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Emitting Output

OrganizationPopular
redhat-et
emitting-output

Writing or converting formatted output in ripwire's C++ source — std::print/std::format via rw::emitTo, emitRaw and formatTo, which printf specifier maps to which format spec, and how to prove a conversion moved zero bytes. Use when adding any output call site, converting a printf-family one, or touching a help/legend string.

Overview

Publisherredhat-et
Repositoryripwire
Skill nameemitting-output
Stars
2.4K
Forks
151
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by redhat-et on GitHub. Read the source before you install it.

Installation

Install the Emitting Output AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/redhat-et/ripwire/tree/main/.claude/skills/emitting-output
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/redhat-et/ripwire.git /tmp/ripwire
mkdir -p .claude/skills
cp -r /tmp/ripwire/.claude/skills/emitting-output .claude/skills/emitting-output
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Emitting Output in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Emitting Output on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Emitting Output is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Emitting output in ripwire

Every byte this tool prints feeds G4 (minified XML), the determinism contract, docs/EVALS.md's recorded numbers, and the stored captures. Output code is therefore held to byte-exactness, not taste.

The three primitives (src/infra/emit.h)

You haveUseWhy
A format string with arguments, going to a streamrw::emitTo( stream, "…{}…", args )The choke point: std::print where the library has it, std::format+fputs where it does not
Literal text, no argumentsrw::emitRaw( stream, "…" )std::format_string is consteval; there is nothing to format, and huge literals do not compile
A caller-owned char bufferrw::formatTo( buf, cap, "…{}…", args )Keeps the stack buffer — std::format into a std::string puts an allocation on hot paths (G2)

Never add a printf-family call site (CONTRIBUTING.md §3). Do not vendor fmt — the standard library has the feature, so a vendored copy is a G3 regression.

The specifier mapping — measured, not remembered

Proven byte-identical on this toolchain (218 checks; see the lane's fmtparity.cpp):

%s %u %d %zu %zd %lu %ld %llu %lld %i   ->  {}
%.*s  (precision, pointer)               ->  {}  with std::string_view( ptr, len )
%10s -> {:>10}      %-11s -> {:<11}      %.9s -> {:.9}
%016llx -> {:016x}  %llx -> {:x}  %llX -> {:X}  %o -> {:o}
%.3f -> {:.3f}      %6.1f -> {:6.1f}     %.0f -> {:.0f}
%.6g -> {:.6g}      %.3g -> {:.3g}       (explicit precision only)

The one unsafe mapping. A %g or %f with no precision is NOT {}. printf's %g prints six significant digits; {} prints the shortest round-trip. 0.1+0.2 is 0.3 under %g and 0.30000000000000004 under {}. Give the format spec an explicit precision or leave the site alone.

Three traps that are invisible at the call site

  1. %% inverts. In a printf format a literal percent is %%. Text handed to emitRaw is no longer a format, so %% there prints two characters — collapse it to one %. This is how garbage reached a generated docs/COMMANDS.md.
  2. Braces invert the other way. emitTo needs {{ and }} for literal braces; emitRaw needs bare { and }. JSON emitters are where this bites.
  3. format_to_n is not snprintf. snprintf( p, S, … ) writes at most S-1 chars plus a NUL; format_to_n( p, S, … ) writes up to S and terminates nothing. Swapping one for the other widens the buffer by a byte and drops the terminator. Use rw::formatTo, whose contract is snprintf's exactly — including its return value, the would-have-written length.

Proving a conversion moved zero bytes

  1. bash test/printffmtparitycheck.sh — per-verb SHA-256 of stdout and stderr. If it does not have a label for the verb you touched, it proves nothing about your change: add the label and pin it first. UPDATE_GOLDEN=1 re-pins; the diff must be additions only. Pinning refuses any verb whose output embeds the git stamp (at="<sha>"), because such a verb cannot hold a pin at all.
  2. A green fence is not coverage. Measure it: a coverage build showed only 25% of one batch's call sites were ever executed by the corpus. Widen at the gaps — --json is a whole second output path.
  3. Differential-test against the pre-conversion binary on a REAL tree. Build the base commit into a scratch worktree and diff both binaries' bytes over src/, test/, docs/ and the repo root, normalising only the git stamp. A toy fixture cannot reach a truncation branch; a real tree does it by accident. This is what caught the format_to_n byte, with all 40 labels green.

Frequently asked questions

What does the Emitting Output AI skill do?

Writing or converting formatted output in ripwire's C++ source — std::print/std::format via rw::emitTo, emitRaw and formatTo, which printf specifier maps to which format spec, and how to prove a conversion moved zero bytes. Use when adding any output call site, converting a printf-family one, or touching a help/legend string.

Why use Emitting Output on TypingMind?

Because you install it once and use it with any model. Emitting Output is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Emitting Output in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/redhat-et/ripwire/tree/main/.claude/skills/emitting-output. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Emitting Output?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Emitting Output?

As many as you like. As long as a model supports skills, you can use Emitting Output with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Emitting Output AI skill free?

Yes. It is published on GitHub by redhat-et under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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