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Agent skill

Bt2020 Png Generation

by clshortfuse in clshortfuse/renodx

RenoDX workflow for generating, validating, or debugging BT.2020/BT.2100 HDR PNG artifacts from EXR, linear RGB, nits arrays, scalar maps, or analysis images.

MITAuto-check passedDevelopment

Install Bt2020 Png Generation

skills CLI
$ npx skills add clshortfuse/renodx --skill bt2020-png-generation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install clshortfuse/renodx bt2020-png-generation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/clshortfuse/renodx.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/bt2020-png-generation .claude/skills/bt2020-png-generation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bt2020-png-generation
GitHub stars
4.5k
Token cost
~2.8k tokens
SKILL.md length
1,456 words
Files
2
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

RenoDX workflow for generating, validating, or debugging BT.2020/BT.2100 HDR PNG artifacts from EXR, linear RGB, nits arrays, scalar maps, or analysis images.

  • Works in 6 steps: Classify the input → Convert to BT.2020 D65 linear RGB → Map scene values to absolute nits → …
  • Creating PQ/ST 2084 16-bit PNGs
  • SKILL.md covers Related smaller skills, Boundaries, Canonical output and Optional HDR ICC / Discord…, plus 9 more sections
  • Runs Python scripts from its folder

What it does

Bt2020 Png Generation is an agent skill from clshortfuse/renodx. RenoDX workflow for generating, validating, or debugging BT.2020/BT.2100 HDR PNG artifacts from EXR, linear RGB, nits arrays, scalar maps, or analysis images. Use when creating PQ/ST 2084 16-bit PNGs, injecting or checking PNG cICP chunks, converting AP0/D60 or BT.709 data to BT.2020, producing Discord/Chrome HDR PNG test files, testing HDR/PQ ICC profiles with cicpTag, or avoiding repeated one-off HDR image experiments.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `templates/bt2020_pq_png.py`).

It sits in Development. It works with Discord. The repository describes itself as: Renovation Engine for DirectX Games. The licence is MIT.

When your agent uses it

  • Creating PQ/ST 2084 16-bit PNGs
  • Checking PNG cICP chunks
  • Converting AP0/D60
  • BT.709 data to BT.2020

Example prompts

  • “/bt2020-png-generation”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Classify the input
  2. Convert to BT.2020 D65 linear RGB
  3. Map scene values to absolute nits
  4. Encode PQ/ST 2084
  5. Write the PNG correctly
  6. Validate every generated PNG

What it can do on your machine

Read from SKILL.md and the folder at commit c20d568. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bt2020 Png Generation loads about 2.8k tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 1,456 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from clshortfuse/renodx at commit c20d568, republished under its MIT licence (© clshortfuse). 1,456 words, ~2,828 tokens.

Download SKILL.mdSave it as .claude/skills/bt2020-png-generation/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
bt2020-png-generation
description
RenoDX workflow for generating, validating, or debugging BT.2020/BT.2100 HDR PNG artifacts from EXR, linear RGB, nits arrays, scalar maps, or analysis images. Use when creating PQ/ST 2084 16-bit PNGs, injecting or checking PNG cICP chunks, converting AP0/D60 or BT.709 data to BT.2020, producing Discord/Chrome HDR PNG test files, testing HDR/PQ ICC profiles with cicpTag, or avoiding repeated one-off HDR image experiments.
argument-hint
input image/data, source color space, diffuse white/peak nits, output path, and whether Discord/ICC preview behavior matters

BT.2020 HDR PNG Generation

Use this skill when generating BT.2020/BT.2100 HDR PNG analysis artifacts. The goal is to stop repeating one-off scripts for the same PNG/PQ/cICP plumbing.

  • Use hdr-test-pattern-generation first when the task is to design ramps, sweeps, charts, banding panels, checkerboards, or other synthetic validation inputs.
  • Use analysis-graphing when the task is to plot curves, hue sweeps, gamut comparisons, LUT statistics, or readable graph images.
  • Use this skill for the final BT.2020/PQ/cICP/ICC PNG output path and validation after the input image or analysis data already exists.

Boundaries

  • Focus on PNG/HDR image generation and validation, not shader changes.
  • Keep scratch outputs in a temporary output path unless the user asks for durable analysis artifacts.
  • Prefer a reusable script or helper under tools/analysis/ for repeated workflows; do not keep cloning the same write_png_rgb16_with_cicp and pq_oetf_st2084 code into new one-off experiments.
  • Do not embed graphing or synthetic-pattern design rules here; delegate those to analysis-graphing and hdr-test-pattern-generation.
  • If the work becomes shader-side tonemap/LUT math, switch to the SDR tonemap/LUT workflow instead.
  • Do not add ICC profiles, AVIF gain maps, JPEG metadata, or platform upload experiments unless the user explicitly asks for that output type. If they ask about Discord, Chrome preview, or Chromium issue 40239687, include the optional HDR ICC path below.

Canonical output

Generate a 16-bit RGB PNG whose pixel values are PQ/ST 2084 encoded absolute nits and whose PNG contains a native cICP chunk:

FieldValueMeaning
color primaries9 / 0x09BT.2020 / BT.2100 primaries
transfer characteristics16 / 0x10SMPTE ST 2084 PQ
matrix coefficients0 / 0x00RGB / identity matrix for PNG RGB samples
video full range flag1 / 0x01Full range

The PNG cICP payload bytes are therefore:

text
09 10 00 01

Do not use matrix coefficient 6 for RGB PNG samples. 9/16/6 is useful for YUV BT.2020 NCL encodes such as many AVIF/video paths, not native RGB PNG pixels.

Optional HDR ICC / Discord preview mode

Use this only when the user asks for Discord preview, Chrome HDR ICC, cicpTag, or Chromium issue 40239687 behavior. Keep the normal analysis output as native PNG cICP unless ICC compatibility is the point of the test.

Chromium issue 40239687 tracks Chrome's backwards-compatible HDR image support through a CICP tag embedded in an ICC profile. Chrome uses the ICC cicpTag to discover PQ/HLG HDR signaling for image formats such as JPEG, PNG, and WebP. The ICC transform itself should still be a reasonable SDR/tone-mapped fallback for decoders that ignore the cicpTag.

For the RenoDX Discord tests, the important behavior is:

PathPractical result
Native PNG cICP onlyGood for local RGB16 HDR PNG analysis, but Discord's WebP preview has no native CICP to carry forward.
PNG + HDR/PQ ICC with cicpTagDiscord may preserve the ICC into the generated 8-bit WebP preview, allowing Chrome to present the preview as HDR. Expect 8-bit PQ preview banding.
JPEG + HDR/PQ ICC with cicpTagSimilar signaling mechanism; verify the embedded ICC cicp tag matches the intended HDR signaling.
AVIF primary ICCUseful for Discord/Chrome experiments, but current Windows AVIF preview tests misdisplay or reject primary-ICC AVIFs.
Gain-map metadataDiscord/Lilliput preview does not preserve gain-map metadata through the transform path; use source ICC if preview HDR signaling is required.

Reference notes live in:

  • docs/AVIF_HDR_FINDINGS.md
  • Chromium issue 40239687: "Backwards-compatible HDR images via CICP in ICC profiles"

Rules for ICC mode:

  • Only attach a HDR/PQ ICC when the pixels are actually PQ-encoded HDR pixels in the matching gamut.
  • Do not tag SDR/gamma pixels with a HDR/PQ ICC.
  • For RGB PNG/WebP/JPEG HDR ICC signaling, the embedded ICC cicpTag should advertise BT.2020 primaries, PQ transfer, RGB/identity matrix, and full range: 9/16/0/full.
  • If both native PNG cICP and ICC are present, keep them semantically aligned. PNG native cICP is expected to take precedence, while Discord WebP preview relies on ICC because WebP lacks native CICP.
  • Treat Discord preview success as metadata-driven HDR over 8-bit WebP. It does not prove high-bit-depth precision survived the preview path.
  • Preserve a no-ICC/native-cICP output when the goal is a clean local analysis artifact rather than a Discord upload experiment.

Expected agent output

When invoked, produce these sections:

  1. Input assumptions - file/data source, source gamut, white point, transfer, and whether values are scene-linear, display-linear nits, or already PQ.
  2. Conversion path - matrices/adaptation used to reach BT.2020 D65 linear RGB.
  3. Nits mapping - diffuse white nits, peak/clip linear, and explicit clipping policy.
  4. PNG signaling - bit depth, PQ encode, cICP tuple, and whether ICC/profile metadata is intentionally absent or intentionally attached.
  5. Validation - stats, read-back cICP check, and any viewer/upload caveats.
  6. Reuse plan - existing utility/helper used, or the reusable helper that should be promoted instead of another one-off clone.

Step 1: Classify the input

Do not normalize or matrix-convert blindly. Identify the source first:

SourceRequired handling
AP0/D60 EXRAP0 to XYZ, chromatic-adapt D60 to D65, then XYZ to BT.2020.
ACEScg/AP1 D60AP1 to XYZ, chromatic-adapt D60 to D65, then XYZ to BT.2020.
BT.709/sRGB D65 linearConvert BT.709 to XYZ, then XYZ to BT.2020. Do not apply sRGB unless the input is explicitly nonlinear.
BT.2020 D65 linearUse directly after validating units and range.
Absolute display nitsUse directly for PQ encode after gamut/range checks.
Scalar/energy mapReplicate to RGB only after choosing the intended nits scale.
Already PQ encodedDo not PQ-encode again; only quantize/write and signal correctly.

Keep negative and out-of-gamut values long enough to report stats. Clip only at the explicit output boundary.

Show full SKILL.md (563 more words)Show less

Step 2: Convert to BT.2020 D65 linear RGB

Use stable, explicit matrices from existing analysis scripts when available. Existing references include:

  • tools/analysis/build_hdr_png_cicp.py for AP0/D60 EXR to BT.2020 PQ PNG and cICP read/write helpers.
  • tools/analysis/export_energy_bw_hdr_png.py and tools/analysis/export_energy_comparison_hdr.py for scalar/energy-map HDR PNG patterns.
  • tools/analysis/synthetic_neutwo_energy_reapply_bt2020.py for synthetic BT.2020 PQ outputs.

If the same helper code is needed in a new durable script, prefer extracting or reusing a shared utility instead of pasting another copy into a scratch script.

Step 3: Map scene values to absolute nits

Use explicit luminance mapping:

text
nits = max(bt2020_linear, 0) * diffuse_white_nits

Common RenoDX analysis defaults:

ParameterDefaultNotes
diffuse white100 nitsLinear 1.0 maps to reference white.
peak linear8.0Often means an 800 nit analysis peak at 100 nit diffuse white.
PQ maximum10000 nitsST 2084 encode domain.
minimum0 or 0.005 nitsUse 0.005 only when the experiment needs display black.

Do not silently scale image max to 1.0. If normalization is intentionally part of the experiment, name it and record it in stats.

Step 4: Encode PQ/ST 2084

Use the ST 2084 OETF constants consistently:

text
m1 = 2610 / 16384
m2 = 2523 / 32
c1 = 3424 / 4096
c2 = 2413 / 128
c3 = 2392 / 128
V = ((c1 + c2 * (L / 10000)^m1) / (1 + c3 * (L / 10000)^m1))^m2

Clamp nits to [0, 10000] before encoding, then quantize with rounding to uint16:

text
png16 = round(saturate(pq) * 65535)

Step 5: Write the PNG correctly

For a dependency-light path, write RGB16 PNG bytes directly:

  • PNG signature: 89 50 4E 47 0D 0A 1A 0A.
  • IHDR: bit depth 16, color type 2 (truecolor RGB), no interlace.
  • Store 16-bit samples big-endian.
  • Use filter type 0 per row unless testing filters/compression.
  • Insert cICP before IDAT with bytes 09 10 00 01.
  • Write IDAT and IEND with correct CRCs.

BT.2020 PQ PNG template contains a small reusable Python implementation for ST 2084 encoding and RGB16 PNG cICP writing. Prefer adapting it over cloning another scratch writer.

Avoid Pillow unless the current environment is known to preserve 16-bit RGB exactly. Many easy Pillow paths downcast, reorder, or treat RGB16 inconsistently.

Step 6: Validate every generated PNG

At minimum, report:

CheckExpected
Bit depth/color typeRGB, 16-bit.
cICP chunk(9, 16, 0, 1).
Optional ICC pathiCCP present, ICC contains cicp tag 9/16/0/full, and native cICP is not contradictory.
Nits mappingLinear 1.0 maps to the chosen diffuse white.
Clip statsNegative channels clipped, channels above 10000 nits clipped, nonfinite pixels excluded or replaced.
Round tripDecode a sample PQ value and verify the expected nits.

Useful stats keys:

text
pixels_total
source_min/source_max
bt2020_min/bt2020_max
negative_bt2020_channels_clipped
over_10000_nits_channels_clipped
diffuse_white_nits
peak_linear
cicp_cp_tc_mc_range
icc_present
icc_cicp_cp_tc_mc_range

Common mistakes to avoid

  • Do not write an 8-bit PNG for HDR precision tests unless testing an 8-bit preview path.
  • Do not use sRGB or gamma 2.2 encoding for HDR PNG pixels.
  • Do not tag SDR/gamma pixels as PQ/BT.2020.
  • Do not PQ-encode data that is already PQ.
  • Do not use 9/16/6 for native RGB PNG samples.
  • Do not assume a native PNG cICP chunk will survive Discord's WebP preview path.
  • Do not assume a Discord HDR preview means the preview kept more than 8-bit precision.
  • Do not attach a primary HDR ICC to AVIF when Windows preview compatibility matters.
  • Do not omit cICP and assume a viewer will infer HDR.
  • Do not hide clipping by normalizing the image max.
  • Do not add a new one-off script when the task is just EXR/linear/scalar to BT.2020 PQ PNG.

Promotion rule

If an experiment repeats any two of these pieces, make or reuse a common utility instead of a scratch clone:

  • ST 2084 PQ encode/decode.
  • RGB16 PNG byte writing.
  • PNG cICP insertion/read-back.
  • HDR ICC cicpTag insertion/read-back.
  • AP0/AP1/BT.709 to BT.2020 conversion.
  • clip/stat CSV generation.

© clshortfuse, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/bt2020-png-generation of clshortfuse/renodx.

  • SKILL.md
  • templates/bt2020_pq_png.py

Open the folder on GitHubat commit c20d568

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Works with

Categories

Questions about Bt2020 Png Generation

What does Bt2020 Png Generation do?

RenoDX workflow for generating, validating, or debugging BT.2020/BT.2100 HDR PNG artifacts from EXR, linear RGB, nits arrays, scalar maps, or analysis images. Bt2020 Png Generation is an agent skill from clshortfuse/renodx.2100 HDR PNG artifacts from EXR, linear RGB, nits arrays, scalar maps, or analysis images.

When should I use Bt2020 Png Generation?

Bt2020 Png Generation fits situations like: creating PQ/ST 2084 16-bit PNGs; checking PNG cICP chunks; converting AP0/D60; BT.709 data to BT.2020.

How do I install Bt2020 Png Generation in Claude Code?

Run `npx skills add clshortfuse/renodx --skill bt2020-png-generation -a claude-code`. Or copy the skill folder (.agents/skills/bt2020-png-generation in clshortfuse/renodx) into .claude/skills/bt2020-png-generation in your project. Claude Code loads it when a task matches its description.

How do I install Bt2020 Png Generation in Codex?

Run `npx skills add clshortfuse/renodx --skill bt2020-png-generation -a codex`. Or copy the skill folder (.agents/skills/bt2020-png-generation in clshortfuse/renodx) into .agents/skills/bt2020-png-generation in your project. Codex loads it when a task matches its description.

Can I use Bt2020 Png Generation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add clshortfuse/renodx --skill bt2020-png-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bt2020-png-generation, .gemini/skills/bt2020-png-generation, .github/skills/bt2020-png-generation and .opencode/skills/bt2020-png-generation in your project.

What does Bt2020 Png Generation need to run?

Going by SKILL.md and its folder, Bt2020 Png Generation needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Bt2020 Png Generation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bt2020 Png Generation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bt2020 Png Generation use?

Bt2020 Png Generation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bt2020 Png Generation use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bt2020 Png Generation?

Skills that share tags, products or a category with Bt2020 Png Generation: Coding Agent (TermiX-official/cryptoclaw, 100 stars), Release (pwrdrvr/openclaw-codex-app-server, 265 stars), Writing Release Notes (GoneTone/genshin-impact-wish-gacha-analyzer, 152 stars) and Code Review Standards (tiramisulabs/seyfert, 321 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bt2020 Png Generation?

clshortfuse (a GitHub user) maintains it in clshortfuse/renodx, which has 4,465 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 9, 2026.

Source: clshortfuse/renodx on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.