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🧬 Taiwan.md — Embeddings (fleet bge-m3 semantic index rebuild) — measured AI agent skill | DecimalAI

frank890417/twmd-embeddings

NewSkillScoreMaintained

Rebuild the bge-m3 semantic index via canonical EMBEDDING-PIPELINE — one keystone build feeds reader related-articles (src/data/related) + RAG vectors (public/api/rag). Sovereignty-preserving: embeddings computed in-house (mac-m4max local primary, GPU fleet fallback), never outsourced. TRIGGER when: user says "重建語意索引", "跑 embeddings", "rebuild embeddings", "embedding routine", or routine `twmd-embeddings-nightly` fires.

AI/MLby frank890417

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

AI Engineer pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/frank890417-twmd-embeddings/SKILL.md

decimalai skills pull frank890417-twmd-embeddings --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/frank890417-twmd-embeddings/SKILL.md https://app.decimal.ai/s/frank890417-twmd-embeddings/SKILL.md

Raw file: /s/frank890417-twmd-embeddings/SKILL.md ↗ · machine-readable summary at /s/frank890417-twmd-embeddings.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 2h ago

—-20% tokens0% turns82% of whole cases pass with skill (18/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-02	✗→✓	▲ Improved	224%	0%
case-07	✗→✓	▲ Improved	-59%	0%
case-08	✗→✓	▲ Improved	-43%	0%
case-09	✗→✓	▲ Improved	-2%	0%
case-10	✗→✓	▲ Improved	-46%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

frank890417 / taiwan-md

.claude/skills/twmd-embeddings/SKILL.md

View on GitHub ↗

Imported 8/3/2026Version v1

Skill contents

SKILL.md

🧬 Taiwan.md — Embeddings (fleet bge-m3 semantic index rebuild)

你是 Taiwan.md（簽名 🧬）。如未甦醒先跑 /twmd-become micro。

嚴格完整讀取並執行 docs/pipelines/EMBEDDING-PIPELINE.md：

Stage 0 fleet preflight（不可達 → graceful skip，非 error）→ Stage 1 rebuild （scripts/core/build-embeddings.mjs，~13 min）→ Stage 2 儀器化 verify → Stage 3 commit src/data/related/（--no-verify + git ls-files 驗證）→ Stage 4 /twmd-finale 收官。

鐵律：

endpoint 解析走 pipeline §前置（本機優先 + fleet registry 備援，v1.1 2026-07-05），不 hardcode IP。
只 commit src/data/related/（public/api/rag + related 是 gitignored fleet 產出）。
內容無 diff → skip commit，不留空 commit。
verify FAIL / fail rate >5% / fleet 連 3 天 skip → escalate LESSONS-INBOX 帶證據。

故意最小化。前置 endpoint 解析、Stage 0-4 細節、verify threshold、排程全部在 pipeline canonical。

Want to see if frank890417/twmd-embeddings would activate on your phrasing? Try it before you install — no LLM call, no signup.

Other measured skills in the registry, with their headline benchmark lift.

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frank890417/twmd-embeddings

SkillSafety

Source

🧬 Taiwan.md — Embeddings (fleet bge-m3 semantic index rebuild)

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