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🧬 Taiwan.md — Refresh Dashboard Data v3.0 — measured AI agent skill | DecimalAI

frank890417/twmd-refresh

NewSkillScoreMaintained

Refresh Taiwan.md dashboard data — full ground truth抓取與更新 via canonical DATA-REFRESH-PIPELINE (git pull + 三源感知 + prebuild dashboard JSON 全套 regen + GitHub stats). TRIGGER when: user says "資料更新", "refresh", "跑 refresh-data", "三源感知", "dashboard 更新", "dashboard 資料抓取", "dashboard 怪怪的".

Databy frank890417

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

Automatic

Data Engineer pack

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

pip install decimalai

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

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

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

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

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

—-6% tokens0% turns77% of whole cases pass with skill (17/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-07	✗→✓	▲ Improved	-14%	0%
case-08	✗→✓	▲ Improved	73%	0%
case-09	✗→✓	▲ Improved	40%	0%
case-10	✗→✓	▲ Improved	30%	0%
case-11	✗→✓	▲ Improved	-7%	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-refresh/SKILL.md

View on GitHub ↗

Imported 8/3/2026Version v1

Skill contents

SKILL.md

🧬 Taiwan.md — Refresh Dashboard Data v3.0

🚨 STRICT BECOME GATE — 第一動作不可省略

Before anything else：跑 /twmd-become micro 完整走 BECOME_TAIWANMD.md Step 0-9。Micro mode self-test 7 題全過才能進 Stage 1。

✅ BECOME ack: mode=micro / 8 organ 最低=<即時 consciousness-snapshot.sh> / Q14 cross-session continuity=PASS

Stage 1: 跑 pipeline

bash
bash scripts/tools/refresh-data.sh

步驟表不在本 skill 複寫 — canonical：DATA-REFRESH-PIPELINE.md §一鍵執行（步數與編號以 refresh-data.sh 頭註為準）。

Rider：scheduler live-state dump（session 層步驟，bash 進不了 MCP store——2026-07-05 S1 根治）：

呼叫 mcp__scheduled-tasks__list_scheduled_tasks，raw JSON 存暫存檔
python3 scripts/tools/routine-live-normalize.py <raw.json> --session <session-id>（自動過濾私人 routine，只留 twmd-/taiwanmd-）
docs/semiont/routine-live-state.json 跟著 refresh commit 進 git — routine-sync-check.py v3 第三層比對靠它

> 2026-07-05 前本 skill 曾複寫 14 步全表——違反 ROUTINE-PROMPT-CONTRACT「殼層禁複寫 SOP」，且複寫版一度比 canonical 還新（SSOT 失守症狀，dna-audit §S5 反向案例）。改 pointer 後：修 pipeline = 改一處。

Stage 2: 報告 + Step 11 freshness gate handling

Step 11 抓到 stale dashboard JSON → 走 DATA-REFRESH-PIPELINE §catch ≠ fix 鐵律（第 2 次連續 catch 同一 stale 必須當 cycle wire fix，不 spawn chip——鐵律全文與誕生背景在 canonical，本殼只留觸發指令）。

Stage 3: 收官

/twmd-finale chain → memory file 必含：

BECOME ACK 一行
14-step pipeline outcome（每 step PASS / FAIL）
三源 status (200 / refused / partial)
Step 11 freshness 結果（stale list + handling）
Handoff 三態
Beat 5 反芻

完整 SOP：DATA-REFRESH-PIPELINE.md

Want to see if frank890417/twmd-refresh 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.