openprd-experience-diagnostic-candidate-eval-20260617151556

openprd-experience-diagnostic-candidate-eval-20260617151556 is a skill for Claude Code, Codex from mileson/openprd. It costs 37 tokens per session (791 once invoked), scanned A, original, MIT.

A draft project-memory guide for checking whether past diagnostic experience fits a new task. It covers visual comparisons, quality reviews, testing strategy, agent integration, and hook behavior.

In plain words
What is it for?
Use it when a task involves visual review, comparison results, tests, agent integration, or hooks. It helps inspect the recorded evidence and decide whether the earlier experience applies.
Why use it?
It helps keep lessons from visual or workflow fixes from being lost after the current task. It also supports evidence-based reuse rather than copying a conclusion from a superficially similar change.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when a task involves visual review, comparison results, tests, agent integration, or hooks. It helps inspect the recorded evidence and decide whether the earlier experience applies.

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Install with agentmods
npx agentmods add skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556
Install

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.

Any agent
npx skills add mileson/openprd --skill openprd-experience-diagnostic-candidate-eval-20260617151556
Clone the repo
git clone --depth 1 https://github.com/mileson/openprd

Made for: Claude Code, Codex.

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-experience-diagnostic-candidate-eval-20260617151556

README.md
[![agentmods](https://agentmods.dev/badge/skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556/github.svg)](https://agentmods.dev/skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556)
Your own site
<a href="https://agentmods.dev/skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556"><img src="https://agentmods.dev/badge/skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556/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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556"><img src="https://agentmods.dev/badge/skills/mileson/openprd/openprd-experience-diagnostic-candidate-eval-20260617151556.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 791 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 $0.00037 $0.00791
Opus 5 $0.00018 $0.00396
Sonnet 5 $0.00007 $0.00158
Haiku 4.5 $0.00004 $0.00079

Measured 11d ago against content hash 556d8c17e36e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

openprd-experience-diagnostic-candidate-eval-20260617151556 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 11d 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.

.openprd/knowledge/drafts/openprd-experience-diagnostic-candidate-eval-20260617151556/SKILL.md · 68 lines

What it actually says

openprd-experience-diagnostic-candidate-eval-20260617151556

状态:draft 候选目录:.openprd/knowledge/candidates/candidate-eval-20260617151556 Promote:openprd quality . --learn --from .openprd/knowledge/candidates/candidate-eval-20260617151556

触发条件

  • 本轮结果里已经出现可复用的症状、排查线索或根因模式,不应该只留在当前对话里。
  • dev-check
  • run-verify
  • quality-verify
  • doctor-green
  • 只回复 ok
  • 症状: 只回复 ok
  • dev-check: dev-check attention=0, warning=1 | touched: src/canvas-app.html.js, src/canvas-i18n.js, test/openprd-canvas.test.js

适用范围

  • 抽象模式: 同类故障通常会先在 runtime-events、timeline、root-cause-candidates 和 diagnostic-report 中留下证据。只要实现阶段就把这些结构化诊断面铺好,后续多数问题都能先靠现有证据定位,而不是临时补日志。
  • 适用于项目源码或核心流程已经落地、需要把实现经验固化为项目知识的任务。
  • 适用于本轮补过验证或测试夹具,后续同类需求需要同步复用验证方式的任务。

典型输入

  • 任务场景: dev-check
  • 相关文件: src/canvas-app.html.js、src/canvas-i18n.js、test/openprd-canvas.test.js
  • 已有证据类型: diagnostic-report
  • 验证信号: dev-check

典型输出

  • 项目经验候选与诊断包
  • 待确认的项目经验草案
  • 验证结论: dev-check attention=0, warning=1
  • 可复用的验证链路与收尾动作

下次触发时先看什么

  • src/canvas-app.html.js
  • src/canvas-i18n.js
  • test/openprd-canvas.test.js
  • .openprd/harness/turn-state.json

不要直接套用

  • 如果只是文件名、路径或个别词相似,但当前目标和验证方式不同,不要直接套用。
  • 如果当前问题没有出现相似症状、事件或证据入口,不要因为改到相似模块就直接照搬旧结论。
  • 如果本轮已经有更新的现场证据,先核对新证据,再决定是否复用旧经验。

可复用模式

  • 先按本轮诊断线索复走一次,再补最小必要证据。

验证方式

  • dev-check attention=0, warning=1
  • 复现一次同类路径,确认新的诊断包仍能导出 runtime-events、timeline、root-cause-candidates 和 diagnostic-report。
  • 重点核对 dev-check -> run-verify -> quality-verify 的顺序是否符合预期。
  • 修复后再次执行同一路径,确认时间线不再在历史失败断点中断。
  • 把最终诊断包与质量报告一起归档,确保后续 Agent 能直接复用已有排查路径。
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. 11d ago First seen · 68 lines · 37 tokens per session scan A 556d8c17e36e

Subscribe to this mod's changes

openprd-experience-diagnostic-candidate-eval-20260617151556 is a skill published in the GitHub repository mileson/openprd (50 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 791 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 2026-08-30.