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.
git clone --depth 1 https://github.com/gonzalezpazmonica/pm-workspaceWrote 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/commands/gonzalezpazmonica/pm-workspace/backlog-prioritize)<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/backlog-prioritize"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/backlog-prioritize/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/commands/gonzalezpazmonica/pm-workspace/backlog-prioritize"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/backlog-prioritize.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 | $0.00022 | $0.01193 |
| Opus 5 | $0.00011 | $0.00596 |
| Sonnet 5 | $0.00004 | $0.00239 |
| Haiku 4.5 | $0.00002 | $0.00119 |
Grade A, and why
backlog-prioritize 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 7d 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.
This is a copy
100% identical to backlog-prioritize — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/backlog-prioritize
🦉 Savia calcula prioridad objetiva: Reach, Impact, Confidence, Effort (RICE) o Weighted Shortest Job First (WSJF).
Cargar perfil
Grupo: Backlog Intelligence — cargar:
CLAUDE.md— proyecto activoprojects/{proyecto}/CLAUDE.md— configcompany/strategy.md— OKRs y alineación estratégica- Backlog items desde Azure DevOps
- Histórico de Story Points (velocity)
Subcomandos
/backlog-prioritize— RICE score por defecto/backlog-prioritize --method wsjf— usar WSJF en lugar de RICE/backlog-prioritize --strategy-aligned— ponderar por alineación con OKRs/backlog-prioritize --effort-weighted— ponderar por esfuerzo estimado
Flujo
Paso 1 — Cargar items y datos
Cargar backlog (sin Done) + Story Points + histórico 3 sprints para velocidad media.
Paso 2 — Calcular RICE
RICE = (Reach × Impact × Confidence) / Effort
- Reach (1-100): usuarios afectados, escala relativa TAM
- Impact (1-3): 1=minor, 2=medium, 3=major
- Confidence (0-100%): madurez del spec (Poor=50%, High=100%)
- Effort (1-20): Story Points históricos
Si PBI sin SP → pedir estimation antes de calcular.
Paso 3 — Calcular WSJF (alternativa)
WSJF = (Business Value + Time Criticality + Risk) / Job Size
- Todos 1-5 (Business Value, Criticality, Risk Reduction, Job Size)
- Usar si equipo es SAFe o prefiere scoring cualitativo
Paso 4 — Ponderar por estrategia (opcional)
Si se usa --strategy-aligned:
Score_final = Score_base × (1 + Strategy_weight)
Donde Strategy_weight:
- Item NO contribuye a OKR → 0.8 (penalizar)
- Item contribuye a 1 KR → 1.0 (neutro)
- Item contribuye a 2+ KR → 1.3 (priorizar)
Leer company/strategy.md para vincular items a KRs.
Paso 5 — Presentar ranking ordenado
# Backlog Prioritization — {proyecto}
Método: RICE | Generado: {fecha}
Velocidad media (3 sprints): {SP/sprint}
## Top 20 Items Priorizados
| Rango | PBI ID | Título | Reach | Impact | Conf. | Effort | RICE Score | Estrategia |
|-------|--------|--------|-------|--------|-------|--------|------------|-----------|
| 1 | #2341 | Feature X | 80 | 3 | 90% | 5 SP | 432 | OKR 1.2 + 2.1 |
| 2 | #2338 | Bug critical | 50 | 3 | 100% | 2 SP | 225 | Tech debt |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
## Items en cola (21-50) — mostrar resumen solo
[Tabla comprimida con rango 21-50]
## Recomendación de capacidad actual
Capacidad sprint: NNN SP
Items para este sprint (score ≥ X): NNN SP
Items para 2-3 sprints próximos: NNN SP
## Insights
- Top 3 items cubren X% del OKR principal
- Y items sin alineación estratégica (considerar deprioritizar)
- Esfuerzo promedio top 10: Z SP/item
---
## Vincular a company/strategy.md
Si existe strategy.md:
```markdown
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.
- 7d ago First seen · 151 lines · 22 tokens per session scan A 32db9fe3cdd1
backlog-prioritize is a command published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 4d ago), licensed MIT. It adds 22 tokens to every session and 1,193 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to backlog-prioritize, differing in 0 lines, and is treated as a copy.
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