deck-pipeline

deck-pipeline is a skill for Claude Code, Codex from TinaDu-AI/deck-pipeline. It costs 162 tokens per session (4,547 once invoked), scanned A, original, MIT.

A four-stage process for turning a Chinese presentation deck into polished English, with checks for meaning, style, layout, and handoff. A deck is a set of presentation slides, and a layout audit checks whether translated text still fits correctly.

In plain words
What is it for?
Use it for full Chinese-to-English deck globalization, English-only layout polishing, or syncing edits made after a comparison spreadsheet. The input does not specify the presentation-file formats.
Why use it?
It keeps translation and visual review together, records what happened at each stage, and supports projects with different brand settings such as fonts, colours, and terminology.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code.

Good fit Use it for full Chinese-to-English deck globalization, English-only layout polishing, or syncing edits made after a comparison spreadsheet. The input does not specify the presentation-file formats.

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Install with agentmods
npx agentmods add skills/tinadu-ai/deck-pipeline/deck-pipeline
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add TinaDu-AI/deck-pipeline --skill deck-pipeline
Clone the repo
git clone --depth 1 https://github.com/TinaDu-AI/deck-pipeline

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for deck-pipeline

README.md
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Per session 162 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,547 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00162 $0.04547
Opus 5 $0.00081 $0.02273
Sonnet 5 $0.00032 $0.00909
Haiku 4.5 $0.00016 $0.00455

Measured 12d ago against content hash e3140a5dd370, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

deck-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

SKILL.md · 441 lines

How it starts

The opening of the file, as written. The whole thing — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deck Pipeline

A 4-stage, audit-trailed Chinese→English deck globalization system with a swappable project profile.

This skill bundles the generic deck-globalization engine (originally upstream DeckGlobalizer v2.1.1) and an editable PROFILE block (palette, fonts, glossary, style preferences). The two are separated by section so the profile can be swapped per project / brand without touching the engine.

For a marketing-style overview, see README.md in this directory. For implementation, see scripts/ and the per-stage runbooks below.


0. Modes & activation

Mode Trigger Stages
Full pipeline CN deck (± EN draft) + user wants English output 1 → 2 → 3 → 4
Polish-only Single-language deck + "layout / format only / skip translation" 1 → 3 → 4
Reverse-sync only User hand-edited a PPT after a comparison Excel was generated 3.5 (sync sub-routine)

Detect the mode in the first turn. If ambiguous, ask one yes/no question ("This deck is already in EN — should I just polish layout, or also rewrite McKinsey-style?"). Do not guess silently.


1. PROFILE block — defaults (swappable)

Edit this block to retarget the skill for your project / brand. Everything below this block is profile-agnostic.

PROFILE:
  # ---- L1 Tokens ----
  palette:
    # Replace with your brand colors.
    ink:          "#1A1A1A"
    primary:      "#000000"   # accent / brand primary
    soft:         "#FFFFFF"   # soft fill behind banners
    page_bg:      "#FFFFFF"
  fonts:
    # Choose a serif title face + a sans-serif body face for best contrast.
    title:        "Georgia"
    body:         "Verdana"
    title_bold:   true
  unit_table:
    # Chinese number magnitudes → English. 亿 is 100M, NOT "billion".
    "百万":       "M"
    "千万":       "10M"
    "亿":         "100M"
    "十亿":       "1B"
    "百亿":       "10B"
    "千亿":       "100B"
    "万亿":       "1T"
    # Currency suffix is left to the user — append "$" / "RMB" / "€" as appropriate.

  # ---- L2 Constants ----
  size_ladder:           [22, 14, 10, 8, 6, 4]   # H1, H2, body, caption, footnote, source
  floors:
    body:     7
    caption:  6
    source:   4
  compression_step:      0.1     # discrete -0.1pt iterations only
  line_height_default:   1.25
  line_height_fallback:  1.15    # used before sub-floor compression
  quote_style:           "single"  # 'McKinsey' single quotes
  footer_format:         "Confidential · For Intended Recipients Only · {month} {year}"
  separator_in_footer:   "·"     # middle dot, NOT em-dash

  # ---- L1 Glossary (extensible) ----
  # Replace the example entries below with your project's locked terms.
  # Categories are illustrative; you can rename / add / remove.
  glossary:
    locked:
      people_orgs:
        # "<source term>": "<canonical translation>"
        # e.g. "John Smith": "John Smith"
        # e.g. "Acme Capital": "Acme Capital"
        {}
      business_terms:
        # Common Chinese business-deck idioms with industry-standard
        # English mappings. Edit / extend as needed.
        "流水":     "gross revenue"
        "私域":     "owned audience"
        "出海":     "global expansion"
      domain_specific:
        # Project / industry / domain terms.
        # "<source term>": "<canonical translation>"
        {}
    rejected_rewrites:
      # Entries the user vetoed during prior sessions.
      # Format: { source: "...", proposed: "...", reason: "..." }
      []
    pending: []
    session_added: []

  # ---- Style rules ----
  # McKinsey is the default baseline. Additional style references can be
  # uploaded and distilled via scripts/style_distill.py; their rules layer
  # ON TOP of the McKinsey base.
  style_baseline: "mckinsey"
  mckinsey:
    title_is_takeaway:      true   # title = the so-what, not the topic
    lead_with_so_what:      true
    parallel_structure:     true   # bullets share tense, opening part-of-speech
    strong_action_verbs:    true   # cut "is/has", prefer concrete verb
    cut_filler:
      - "in order to → to"
      - "a number of → many"
      - "due to the fact that → because"
      - "at this point in time → now"
    case:                   "sentence"   # lowercase unless proper noun or locked term
    em_dash_policy:         "use em-dash for parentheticals; use · (middle dot) in lists/footers"
  style_references:
    # Each entry is a PDF / .pptx reference. style_distill.py reads it and
    # emits rules (cadence, signature phrases, paragraph length, tone) that
    # layer on top of the McKinsey base. Conflicts: more recent entry wins;
    # user is asked at first conflict.
    # Example:
    # - path: "/path/to/sample.pdf"
    #   weight: 0.7
    []

  # ---- Structural anchor heuristics ----
  anchor_detection:
    min_pages: 3              # appears on ≥3 slides
    match_on:                 # signature components
      - position_xy
      - fill_color
      - font_size_class
    auto_protect: true

  # ---- Overflow estimator ----
  overflow:
    severity:
      high: 1.5
      med:  1.15
      low:  1.0
    surface_only: "high"      # surface MED/LOW only when explicitly asked
    defer_to_user_threshold: 10   # if HIGH > 10 → ask user to render externally

  # ---- CN ↔ EN slide alignment ----
  # Default is 1:1 (EN slide N maps to CN slide N).
  # Set overrides only when the two decks have been restructured.
  # Pass this config to excel_sync.py via `--cn-offset <yaml>`.
  cn_en_slide_offset:
    default: 0           # offset added to EN slide number (0 = 1:1)
    overrides: {}        # e.g. {"9-26": -1, "20": null}
                         # int = relative offset; null = no CN counterpart

Read the full file on GitHub · 441 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 441 lines · 162 tokens per session scan A e3140a5dd370

Subscribe to this mod's changes

deck-pipeline is a skill published in the GitHub repository TinaDu-AI/deck-pipeline (2 stars, last pushed 4mo ago), licensed MIT. It adds 162 tokens to every session and 4,547 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.