(withwidth/heightmultiples of 16, 256–1440)seed,lora_weights,lora_scale,hf_api_token,disable_safety_checker
Typical next steps
Common follow-ons after this skill:
| Skill | Description | Install | | --- | --- | --- | | p-image-edit | Use when someone wants to edit an existing photo — change outfits or backgrounds, compose from reference images, or apply prompt-driven edits. | npx skills add PrunaAI/pruna-skills@p-image-edit -y | | p-image-try-on | Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce. | npx skills add PrunaAI/pruna-skills@p-image-try-on -y | | p-image-upscale | Use when someone wants to upscale or sharpen an existing image for print, large crops, or higher-quality delivery. | npx skills add PrunaAI/pruna-skills@p-image-upscale -y | | p-video | Use when someone wants one short video clip from text or images — B-roll, start/end frame animation, or a quick motion shot. Not for full multi-scene films or lip-synced hosts. | npx skills add PrunaAI/pruna-skills@p-video -y | | avatar-single-scene | Use when someone wants one polished host-on-camera beat — a speaking person with intake and approval gates before generation. | npx skills add PrunaAI/pruna-skills@avatar-single-scene -y | | avatar-multi-scene | Use when someone wants the same person hosting several clips — multi-segment UGC, comparison reels, or mixed speaking and animated scenes with continuity. | npx skills add PrunaAI/pruna-skills@avatar-multi-scene -y |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: PrunaAI
- Source: PrunaAI/pruna-skills
- License: Apache-2.0
- Homepage: https://dashboard.pruna.ai
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.