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 nguyenvanphituoc/shapeup-sdlc-plugin --skill translatorgit clone --depth 1 https://github.com/nguyenvanphituoc/shapeup-sdlc-pluginWrote 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/nguyenvanphituoc/shapeup-sdlc-plugin/translator)<a href="https://agentmods.dev/skills/nguyenvanphituoc/shapeup-sdlc-plugin/translator"><img src="https://agentmods.dev/badge/skills/nguyenvanphituoc/shapeup-sdlc-plugin/translator.svg" alt="Measured on agentmods" 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 | $0.00168 | $0.03100 |
| Opus 5 | $0.00084 | $0.01550 |
| Sonnet 5 | $0.00034 | $0.00620 |
| Haiku 4.5 | $0.00017 | $0.00310 |
Grade A, and why
translator 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 7d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Translator (harness language gate)
The one job upstream of the whole harness: make the input English, faithfully, once. The harness is English-only end to end — the planner, generator, and judge all assume English and HARD-FAIL on anything else. This skill is the single component that guarantees that assumption holds. It is not a translator that rewrites; it normalizes language while preserving every requirement, number, boundary, and structural element 1:1.
PO writes intake (any language) → TRANSLATOR → <name>.en.md (English) → ba-pitch-analyzer → …
(this skill) + glossary.md + report
Scope is deliberately narrow (simplicity first):
- ✅ Translate input docs (pitch / PRD / requirement / transcript) into English copies.
- ✅ Emit a persisted glossary so domain terms map consistently across docs and runs.
- ✅ Verify the output is English and structurally identical to the source.
- 🚫 Does NOT plan, build, or judge — that is the harness's job.
- 🚫 Does NOT translate harness output back to the PO's language (input-normalization only).
- 🚫 Does NOT summarize, add, drop, or "improve" content — faithful 1:1 only.
What is preserved verbatim vs translated, glossary protocol, verification scan →
references/preservation-rules.md— read it before translating any file.
Workflow Overview
INPUT: doc(s) in any language
│
⏸ GATE T0 │ Detect & Scope ──────► per-file source-language detection; list files to
│ translate; already-English files → skipped (idempotent)
│
▶ Phase 1 │ Glossary ────────────► extract domain terms + proper nouns + do-not-translate
│ tokens; map source→English; reuse existing glossary.md
⏸ GATE T1 │ Glossary Review ──────► PO confirms term mappings; ambiguous terms resolved here
│ (never guessed). lite/auto: auto-proceed on unambiguous.
│
▶ Phase 2 │ Translate ───────────► WRITE <name>.en.md (original untouched): prose → English,
│ glossary applied; frontmatter keys, wikilinks, code, URLs,
│ numbers preserved
│
▶ Phase 3 │ Verify ──────────────► residual-non-English scan + structural diff (heading /
│ link / code-block / list-item counts must match source)
⏸ GATE T2 │ Output Sign-off ──────► confirm clean scan + matching structure, then emit
│ glossary.md + translation-report.md (the .en.md is already
│ written in Phase 2)
✅ Done └─► English intake ready for the harness
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 259 lines · 168 tokens per session scan A 04423ef02839
translator is a skill published in the GitHub repository nguyenvanphituoc/shapeup-sdlc-plugin (2 stars, last pushed 3d ago), licensed MIT. It adds 168 tokens to every session and 3,100 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.
Other skills, from other repositories
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pdlc-i18n
An internationalization workflow for adding multiple languages to frontend and backend software. Internationalization, often called i18n, means keeping user-facing text and formatting adaptable to different languages and regions.
sheltered-instruction-lesson-modifier
Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.
localization-authority-audit
Use when auditing localization source authority, generated copies, translation keys, missing or extra entries, mismatched text, encoding, mojibake, or client and server localization drift.
narrative-quest-content-contract
Use when narrative, quest, dialogue, objective, state, reward, localization, cinematic, and implementation teams need one explicit content contract; not for general story brainstorming or protocol review.