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 agentmods add skills/asherzj/ashers-agent-skills/from-transcriptnpx skills add asherzj/ashers-agent-skills --skill from-transcriptgit clone --depth 1 https://github.com/asherzj/ashers-agent-skillsWrote 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/asherzj/ashers-agent-skills/from-transcript)<a href="https://agentmods.dev/skills/asherzj/ashers-agent-skills/from-transcript"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/from-transcript.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 | $0.00050 | $0.00945 |
| Opus 5 | $0.00025 | $0.00473 |
| Sonnet 5 | $0.00010 | $0.00189 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
from-transcript 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 5d 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.
What it actually says
从逐字稿建立研发输入
逐字稿是高带宽的一手来源，不是可直接执行的规格。本 Skill 保留原始来源的可追溯性，把其中的事实、决策、例子、矛盾和未知项整理成一份经人类确认的输入简报，再交给后续澄清或交付流程。
边界
- 不把未经确认的口述写进
CONTEXT.md、ADR、规格或代码。 - 不因为说话人语气确定，就把猜想当作决策。
- 不要求用户重新填写一份完整表格；先从已有口述中提取，只有真正会改变路线的缺口才追问。
- 原始逐字稿只作为证据保存或引用。除非用户或仓库规则要求，不复制到仓库。
- 逐字稿可能包含敏感信息；沿用当前环境的访问与传输授权，不主动上传、转发或扩大可见范围。
1. 建立来源指针
记录已知的来源、日期、参与者和讨论主题。信息未知就标为未知，不猜测。来源可以是文件、会议链接、消息线程或当前对话。
不要改写原始来源。后续产物引用它，而不是替代它。
2. 编译输入
把内容整理为以下类别，并为每条标记认识状态：
- 已陈述：逐字稿中明确说过，但尚未经过本次确认；
- 已确认：用户明确认可，可以进入后续规格；
- 推断：根据上下文得出的解释，必须单独标出。
提取：
- 问题、目标和受影响的人；
- 具体使用场景与现有替代方式；
- 范围和非目标；
- 领域术语及可能冲突的同义词；
- 业务规则、技术约束和不可突破的架构范围；
- 已做决策、理由、被否决方案和决策者；
- 正常例子、业务异常、外部依赖故障、并发或重试场景；
- 相互矛盾的说法、含糊指代和仍然未知的事项。
查代码、仓库文档或工具就能确定的事实由 Agent 自己调查。只有需要价值判断、业务语义或范围选择的问题才交给人类。
3. 形成待确认输入简报
使用紧凑结构呈现：
## 来源
## 目标与用户
## 场景与例子
## 范围与非目标
## 术语与约束
## 已陈述的决策
## 冲突与未知项
## 建议进入后续流程的内容
每个冲突都要并列展示相互冲突的说法和它们的来源；不要静默选择一个。每个推断都要说明依据。
4. 人类确认门禁
把输入简报交给用户确认。未经确认，不发布规格、不拆票、不实现，也不把术语和决策升级为长期项目上下文。
如果仍有会改变路线的开放决策，调用 grilling；如果术语或难以回退的决策被确认，调用 domain-modeling 就地维护 CONTEXT.md 或 ADR。
5. 路由
确认后的输入简报是后续流程的一手来源：
- 一个局部、单上下文行为 →
flow-small-change； - 路线可澄清、需要规格和多张工单 →
flow-feature； - 路线仍不可见、需要跨会话决策地图 →
flow-large-effort； - 只需要发布规格 →
to-spec。
后续流程只针对尚未解决的缺口继续澄清，不要让用户重述逐字稿已经提供并确认的事实。
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.
- 5d ago First seen · 86 lines · 50 tokens per session scan A f3f385648eb9
from-transcript is a skill published in the GitHub repository asherzj/ashers-agent-skills (2 stars, last pushed 8d ago), licensed MIT. It adds 50 tokens to every session and 945 once invoked, about $0.0003 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.
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