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.
npx skills add TinaDu-AI/deck-pipeline --skill deck-pipelinegit clone --depth 1 https://github.com/TinaDu-AI/deck-pipelineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tinadu-ai/deck-pipeline/deck-pipeline)<a href="https://agentmods.dev/skills/tinadu-ai/deck-pipeline/deck-pipeline"><img src="https://agentmods.dev/badge/skills/tinadu-ai/deck-pipeline/deck-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tinadu-ai/deck-pipeline/deck-pipeline"><img src="https://agentmods.dev/badge/skills/tinadu-ai/deck-pipeline/deck-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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.
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
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.
- 12d ago First seen · 441 lines · 162 tokens per session scan A e3140a5dd370
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.
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