evidence-rubric

evidence-rubric is a skill for Claude Code from kimsanguine/hplan. It costs 112 tokens per session (1,217 once invoked), scanned A, original, MIT.

A 100-point checklist for judging a product idea before writing a PRD, which is a detailed product requirements document. It scores evidence such as the target user, painful event, workaround, repetition, economic impact, and path to the first five users.

In plain words
What is it for?
Scoring an idea from its target user, hypothesis, alternatives, and proposed features; finding weak evidence areas; and deciding what to investigate next. A high score does not by itself approve PRD writing or product development.
Why use it?
It replaces a decision based on enthusiasm with a repeatable review of what is actually known. The result shows which evidence is missing and whether to build, interview more people, change direction, or pause.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; built for gstack.

Part of the hplan plugin — 8 skills, 11 commands, 4 agents shipped together

Good fit Scoring an idea from its target user, hypothesis, alternatives, and proposed features; finding weak evidence areas; and deciding what to investigate next. A high score does not by itself approve PRD writing or product development.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kimsanguine/hplan/evidence-rubric
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 kimsanguine/hplan --skill evidence-rubric
Clone the repo
git clone --depth 1 https://github.com/kimsanguine/hplan

Made for: Claude Code.

Or install hplan, the plugin that ships this one along with the rest of its 8 skills, 11 commands, 4 agents.

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 evidence-rubric

README.md
[![agentmods](https://agentmods.dev/badge/skills/kimsanguine/hplan/evidence-rubric/github.svg)](https://agentmods.dev/skills/kimsanguine/hplan/evidence-rubric)
Your own site
<a href="https://agentmods.dev/skills/kimsanguine/hplan/evidence-rubric"><img src="https://agentmods.dev/badge/skills/kimsanguine/hplan/evidence-rubric/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 evidence-rubric

Your own site · 80×15
<a href="https://agentmods.dev/skills/kimsanguine/hplan/evidence-rubric"><img src="https://agentmods.dev/badge/skills/kimsanguine/hplan/evidence-rubric.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,217 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.
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 $0.00112 $0.01217
Opus 5 $0.00056 $0.00609
Sonnet 5 $0.00022 $0.00243
Haiku 4.5 $0.00011 $0.00122

Measured 12d ago against content hash 9c6249f6c799, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

evidence-rubric 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 12d 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.

hplan/skills/evidence-rubric/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Evidence Rubric — 100-Point Idea Scoring

Running for: $ARGUMENTS

Core Goal

  • 아이디어를 100점 루브릭으로 측정하여 "PRD 쓰기 전에 인터뷰가 더 필요한지" 명확한 신호를 만든다.
  • 8개 축 각각의 점수 + 부족한 axis 목록을 반환해서, 다음 인터뷰에서 어떤 신호를 들어야 하는지 좁힌다.
  • LLM의 직감 평가가 아닌 결정론적 스크립트(generate_report.py)로 점수화해 hand-wave를 차단한다.

Trigger Gate

Use This Skill When

  • 사용자가 아이디어 한 문장 + 타깃 + 가설 + 대체재 + 기능 후보를 제시했을 때
  • Spec-Kit / Kiro / GStack / Superpowers 워크플로우 진입
  • 이미 인터뷰 노트가 있어 evidence strength를 객관적으로 측정하고 싶을 때
  • "build로 가야 할까 interview를 더 해야 할까" 고민이 등장했을 때

Route to Other Skills When

  • 점수가 낮고 인터뷰 자체가 부족할 때 → interview-synthesis (hplan plugin)
  • 이미 점유된 영역으로 보일 때 → exclusions (hplan plugin) check
  • 점수는 충분한데 비용 구조가 불확실할 때 → cogs-sentinel (hplan plugin)
  • 아이디어 발굴 단계로 돌아가야 할 때 → opp-tree (discover plugin)

Boundary Checks

  • ❌ 이 skill은 아이디어 발굴이 아니다 (그건 discover/opp-tree). 이미 아이디어가 있을 때만 호출.
  • ❌ 이 skill은 PRD 작성을 허락하지 않는다. 점수가 충분해도 Product Gate + Build Gate를 거쳐야 한다.
  • ❌ 점수만 높고 인터뷰가 0건이면 interview 결정이 강제된다.

Inputs

JSON 파일 또는 인라인 입력:

{
  "idea": "한 문장 가설",
  "target": "ICP 행동 기술 (인구통계 금지)",
  "hypothesis": "현재 상황",
  "alternatives": "대체재 콤마 구분",
  "features": "MVP 기능 후보 콤마 구분",
  "interview_notes": "인터뷰 발화 한 줄당 하나"
}

Steps

  1. Read examples/good-01.md to internalize the rubric.
  2. If user input is freeform, structure it into the 6 fields above.
  3. Save to harness/evidence/last_input.json.
  4. Run python3 hplan/scripts/generate_report.py <path> --json.
  5. Report score + decision + breakdown + missing axes.
  6. If decision == "interview", immediately route to interview-synthesis skill.
  7. If decision == "build", write the report to harness/evidence/report.md and route to cogs-sentinel.

Outputs

  • harness/evidence/report.md — markdown diagnosis
  • harness/evidence/last_input.json — preserved input
  • Decision: build (≥75 + interview_lines ≥ 2 + economic pain) / interview (≥55, or ≥75 without required conditions) / pivot (35–54) / hold (<35) — build requires mandatory economic_pain + 2+ interview lines; score alone is not sufficient

Read the full file on GitHub · 94 lines

Files

What ships with it

4 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.

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. 12d ago First seen · 94 lines · 112 tokens per session scan A 9c6249f6c799

Subscribe to this mod's changes

evidence-rubric is a skill published in the GitHub repository kimsanguine/hplan (2 stars, last pushed 26d ago), licensed MIT. It adds 112 tokens to every session and 1,217 once invoked, about $0.0006 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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