AI Skill
Review
Audit score 70

flux-kontext

agentspace-so/runcomfy-agent-skills

Edit images precisely with Flux 1 Kontext Pro via RunComfy CLI — single-reference local edits with strong prompt control

What is flux-kontext?

flux-kontext installs prompting patterns and CLI wiring for Black Forest Labs' Flux 1 Kontext Pro image-edit model, accessed through the RunComfy Model API. It handles single-reference local image edits — changing specific elements while preserving identity, pose, branding, or framing — and includes documented anti-patterns, routing guidance to sibling models, and exit code reference.

  • Runs Flux 1 Kontext Pro image edits via `runcomfy run blackforestlabs/flux-1-kontext/pro/edit`
  • Accepts a single source image URL and a declarative edit prompt
  • Supports optional aspect_ratio and seed fields for reproducible variants
  • Bundles verified prompting patterns (preservation-led, single-instruction) for sharper output
  • Routes to sibling models (Nano Banana Edit, GPT Image 2, Flux 2 Klein) when Kontext is the wrong fit
  • Downloads generated outputs to a local directory specified by --output-dir

How to install flux-kontext

npx skills add https://github.com/agentspace-so/runcomfy-agent-skills --skill flux-kontext
Prerequisites
  • Node.js (to run npx skills add)
  • RunComfy CLI: `npm i -g @runcomfy/cli`
  • RunComfy account with `runcomfy login` (browser device-code flow)
  • For CI/containers: set `RUNCOMFY_TOKEN=<token>` environment variable
  • Source image must be publicly fetchable via HTTPS URL
Claude Code
Cursor
Windsurf
Cline

How to use flux-kontext

  1. 1.Install the skill: `npx skills add https://github.com/agentspace-so/runcomfy-agent-skills --skill flux-kontext`
  2. 2.Authenticate: run `runcomfy login` or set `RUNCOMFY_TOKEN` in your environment
  3. 3.Prepare a publicly accessible HTTPS URL for your source image
  4. 4.Write a single declarative edit prompt; lead with preservation instructions (e.g., 'Keep face and pose unchanged.')
  5. 5.Run: `runcomfy run blackforestlabs/flux-1-kontext/pro/edit --input '{"prompt":"...","image":"https://..."}' --output-dir <absolute/path>`
  6. 6.Add `"seed": 42` to the input JSON for reproducible variant comparisons
  7. 7.If the edit drifts, split compound changes into sequential single-instruction passes
  8. 8.Check exit codes (0=success, 65=bad input, 75=retry, 77=auth) for troubleshooting

Use cases

Good for
  • Precise local edits: add or swap a single object while preserving everything else
  • Brand asset updates: replace label text or colors with identity preserved
  • Portrait or product photo micro-edits with high source fidelity
  • Iterative single-image variant generation using a fixed seed
  • Sequential multi-pass edits where compound prompts would cause drift
Who it's for
  • Developers building image-editing pipelines with coding agents like Claude Code or Cursor
  • Designers needing scriptable, reproducible local image edits
  • Product teams doing brand-asset text or color swaps at scale
  • Anyone explicitly requesting Flux Kontext / BFL Kontext in their workflow
  • CI/CD pipelines requiring token-based (non-interactive) image editing

flux-kontext FAQ

When should I use Nano Banana Edit instead of Flux Kontext?

Use Nano Banana Edit when you need to edit multiple images in a batch (1–20 images) or when your workflow requires multi-image reference inputs. Flux Kontext only accepts a single source image.

Can I edit text embedded in images, like multilingual labels?

Flux Kontext handles quoted brand text swaps reasonably well with preservation-led prompts, but for multilingual or complex embedded text editing, GPT Image 2 edit is recommended.

Why do compound prompts produce worse results?

Kontext is optimized for single declarative instructions. Prompts with multiple simultaneous changes (change A and add B and remove C) cause drift. Split into sequential single-instruction passes instead.

How do I use this in CI without interactive login?

Set the `RUNCOMFY_TOKEN=<token>` environment variable. The CLI will use this token instead of reading from `~/.config/runcomfy/token.json`, bypassing the browser login flow entirely.

Are my image URLs fetched by the CLI or by RunComfy servers?

Image URLs are fetched by the RunComfy model server, not by the CLI on your local machine. Only `model-api.runcomfy.net` and `*.runcomfy.net`/`*.runcomfy.com` are contacted; there is no telemetry.

Full instructions (SKILL.md)

Source of truth, from agentspace-so/runcomfy-agent-skills.


name: flux-kontext displayName: "Flux Kontext Pro — Pro Pack on RunComfy" description: > Edit images with Flux 1 Kontext Pro (Black Forest Labs' precise local image-edit model) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Flux Kontext's strengths (single-reference precise local edits, strong prompt control, consistent high-fidelity outputs), the schema (single image + prompt), and when to route to Nano Banana Edit / GPT Image 2 edit / Flux 2 Klein instead. Calls runcomfy run blackforestlabs/flux-1-kontext/pro/edit through the local RunComfy CLI. Triggers on "flux kontext", "flux-kontext", "flux 1 kontext", "kontext", "BFL kontext", or any explicit ask to edit with this model. homepage: https://www.runcomfy.com license: MIT

Flux Kontext Pro — Pro Pack on RunComfy

runcomfy.com · Model page · GitHub

Black Forest Labs' Flux 1 Kontext Pro — single-reference precise local image edit — hosted on the RunComfy Model API. Strong prompt control, consistent outputs, high fidelity.

npx skills add agentspace-so/runcomfy-skills --skill flux-kontext -g

When to pick this model (vs siblings)

You wantUse
Single-image precise local edit ("she's now holding X")Flux Kontext
High-fidelity preservation of source identityFlux Kontext
Batch edits across 1–20 imagesNano Banana Edit
Edit multilingual / embedded text in imageGPT Image 2 edit
Generate from scratch, no source imageFlux 2 Klein

If the user said "Flux Kontext" / "kontext" / "BFL Kontext" explicitly, route here regardless.

Prerequisites

  1. RunComfy CLInpm i -g @runcomfy/cli
  2. RunComfy accountruncomfy login opens a browser device-code flow.
  3. CI / containers — set RUNCOMFY_TOKEN=<token> instead of runcomfy login.

Endpoints + input schema

blackforestlabs/flux-1-kontext/pro/edit

FieldTypeRequiredDefaultNotes
promptstringyesSingle declarative edit instruction.
imagestringyesSingle source image URL (publicly fetchable HTTPS).
aspect_ratioenumno(input)Pick from supported W:H options on the model page.
seedintnoReuse for variant comparisons.

The schema is intentionally minimal — Kontext leans on prompt + single ref. For multi-image or web-grounded edits, route to Nano Banana Edit.

How to invoke

Default — local edit, preserve everything else:

runcomfy run blackforestlabs/flux-1-kontext/pro/edit \
  --input '{
    "prompt": "Keep the person'\''s face, pose, and clothing unchanged. Add an orange umbrella in her left hand and a slight smile.",
    "image": "https://.../portrait.jpg"
  }' \
  --output-dir <absolute/path>

With seed for reproducible variant series:

runcomfy run blackforestlabs/flux-1-kontext/pro/edit \
  --input '{
    "prompt": "Keep the bottle, label, and lighting unchanged. Replace the brand text on the label from \"ALPHA\" to \"AURA\".",
    "image": "https://.../bottle.jpg",
    "seed": 42
  }' \
  --output-dir <absolute/path>

Prompting — what actually works

One declarative instruction. Kontext shines on prompts shaped like the docs example: "She is now holding an orange umbrella and smiling". Imperative mood, single change.

Preservation first. Lead with "Keep [identity / pose / framing / brand] unchanged." Then the change. Models honor what's stated up front.

Single ref only — pick the right one. No multi-image fanout here. If you have multiple references, decide which is primary and pass that one. For multi-image flows, route to Nano Banana Edit.

Iterate on small changes. If Kontext drifts, split a compound edit into sequential single-instruction passes (pass 1: change background, pass 2: change clothing).

Aspect ratio — pick from the supported enum. Out-of-list values 422 or crop.

Anti-patterns:

  • Compound prompts ("change A and add B and remove C") → drift.
  • Trying to fan out to multiple source images → wrong model (use Nano Banana Edit).
  • Prompts written in passive voice → less reliable.
  • Asking for novel composition without a source image → wrong model (use Flux 2 Klein t2i).

Where it shines

Use caseWhy Flux Kontext
Single-shot precise local editSpecifically designed for this; high fidelity
Preserve source identity through targeted changeStrong preservation under explicit instruction
Brand-asset text or color swapQuoted text + preservation lead-in works well
Quick iteration on one imageShort prompts + single ref = fast result loop

Sample prompts (verified to produce strong results)

Page example:

She is now holding an orange umbrella and smiling

Preservation-led brand edit:

Keep the bottle silhouette, table, and lighting exactly as in the input.
Replace only the brand text on the label, from "ALPHA" to "AURA".
Same font weight, white on black, centered.

Compositional micro-edit:

Keep the person's face, pose, and clothing unchanged. Add a leather
shoulder bag, dark brown, hanging on the right shoulder.

Limitations

  • Single source image only. For multi-image flows, use Nano Banana Edit (1–20).
  • Public RunComfy docs are minimal — schema fields beyond prompt + image + aspect_ratio + seed may exist; check the model page for the latest field list.
  • Compound prompts drift — split into sequential passes.
  • For multilingual / embedded text editing, GPT Image 2 edit usually wins.

Exit codes

codemeaning
0success
64bad CLI args
65bad input JSON / schema mismatch
69upstream 5xx
75retryable: timeout / 429
77not signed in or token rejected

Full reference: docs.runcomfy.com/cli/troubleshooting.

How it works

The skill invokes runcomfy run blackforestlabs/flux-1-kontext/pro/edit with a JSON body matching the schema. The CLI POSTs to https://model-api.runcomfy.net/v1/models/blackforestlabs/flux-1-kontext/pro/edit, polls the request, fetches the result, and downloads any .runcomfy.net/.runcomfy.com URL into --output-dir. Ctrl-C cancels the remote request before exit.

Security & Privacy

  • Token storage: runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner-only read/write). Set RUNCOMFY_TOKEN env var to bypass the file entirely in CI / containers.
  • Input boundary: the user prompt is passed as a JSON string to the CLI via --input. The CLI does NOT shell-expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content.
  • Third-party content: image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image-based prompt injection is a known risk for any image-edit / video-edit model.
  • Outbound endpoints: only model-api.runcomfy.net (request submission) and *.runcomfy.net / *.runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks.
  • Generated-file size cap: the CLI aborts any single download > 2 GiB to prevent disk-fill from a malicious or runaway model output.