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Printing Press Score

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mvanhorn
printing-press-score

Score a generated CLI against the Steinberger bar, compare two CLIs side-by-side

Overview

Publishermvanhorn
Repositorycli-printing-press
Skill nameprinting-press-score
Stars
4.8K
Forks
515
Bundled files
Instructions only
LicenseMIT
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 mvanhorn on GitHub. Read the source before you install it.

Installation

Install the Printing Press Score 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/mvanhorn/cli-printing-press/tree/main/skills/printing-press-score
  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/mvanhorn/cli-printing-press.git /tmp/cli-printing-press
mkdir -p .claude/skills
cp -r /tmp/cli-printing-press/skills/printing-press-score .claude/skills/printing-press-score
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Printing Press Score 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 Printing Press Score 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 Printing Press Score 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.

/printing-press-score

Score generated CLIs against the Steinberger bar. Supports rescoring, scoring by name/path, and comparing two CLIs.

Quick Start

/printing-press-score                              # rescore current CLI
/printing-press-score notion-pp-cli-4              # score by name
/printing-press-score ~/my-cli                     # score by path
/printing-press-score notion-pp-cli-4 vs notion-pp-cli-2  # compare two

Prerequisites

  • Go 1.26.6 or newer installed
  • cli-printing-press binary on PATH (install with go install github.com/mvanhorn/cli-printing-press/v4/cmd/cli-printing-press@latest)

Step 0: Setup

Before any other commands, run the setup contract to verify the cli-printing-press binary is on PATH and initialize scope variables:

bash
# min-binary-version: 4.0.0

# Derive scope first — needed for local build detection
_scope_dir="$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")"
_scope_dir="$(cd "$_scope_dir" && pwd -P)"

# Prefer local build when running from inside the printing-press repo.
_press_repo=false
if [ -x "$_scope_dir/cli-printing-press" ] && [ -d "$_scope_dir/cmd/cli-printing-press" ]; then
  _press_repo=true
  export PATH="$_scope_dir:$PATH"
  echo "Using local build: $_scope_dir/cli-printing-press"
elif ! command -v cli-printing-press >/dev/null 2>&1; then
  if [ -x "$HOME/go/bin/cli-printing-press" ]; then
    echo "cli-printing-press found at ~/go/bin/cli-printing-press but not on PATH."
    echo "Add GOPATH/bin to your PATH:  export PATH=\"\$HOME/go/bin:\$PATH\""
  else
    echo "cli-printing-press binary not found."
    echo "Install with:  go install github.com/mvanhorn/cli-printing-press/v4/cmd/cli-printing-press@latest"
  fi
  return 1 2>/dev/null || exit 1
fi

# Resolve and emit the absolute path the agent must use for every later
# `cli-printing-press` invocation. `export PATH` above only affects this one
# Bash tool call; subsequent calls open a fresh shell and resolve bare
# `cli-printing-press` against the user's default PATH, where a stale global
# can silently shadow the local build. The agent captures this marker and
# substitutes the absolute path into every later invocation.
if [ "$_press_repo" = "true" ]; then
  PRINTING_PRESS_BIN="$_scope_dir/cli-printing-press"
else
  PRINTING_PRESS_BIN="$(command -v cli-printing-press 2>/dev/null || true)"
fi
echo "PRINTING_PRESS_BIN=$PRINTING_PRESS_BIN"

PRESS_BASE="$(basename "$_scope_dir" | tr '[:upper:]' '[:lower:]' | sed -E 's/[^a-z0-9_-]/-/g; s/^-+//; s/-+$//')"
if [ -z "$PRESS_BASE" ]; then
  PRESS_BASE="workspace"
fi

PRESS_SCOPE="$PRESS_BASE-$(printf '%s' "$_scope_dir" | shasum -a 256 | cut -c1-8)"
PRESS_HOME="${PRINTING_PRESS_HOME:-$HOME/printing-press}"
PRESS_RUNSTATE="$PRESS_HOME/.runstate/$PRESS_SCOPE"
PRESS_LIBRARY="$PRESS_HOME/library"
PRESS_MANUSCRIPTS="$PRESS_HOME/manuscripts"
PRESS_CURRENT="$PRESS_RUNSTATE/current"

mkdir -p "$PRESS_RUNSTATE" "$PRESS_LIBRARY" "$PRESS_MANUSCRIPTS" "$PRESS_CURRENT"

After running the setup contract, capture the PRINTING_PRESS_BIN=<abs-path> line from stdout. Every subsequent cli-printing-press ... invocation in this skill must use that absolute path (substitute the value, not the literal $PRINTING_PRESS_BIN token) — export PATH above only affects the single Bash tool call it runs in, so later calls open a fresh shell where bare cli-printing-press resolves against the user's default PATH and a stale global can shadow the local build.

After capturing the binary path, check binary version compatibility. Read the min-binary-version field from this skill's YAML frontmatter. Run <PRINTING_PRESS_BIN> version --json and parse the version from the output. Compare it to min-binary-version using semver rules. If the installed binary is older than the minimum, stop immediately and tell the user: "cli-printing-press binary vX.Y.Z is older than the minimum required vA.B.C. Run go install github.com/mvanhorn/cli-printing-press/v4/cmd/cli-printing-press@latest to update."

Current-run state is resolved from $PRESS_RUNSTATE. Published CLIs are resolved from $PRESS_LIBRARY. Archived manuscripts are resolved from $PRESS_MANUSCRIPTS.

Step 1: Parse Arguments

Read the user's input after /printing-press-score. The input is free-form — interpret intent, don't enforce syntax.

Noise words to strip: compare, vs, versus, and, against, with, to

After stripping noise words, count the remaining tokens:

  • 0 tokens → Rescore Current mode
  • 1 token → Score Single mode
  • 2 tokens → Compare mode

Step 2: Resolve CLI Directories

For each CLI identifier, resolve it to a directory path:

If the token contains / or .

Treat it as a path (absolute or relative). Verify the directory exists.

If the token is a plain name

Try these locations in order:

  1. $PRESS_LIBRARY/<name>/ — exact match
  2. $PRESS_LIBRARY/<name>-pp-cli/ — with -pp-cli suffix
  3. If neither exists, Glob $PRESS_LIBRARY/<name>-pp-cli*
  4. If exactly one glob match exists and is a directory, use it
  5. If multiple glob matches exist, present a numbered menu using AskUserQuestion

If neither exists, scan current-run and archived state: 6. Use Glob to find $PRESS_RUNSTATE/runs/*/state.json files 7. Read each, look for an output_dir or working_dir value whose basename contains the name 8. If found and the directory exists, use it

If nothing resolves, report the error: "Could not find CLI ''. Provide a path or check the name."

Rescore Current (0 tokens)

  1. Use Glob to find all $PRESS_CURRENT/*.json files
  2. Read each to get api_name, state_path, and working_dir
  3. Filter to those whose working_dir actually exists on disk
  4. If none are found, Glob $PRESS_LIBRARY/*-pp-cli* and use those directories instead
  5. If exactly one → use it automatically
  6. If multiple → present a numbered menu using AskUserQuestion:
    Multiple CLIs found. Which one to score?
    1. stripe-pp-cli ($PRESS_LIBRARY/stripe-pp-cli)
    2. notion-pp-cli ($PRESS_LIBRARY/notion-pp-cli)
    3. linear-pp-cli ($PRESS_LIBRARY/linear-pp-cli)
  7. If none found → report: "No generated CLIs found. Provide a name or path."

Step 3: Find Spec for Tier 2 Scoring

For each resolved CLI directory, find the OpenAPI spec:

  1. Check <cli-dir>/spec.json — the pipeline converts YAML specs to JSON during generation
  2. If not found, scan $PRESS_RUNSTATE/runs/*/state.json files for one matching this CLI's directory. Read its spec_path field. If that file exists on disk, use it.
  3. If no spec found, proceed without --spec. Note to the user: "No spec found — spec-derived dimensions will be marked N/A and omitted from the denominator. Provide a spec path for full scoring."

Step 4: Run Scorecard

Single Score Mode

Run the scorecard command:

bash
cli-printing-press scorecard --dir <resolved-path> --json

If a spec was found, add --spec <spec-path>.

Parse the JSON output. The structure is:

json
{
  "api_name": "...",
  "steinberger": {
    "output_modes": 8,
    "auth": 7,
    "error_handling": 6,
    "terminal_ux": 9,
    "readme": 5,
    "doctor": 10,
    "agent_native": 7,
    "local_cache": 4,
    "breadth": 7,
    "vision": 6,
    "workflows": 3,
    "insight": 5,
    "path_validity": 0,
    "auth_protocol": 0,
    "data_pipeline_integrity": 7,
    "sync_correctness": 6,
    "type_fidelity": 4,
    "dead_code": 3,
    "total": 72,
    "percentage": 72
  },
  "overall_grade": "B",
  "gap_report": ["..."],
  "unscored_dimensions": ["path_validity", "auth_protocol"]
}

If unscored_dimensions is present, those dimensions should be rendered as N/A, not 0/x, and should be described as omitted from the denominator rather than as fixable CLI defects. For backward compatibility, JSON still encodes the numeric fields as 0; consumers must use unscored_dimensions to distinguish N/A from a real zero.

Learn-loop credit is static-behavioral, never presence-based: the scorecard credits teach/recall/learnings registration on the root command and non-empty entity lookup seeds (or the spec's recorded no-entities escape). The learn loop is default-on, so internal/learn/ existing earns nothing; when explaining a learn gap, point at missing seeds or unregistered commands, not missing files. Execution proof (verify matrix, learnings stats) belongs to verify and dogfood; the scorecard runs no binaries.

Compare Mode

Run both scorecard commands in parallel using two simultaneous Bash tool calls:

bash
# Call 1:
cli-printing-press scorecard --dir <path1> --spec <spec1> --json

# Call 2:
cli-printing-press scorecard --dir <path2> --spec <spec2> --json

Parse both JSON outputs.

Step 5: Render Output

Single Score Table

Render a rich markdown table. Note: Tier 1 dimensions are all /10. Tier 2 dimensions are /10 except TypeFidelity and DeadCode which are /5.

Scorecard: <api_name>

Infrastructure (Tier 1)
| Dimension      | Score |
|----------------|-------|
| Output Modes   | 8/10  |
| Auth           | 7/10  |
| Error Handling | 6/10  |
| Terminal UX    | 9/10  |
| README         | 5/10  |
| Doctor         | 10/10 |
| Agent Native   | 7/10  |
| Local Cache    | 4/10  |
| Breadth        | 7/10  |
| Vision         | 6/10  |
| Workflows      | 3/10  |
| Insight        | 5/10  |

Domain Correctness (Tier 2)
| Dimension               | Score |
|--------------------------|-------|
| Path Validity            | 9/10  |
| Auth Protocol            | 8/10  |
| Data Pipeline Integrity  | 7/10  |
| Sync Correctness         | 6/10  |
| Type Fidelity            | 4/5   |
| Dead Code                | 3/5   |

**Total: 72/100 — Grade B**

If gap_report is non-empty, list the gaps:

Gaps:
- <gap 1>
- <gap 2>

If unscored_dimensions is non-empty, add a note after the table:

Note: path_validity, auth_protocol were unscored and omitted from the denominator. Provide a spec path for full scoring.

Compare Table

Render a side-by-side table with a delta column. Show the first CLI name and second CLI name as column headers. Calculate delta as (CLI 1 score - CLI 2 score). Show +N for positive, -N for negative, — for zero.

Scorecard Comparison: <name1> vs <name2>

Infrastructure (Tier 1)
| Dimension      | <name1> | <name2> | Delta |
|----------------|---------|---------|-------|
| Output Modes   | 8/10    | 5/10    | +3    |
| Auth           | 7/10    | 7/10    | —     |
| ...            |         |         |       |

Domain Correctness (Tier 2)
| Dimension               | <name1> | <name2> | Delta |
|--------------------------|---------|---------|-------|
| Path Validity            | 9/10    | 6/10    | +3    |
| ...                      |         |         |       |

| **Total**  | **72/100 (B)** | **56/100 (C)** | **+16** |

Error Handling

  • If the cli-printing-press binary is not on PATH → show install instructions: go install github.com/mvanhorn/cli-printing-press/v4/cmd/cli-printing-press@latest
  • If the scorecard command fails → report the error with the full stderr output
  • If a CLI directory doesn't exist → report which name couldn't be resolved
  • If JSON parsing fails → show the raw output and report the parsing error

Frequently asked questions

What does the Printing Press Score AI skill do?

Score a generated CLI against the Steinberger bar, compare two CLIs side-by-side

Why use Printing Press Score on TypingMind?

Because you install it once and use it with any model. Printing Press Score 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 Printing Press Score in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvanhorn/cli-printing-press/tree/main/skills/printing-press-score. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Printing Press Score?

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 Printing Press Score?

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

Is the Printing Press Score AI skill free?

Yes. It is published on GitHub by mvanhorn under the MIT 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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