obsidian-wechat-converter: Skill for Claude Code

.claude/skills/openprd-discovery-loop/SKILL.md

openprd-discovery-loop is a skill for Claude Code from DavidLam-oss/obsidian-wechat-converter. It costs 26 tokens per session (878 once invoked), scanned A, original, MIT.

A discovery workflow for investigating an existing project, a reference project, or an unclear requirement over several passes. It gathers evidence before turning findings into requirements or plans.

In plain words
What is it for?
Use it to compare projects, inspect many files or histories, investigate complex problems, assess risks, and record supported requirements and open questions.
Why use it?
It prevents guesses from becoming project decisions and limits parallel investigation to cases where it can reduce repeated reading or improve verification.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions subagents.

This is DavidLam-oss/obsidian-wechat-converter's own configuration. It tells Claude Code how to work on obsidian-wechat-converter itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything obsidian-wechat-converter configures →

Reuse

Borrowing it

Nothing to install: this file belongs to DavidLam-oss/obsidian-wechat-converter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/DavidLam-oss/obsidian-wechat-converter/main/.claude/skills/openprd-discovery-loop/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/DavidLam-oss/obsidian-wechat-converter

Made for: Claude Code.

Wrote 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.

agentmods badge for openprd-discovery-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-discovery-loop/github.svg)](https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-discovery-loop)
Your own site
<a href="https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-discovery-loop"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-discovery-loop/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.

agentmods 80×15 button for openprd-discovery-loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-discovery-loop"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-discovery-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 878 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.00878
Opus 5 $0.00013 $0.00439
Sonnet 5 $0.00005 $0.00176
Haiku 4.5 $0.00003 $0.00088

Measured 5d ago against content hash f1955509c75c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

openprd-discovery-loop 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.

.claude/skills/openprd-discovery-loop/SKILL.md · 45 lines

What it actually says

OpenPrd Discovery Loop

当用户要求继续、深挖、补全、对比、复刻、全面梳理 requirements,或进行大量只读扫描时,使用这份 skill。

大量只读扫描调度

  • 日常任务仍由主 agent 先直接读取本地上下文;不要因为用户只说“看看、分析、梳理、定位、排查”就自动并行。
  • 用户明确要求深度分析、深入调研、全面梳理、多角度评估、交叉验证、并行排查、对标复刻或风险审查时,优先考虑只读 subagent。
  • 任务需要同时阅读多个目录、文档、模块、日志、历史实现或参考项目,且并行收集证据能明显减少主上下文污染或节省时间时,可以启动。
  • 任务涉及外部技术事实、公开仓库对标、复杂排障、发布风险或安全风险,且需要独立复核时,可以启动;仍必须遵守 Context7、DeepWiki、secrets-vault 和长文件门禁。
  • 用户明确说“不用 subagent / 直接做 / 先别并行 / 只回答”时,不启动。
  • 单文件小改、明确文案微调、简单命令、非常短的问题或清晰 bug 修复,默认不启动。
  • 一旦进入深度研究型 subagent 流程,默认使用 3 个只读 subagent:2 个独立调研执行者 + 1 个审查/交叉验证者。
  • 最多启动 5 个 subagent:最多 4 个调研执行者 + 1 个审查者。只有任务天然拆成 4 个互不冲突的研究分支时才扩到 5 个。
  • 代码与文档调研优先使用 spark-code-researcherspark-doc-readerdocumentation-explore;对标复刻用 electron-parity-mapper;安装发布或渠道排障用 release-diagnostics-researcherchannel-debug-researcher;审查与风险扫描用 skill-workflow-reviewersecurity-risk-researcher
  • 每个 subagent 只回答一个清晰问题,不再继续 spawn;主 agent 负责决策、整合和所有写入,subagent 只做只读调研、归纳和交叉验证。
  • subagent 输出必须回到主 agent 汇总;写入 discovery claim、requirements、specs 或 tasks 前,主 agent 必须把结论映射到证据路径、置信度和未解决问题。

循环

  • openprd discovery . --mode <brownfield|reference|requirement> 启动或恢复。
  • 每次只推进一个有证据支撑的覆盖项。
  • 报告运行健康前,用 openprd discovery . --verify 做校验。
  • 通过 openprd standards . --verify 保持基线文档标准同步。
  • 单个任务完成后只保留 task-scoped evidence;阶段收口或整体实现完成后,再用 openprd quality . --verify 审查 HTML 质量评估报告里的场景标签、必需 EVO 门禁、日志、业务护栏、冒烟覆盖、性能和知识缺口。

深度规则

  • 每个 claim 都要带来源、证据路径和置信度。
  • 推断出的行为不能直接变成 accepted requirement,必须保持可评审。
  • 大型任务文件必须分片并通过校验。
  • 只有在覆盖耗尽、被阻塞,或明确交接后才停止。
Changes

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.

  1. 5d ago First seen · 45 lines · 26 tokens per session scan A f1955509c75c

Subscribe to this mod's changes

openprd-discovery-loop is a skill published in the GitHub repository DavidLam-oss/obsidian-wechat-converter (307 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 878 once invoked, about $0.0001 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 2026-09-04.

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