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 s0912758806p/agentic-sop-to-work --skill acceptgit clone --depth 1 https://github.com/s0912758806p/agentic-sop-to-workWrote 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/s0912758806p/agentic-sop-to-work/accept)<a href="https://agentmods.dev/skills/s0912758806p/agentic-sop-to-work/accept"><img src="https://agentmods.dev/badge/skills/s0912758806p/agentic-sop-to-work/accept/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/s0912758806p/agentic-sop-to-work/accept"><img src="https://agentmods.dev/badge/skills/s0912758806p/agentic-sop-to-work/accept.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 | $0.00043 | $0.00404 |
| Opus 5 | $0.00022 | $0.00202 |
| Sonnet 5 | $0.00009 | $0.00081 |
| Haiku 4.5 | $0.00004 | $0.00040 |
Grade A, and why
accept scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Skill — accept（終端節點，不信任路由）
圖示範流程的終端節點。刻意不信任路由：即使編排把一份 reject 的判定送到這裡
（例如有人手改了 flow.json 的 branch 條件），這一步仍拒絕通過。
閘門查真相，不查「我是被誰叫來的」。
綁定的單一工具
python3（標準庫）。本 skill 只此一個工具。
依賴
python>= 3.8
參數化（無硬編碼）
- 輸入：
--in <verdict artifact.json>；輸出：--out <accepted artifact.json>
介面
- 輸入 artifact：
verdict@1（讀data.verdict、data.round）。 - 輸出 artifact：
accepted@1—data{accepted, rounds, draft_status:"DRAFT"}，透傳trace。
fail-loud 邊界
data.verdict != "pass" → exit 3。產出一律 DRAFT，需人覆核。
執行
python3 skills/accept/tool.py --in <$RUN/verdict.json> --out <$RUN/accepted.json>
獨立重用
skills/accept/ + lib/kit.py 可單獨抽出；只要上游給 verdict@1 即可運作。
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.
- yesterday First seen · 33 lines · 43 tokens per session scan A 8b6f4296ebbc
accept is a skill published in the GitHub repository s0912758806p/agentic-sop-to-work (206 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 404 once invoked, about $0.0002 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 2026-09-08.
Other skills, from other repositories
x-bug2rag
A knowledge-capture tool that turns reusable bug explanations into a local RAG collection, meaning a searchable store of text that an agent can retrieve later. It records the trigger, incorrect implementation, correct implementation, and observable difference.
x-cr
A software-correctness investigation skill for finding why code behaves differently from what was expected. It uses evidence from code paths, specifications, tests, logs, and changes to assess possible causes.
x-spec2
A compact system-design guide for turning vague or cross-module requests into a small, structured specification package. It defines requirements, acceptance scenarios, module boundaries, and—when needed—data flow, state, timing, resources, or recovery design.
x-adversarial-risk
A focused adversarial review of a software specification. It tries to find small counterexamples that would expose incorrect implementations, such as invalid state changes, crashes, duplicate actions, permission mistakes, or concurrent events.
x-qdev
A compact development workflow for a small, clearly defined code change. It keeps the requirement, initially failing tests, implementation, and real verification results in one task document, following TDD, or test-driven development.
coordination-audit
Produce a structured organizational diagnostic that quantifies time spent on specification vs coordination vs execution, saved as a persistent audit artifact to $HOME/.ai-first-kit/. Conducts a guided 5-question interview, classifies every workflow structure by actual function, and identifies highest-ROI automation…