PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill eval-rubric-designergit clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills/eval-rubric-designer)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/eval-rubric-designer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/eval-rubric-designer/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/mohitagw15856/pm-claude-skills/eval-rubric-designer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/eval-rubric-designer.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 | $0.00092 | $0.01024 |
| Opus 5.5 | $0.00037 | $0.00410 |
| Sonnet 5.5 | $0.00018 | $0.00205 |
| Haiku 4.5 | $0.00009 | $0.00102 |
Grade A, and why
eval-rubric-designer 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Rubric Designer Skill
You can't improve what you can't score. The hard part of evaluating AI output isn't running the judge — it's defining dimensions that are specific, observable, and independent, with anchors concrete enough that two people (or two judge runs) agree. This skill turns "is the output good?" into a rubric and a judge prompt you can run today.
Working from a brief
Given just "I need to eval my summariser", produce the full rubric anyway — infer the task, the output type, and the dimensions that matter for it, and label inferred choices. Never hand back a list of dimension names with no anchors; the anchors are where the rubric earns its keep.
Required Inputs
Ask for these only if they aren't already provided (else infer and label):
- The task — what the AI is supposed to produce, and for whom.
- A sample output (or two) — ideally one good and one weak, to calibrate anchors.
- What "good" means here — the quality bar and any non-negotiables (e.g. must be grounded, must follow format).
- How it'll be scored — human review, LLM-as-judge, or both; and whether you need a single score or per-dimension.
Output Format
Eval Rubric: [task]
1. Dimensions — 3–6 independent dimensions, each with a one-line definition and a weight. Default set, tailored to the task: structure, completeness, correctness/grounding, usefulness, safety/tone.
2. Anchors — for each dimension, concrete descriptions at 1, 3, and 5 (what a poor / acceptable / excellent answer looks like for this task). Anchors must be observable, not "feels good".
| Dimension (weight) | 1 — poor | 3 — acceptable | 5 — excellent |
|---|---|---|---|
| Grounding (×2) | invents facts not in the source | mostly grounded, minor drift | every claim traceable to the source |
3. Judge prompt — a ready-to-run LLM-as-judge prompt in a fenced block: the task description, the rubric,
an instruction to score each dimension 1–5, and a strict JSON output contract ({"dimension":N,...}) so
scores parse reliably. Include a one-line "return only JSON" reinforcement.
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.
- 2d ago First seen · 79 lines · 92 tokens per session scan A ae8c781e36d7
eval-rubric-designer is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 1,024 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-12.
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