Ejecuta cualquier Skill en Manus
con un clic

Ejecuta cualquier Skill en Manus con un clic

pmstack-eval-self

Estrellas8

Forks1

Actualizado25 de abril de 2026 a las 04:53

Run the pmstack self-eval suite to score every pmstack skill against canonical scenarios, with regression check vs the locked golden baseline. Use when the user wants to test pmstack itself, check skill quality after a model upgrade or prompt change, or verify pmstack still works before recommending it to others.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Ejecutar en Manus

Fuente

RyanAlberts

RyanAlberts/pmstack

Abrir repositorio de GitHub Ver repositorios del creador

Descarga

Ejecutar en Manus

Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Especialistas en gestión de proyectosOperaciones empresariales y financieras·SOC 13-1082

SKILL.md

readonly

name	pmstack-eval-self
description	Run the pmstack self-eval suite to score every pmstack skill against canonical scenarios, with regression check vs the locked golden baseline. Use when the user wants to test pmstack itself, check skill quality after a model upgrade or prompt change, or verify pmstack still works before recommending it to others.

pmstack self-eval

Score the pmstack skills against the canonical test set in evals/pmstack-self.yaml. Then compare to evals/golden/baseline.json and flag regressions.

What you must do

Run python3 evals/runner.py (with optional --skill <name> for a single skill, or --max-budget-usd N to cap cost). The runner installs pmstack into a temp dir, invokes each skill via claude -p, runs structural checks, asks a separate-family judge to score quality, writes JSON to evals/results/.
Find the just-written results file.
Run python3 evals/regression-check.py <new-result> to compare against the golden baseline. Exits non-zero if any skill's P0 pass rate dropped or mean quality dropped > 0.5.
Present the user:
- Headline numbers (P0 pass rate per skill)
- Any regression alerts, verbatim
- Top 3 failures with case IDs
- Whether to update the golden (only if user explicitly says scores improved)

Cost

Full suite is ~$5-10 of API tokens. Single skill is ~$1. Always warn before a full run.

Hard rules

Don't run individual cases by hand. Always go through the runner.
Don't cherry-pick — show the full table, not just wins.
If regression-check exits non-zero, surface that as a release blocker.

Runtime requirements

Python 3 with pyyaml, the claude CLI on PATH. Sandboxed code-exec on Claude.ai web is sufficient if available.

Más de este repositorio

mismo repositorio

pmstack-eval-report

RyanAlberts/pmstack

Turns a /run-eval result into a single self-contained, shareable HTML report — a verdict hero (pass/conditional/fail), the per-case table with PASS/FAIL color-coding, and the metrics table — that a PM can paste into Slack, a launch doc, LinkedIn, or a PR. The report carries a "run your own eval" backlink to pmstack. Use when the user says "make this eval shareable," "turn my eval run into a report," "export the eval results," "an HTML report of my eval," "share my eval results," or has a /run-eval output directory and wants a presentable artifact. Renders real run data only (from summary.md) — never invents numbers. Runs bin/eval-report.py on a run directory. Output: report.html next to the run's summary.md.

2026-06-248

pmstack-eval-grade

RyanAlberts/pmstack

Grades an eval the user already has against Anthropic's eval-design principles and returns an Eval Report Card — a letter grade A–F, the failures that earned it with the offending line, the single highest-leverage fix, and a shareable verdict card. It is the inverse of /eval (which writes a suite); this checks whether a suite is any good. Use when the user says "grade my eval," "is my eval any good," "review my eval suite," "roast my eval," "what's wrong with this eval," "did I write this eval right," pastes an eval YAML or rubric and asks for feedback, or wants to check an eval against the Anthropic framework before trusting it. Catches grader leakage, missing negative cases, no target, vibes-as-metric, and uncalibrated LLM-as-judge. Accepts a file path, a pasted/attached eval, or a plain-English success metric. Output: outputs/eval-grade-<feature>-<date>.md.

2026-06-208

pmstack-voc

RyanAlberts/pmstack

Synthesize many raw customer signals (support tickets, interview snippets, churn reasons, sales notes, NPS verbatims, app reviews) into a small set of ranked, PRD-ready problems. Use when a PM pastes or attaches a pile of feedback and asks "what's the real problem / what should we build first / what are the themes", when there are too many signals to read one by one, or when someone needs to turn voice-of-customer data into prioritized problem statements before writing a PRD. This is the front-of-funnel step that precedes pmstack-prd.

2026-05-298

pmstack-onboarding

RyanAlberts/pmstack

An interactive 9-step tutorial that walks a new user through every pmstack capability using a realistic AI-code-review scenario. Includes Anthropic's vibe-test (read raw transcripts before formalizing an eval) and transcript-review (diagnose failed trials as model / grader / task errors) rituals. Works in claude.ai web, desktop, and mobile (no terminal needed). Trigger when the user says "I just installed pmstack," "how do I use this," "tutorial," "walk me through pmstack," "getting started," or "show me what pmstack does."

2026-05-058

pmstack-transcript-review

RyanAlberts/pmstack

Walks a PM through Anthropic's Step 6 ritual — reading transcripts from many trials to diagnose every failed eval task as one of three things — model mistake, grader mistake, or task-spec error. Implements the practice Anthropic describes as "critical" — without it, badly-calibrated graders mask real model improvements. Use when the user has a /run-eval result with failures, asks "why did this fail?", says "let's read the transcripts," wants to debug an eval, suspects grader bugs, or mentions Anthropic's transcript-review practice. Accepts a run folder path, pasted summary + cases, or attached files. Output: a markdown memo at outputs/transcript-review-<feature>-<date>.md with verdict counts and per-trial diagnoses.

2026-05-058

pmstack-vibe-test

RyanAlberts/pmstack

Walks a PM through the layer-1 ritual of reading raw transcripts of an AI feature in action — what Anthropic calls "manual testing, dogfooding, and intuition" — before formalizing a structured eval. Surfaces failure patterns, drafts task candidates (including balanced negative cases per Anthropic Step 3), and produces a verdict on whether the feature is ready for /eval. Use when the user says "I want to vibe-test this," "I have transcripts of our AI feature," "before we write evals let's read the data," "we have support tickets / bug reports about this AI feature," or asks how to start an eval from scratch. Accepts pasted transcripts, attached files, or a folder path. Output: a markdown memo at outputs/vibe-test-<feature>-<date>.md.

2026-05-058

name	pmstack-eval-self
description	Run the pmstack self-eval suite to score every pmstack skill against canonical scenarios, with regression check vs the locked golden baseline. Use when the user wants to test pmstack itself, check skill quality after a model upgrade or prompt change, or verify pmstack still works before recommending it to others.

pmstack self-eval

Score the pmstack skills against the canonical test set in evals/pmstack-self.yaml. Then compare to evals/golden/baseline.json and flag regressions.

What you must do

Run python3 evals/runner.py (with optional --skill <name> for a single skill, or --max-budget-usd N to cap cost). The runner installs pmstack into a temp dir, invokes each skill via claude -p, runs structural checks, asks a separate-family judge to score quality, writes JSON to evals/results/.
Find the just-written results file.
Run python3 evals/regression-check.py <new-result> to compare against the golden baseline. Exits non-zero if any skill's P0 pass rate dropped or mean quality dropped > 0.5.
Present the user:
- Headline numbers (P0 pass rate per skill)
- Any regression alerts, verbatim
- Top 3 failures with case IDs
- Whether to update the golden (only if user explicitly says scores improved)

Cost

Full suite is ~$5-10 of API tokens. Single skill is ~$1. Always warn before a full run.

Hard rules

Don't run individual cases by hand. Always go through the runner.
Don't cherry-pick — show the full table, not just wins.
If regression-check exits non-zero, surface that as a release blocker.

Runtime requirements

Python 3 with pyyaml, the claude CLI on PATH. Sandboxed code-exec on Claude.ai web is sufficient if available.