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skillgrade-graders

Authors deterministic and LLM rubric graders for skillgrade evaluations. Use when creating scoring scripts, writing evaluation rubrics, or combining multiple graders with weighted scoring. Don't use for setting up eval pipelines, configuring eval.yaml defaults, or general test wr

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Precio sin confirmar★ 696 Estrellas de GitHubRegistro actualizado · 2 sept 2026agent-skill

Resumen

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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

Skillgrade Grader Authoring

Procedures

Step 1: Identify the Grading Strategy

Determine whether the task requires objective verification (deterministic) or qualitative assessment (LLM rubric).
For most tasks, combine both: deterministic graders verify outcomes (weight 0.7), LLM rubrics assess approach quality (weight 0.3).

Step 2: Write a Deterministic Grader

Create a script in the skill's graders/ directory (bash or TypeScript).

The script must output a JSON object to stdout with the following structure:

{"score": 0.67, "details": "2/3 checks passed", "checks": [{"name": "check-name", "passed": true, "message": "Description"}]}

score (0.0–1.0) and details are required. checks is optional but recommended.
Read references/grader-output-schema.md for the full output specification.
Use awk for arithmetic in bash scripts — bc is not available in node:20-slim.

Reference the grader in eval.yaml:

- type: deterministic
  run: bash graders/check.sh
  weight: 0.7

Step 3: Write an LLM Rubric Grader

Draft a rubric with explicit scoring criteria and point allocations.

Structure the rubric into weighted sections that sum to 1.0:

Workflow Compliance (0-0.5):
- Did the agent follow the mandatory workflow steps?
Efficiency (0-0.5):
- Completed in ≤5 commands without trial-and-error?

Reference the rubric in eval.yaml:

- type: llm_rubric
  rubric: |
    [rubric text or file path]
  weight: 0.3
  provider: gemini               # optional: gemini (default) | anthropic | openai
  model: gemini-3.5-flash        # optional model override (defaults to the latest dynamically resolved flash model)

For long rubrics, store in a separate file and reference by path: rubric: rubrics/quality.md.

Step 4: Combine Multiple Graders

Assign weights to each grader based on importance. Weights are normalized automatically.
Final reward is calculated as: Σ (grader_score × weight) / Σ weight.

Example configuration:

graders:
  - type: deterministic
    run: bash graders/check.sh
    weight: 0.7
  - type: llm_rubric
    rubric: rubrics/quality.md
    weight: 0.3

Step 5: Validate Graders

Create a reference solution script that produces the expected output.
Run skillgrade --validate to verify graders score the reference solution correctly.
Test only deterministic graders: skillgrade --grader=deterministic (skips LLM calls, faster iteration).
Test only LLM rubric graders: skillgrade --grader=llm_rubric.
Run a specific eval with a specific grader type: skillgrade --eval=my-eval --grader=deterministic.
If a grader returns unexpected scores, inspect the script output and adjust scoring logic.

Error Handling

If a deterministic grader outputs non-JSON, ensure all echo/console.log statements except the final JSON result are redirected to stderr.
If an LLM rubric grader returns 0.00 with a missing API key message, set the appropriate key for your provider: GEMINI_API_KEY (provider: gemini), ANTHROPIC_API_KEY (provider: anthropic), or OPENAI_API_KEY (provider: openai).
To use a custom/self-hosted LLM endpoint, set ANTHROPIC_BASE_URL (for provider: anthropic) or OPENAI_BASE_URL (for provider: openai) — e.g. for Ollama or vLLM.
If scores are inconsistent across trials, reduce rubric ambiguity by adding concrete examples of passing and failing behavior.

Ver fuente ↗

Metadatos del archivo

name: skillgrade-graders
description: Authors deterministic and LLM rubric graders for skillgrade evaluations. Use when creating scoring scripts, writing evaluation rubrics, or combining multiple graders with weighted scoring. Don't use for setting up eval pipelines, configuring eval.yaml defaults, or general test writing.

Ver texto original

---
name: skillgrade-graders
description: Authors deterministic and LLM rubric graders for skillgrade evaluations. Use when creating scoring scripts, writing evaluation rubrics, or combining multiple graders with weighted scoring. Don't use for setting up eval pipelines, configuring eval.yaml defaults, or general test writing.
---

# Skillgrade Grader Authoring

## Procedures

**Step 1: Identify the Grading Strategy**
1. Determine whether the task requires objective verification (deterministic) or qualitative assessment (LLM rubric).
2. For most tasks, combine both: deterministic graders verify outcomes (weight 0.7), LLM rubrics assess approach quality (weight 0.3).

**Step 2: Write a Deterministic Grader**
1. Create a script in the skill's `graders/` directory (bash or TypeScript).
2. The script must output a JSON object to stdout with the following structure:
   ```json
   {"score": 0.67, "details": "2/3 checks passed", "checks": [{"name": "check-name", "passed": true, "message": "Description"}]}
   ```
3. `score` (0.0–1.0) and `details` are required. `checks` is optional but recommended.
4. Read `references/grader-output-schema.md` for the full output specification.
5. Use `awk` for arithmetic in bash scripts — `bc` is not available in `node:20-slim`.
6. Reference the grader in eval.yaml:
   ```yaml
   - type: deterministic
     run: bash graders/check.sh
     weight: 0.7
   ```

**Step 3: Write an LLM Rubric Grader**
1. Draft a rubric with explicit scoring criteria and point allocations.
2. Structure the rubric into weighted sections that sum to 1.0:
   ```
   Workflow Compliance (0-0.5):
   - Did the agent follow the mandatory workflow steps?
   Efficiency (0-0.5):
   - Completed in ≤5 commands without trial-and-error?
   ```
3. Reference the rubric in eval.yaml:
   ```yaml
   - type: llm_rubric
     rubric: |
       [rubric text or file path]
     weight: 0.3
     provider: gemini               # optional: gemini (default) | anthropic | openai
     model: gemini-3.5-flash        # optional model override (defaults to the latest dynamically resolved flash model)
   ```
4. For long rubrics, store in a separate file and reference by path: `rubric: rubrics/quality.md`.

**Step 4: Combine Multiple Graders**
1. Assign weights to each grader based on importance. Weights are normalized automatically.
2. Final reward is calculated as: `Σ (grader_score × weight) / Σ weight`.
3. Example configuration:
   ```yaml
   graders:
     - type: deterministic
       run: bash graders/check.sh
       weight: 0.7
     - type: llm_rubric
       rubric: rubrics/quality.md
       weight: 0.3
   ```

**Step 5: Validate Graders**
1. Create a reference solution script that produces the expected output.
2. Run `skillgrade --validate` to verify graders score the reference solution correctly.
3. Test only deterministic graders: `skillgrade --grader=deterministic` (skips LLM calls, faster iteration).
4. Test only LLM rubric graders: `skillgrade --grader=llm_rubric`.
5. Run a specific eval with a specific grader type: `skillgrade --eval=my-eval --grader=deterministic`.
6. If a grader returns unexpected scores, inspect the script output and adjust scoring logic.

## Error Handling
* If a deterministic grader outputs non-JSON, ensure all `echo`/`console.log` statements except the final JSON result are redirected to stderr.
* If an LLM rubric grader returns 0.00 with a missing API key message, set the appropriate key for your provider: `GEMINI_API_KEY` (provider: gemini), `ANTHROPIC_API_KEY` (provider: anthropic), or `OPENAI_API_KEY` (provider: openai).
* To use a custom/self-hosted LLM endpoint, set `ANTHROPIC_BASE_URL` (for provider: anthropic) or `OPENAI_BASE_URL` (for provider: openai) — e.g. for Ollama or vLLM.
* If scores are inconsistent across trials, reduce rubric ambiguity by adding concrete examples of passing and failing behavior.

Revisar el código fuente

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Licencia: MIT
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Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: mgechev/skillgrade
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 26 ago 2026
Registro actualizado: 2 sept 2026
Ruta de instrucciones: skills/skillgrade-graders/SKILL.md @ 8d9e5b8a275f

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

72/100

Sólido

Confianza

65/100

Solo sandbox

Auditoría

77/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Resultados: —

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Más detalles

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}

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Esta ficha Indexado por Registry se atribuye a mgechev, pero aún no está marcada como oficial. Reclámala para añadir una señal de propietario verificado y hacer más fiables futuras actualizaciones de lanzamiento, instalación y auditoría.

Kit para compartir

Kit de enlaces para creadores

Añade las insignias de evidencia a tu README

Muestra la ficha canónica, las señales actuales de confianza y auditoría, y evidencia real de Agent-Proven donde los desarrolladores evalúan el repositorio.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/mgechev-skillgrade-graders?metric=listed&label=Listed)](https://www.openagentskill.com/skills/mgechev-skillgrade-graders?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/mgechev-skillgrade-graders?metric=trust&label=Trust)](https://www.openagentskill.com/skills/mgechev-skillgrade-graders?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/mgechev-skillgrade-graders?metric=audit&label=Audit)](https://www.openagentskill.com/skills/mgechev-skillgrade-graders/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/mgechev-skillgrade-graders?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/mgechev-skillgrade-graders?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Vista previa de insignia Abrir auditoría Kit para creadores

Señal de comunidad

Comparte si este skill resulta útil para tu flujo de Agent. Los comentarios agregados mejoran la clasificación con el tiempo.