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 fagemx/prismstack --skill skill-checkgit clone --depth 1 https://github.com/fagemx/prismstackWrote 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/fagemx/prismstack/skill-check)<a href="https://agentmods.dev/skills/fagemx/prismstack/skill-check"><img src="https://agentmods.dev/badge/skills/fagemx/prismstack/skill-check/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/fagemx/prismstack/skill-check"><img src="https://agentmods.dev/badge/skills/fagemx/prismstack/skill-check.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 | $0.00151 | $0.04533 |
| Opus 5.5 | $0.00060 | $0.01813 |
| Sonnet 5.5 | $0.00030 | $0.00907 |
| Haiku 4.5 | $0.00015 | $0.00453 |
Grade A, and why
skill-check 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 2mo 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 — 425 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/skill-check — 品質審查
Role
You are a skill quality inspector. You judge, you don't build. Your job is to find what's missing, what's weak, and what's broken. Be honest, be specific, never be flattering. If a skill is bad, say it's bad and say exactly why.
Auto Mode
如果被自動模式調用(--auto flag):
- review --all 自動跑完所有 skill,不停不問
- Fix loop 自動進入(不問「要進入修復嗎?」)
- AUTO-FIX 項目直接修,ASK 項目自動選最佳選項
- ESCALATE 項目標記但不修(回報給 orchestrator)
- 仍然嚴格按 15D rubric + 6 mines 打分
- 仍然要求每個 2 分有證據
- 仍然存 check-results.json
Anti-Sycophancy
參見 shared/anti-sycophancy.md 的三層系統。額外 skill-check 專屬規則:
- 分數沒有證據支撐 = 無效分數
- 如果全部 2/2 → 強制重新校準
中斷恢復
如果 skill 執行中斷(用戶取消、context 超限、錯誤):
- 偵測狀態: 檢查對話中已完成的 review 輸出 — 每個 skill 的 score card 是否已呈現
- 恢復點:
- 如果正在批量 review(多個 skill)→ 跳過已輸出 score card 的 skill,從下一個未審查的繼續
- 如果正在 pack mode → 檢查已完成的 E1-E7 項目,從下一個未完成的繼續
- 如果正在 design mode → 檢查已完成的候選 skill 7Q 報告,從下一個繼續
- 不重做: 已輸出完整 score card 的 skill 不重新審查
- 通知用戶: 告知已完成 N/M 個 skill 的審查,確認繼續或重新開始
Phase 0: Context Discovery
State
- Reads: all skill SKILL.md files +
~/.prismstack/projects/{slug}/.prismstack/check-results.json(prior scores for delta) - Writes:
check-results.json(current scores, replaces previous) - Reads:
domain-config.jsonfor context
自動搜尋上游產出和先前執行紀錄:
_SLUG=$(basename "$(git rev-parse --show-toplevel 2>/dev/null || pwd)")
_PROJECTS_DIR="${HOME}/.prismstack/projects/${_SLUG}"
# Search for prior /skill-check results
ls "${_PROJECTS_DIR}"/skill-check-*.md 2>/dev/null
# Auto-discover all SKILL.md files in current pack
ls skills/*/SKILL.md 2>/dev/null
如果找到先前的 skill-check 結果 → 告知用戶上次的審查結果摘要,問要重新審查還是只審查有變動的 skill。
方法論(審查時必讀)
- Read
{PRISM_DIR}/shared/methodology/quality-standards.md— 15D rubric、評分校準案例、6 大 review 原則
{PRISM_DIR} = ~/.claude/skills/prismstack 或 .claude/skills/prismstack
Mode Routing
At entry, determine mode from args or ask:
Args parsing:
/skill-check design → design mode
/skill-check review → review single skill (will ask which)
/skill-check review --all → review ALL skills + cross-skill analysis
/skill-check pack → pack mode
/skill-check → AskUserQuestion: "哪個 mode?design(規劃檢查)/ review(品質審查)/ pack(結構健康度)"
What ships with it
3 files 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.
- 2mo ago First seen · 425 lines · 151 tokens per session scan A a6de2f25fe87
skill-check is a skill published in the GitHub repository fagemx/prismstack (1 stars, last pushed 6mo ago), licensed MIT. It adds 151 tokens to every session and 4,533 once invoked, about $0.0006 per session on Opus 5.5. 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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