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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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Prix non confirmé★ 696 Stars GitHubRegistre mis à jour · 2 sept. 2026agent-skill

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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.

Voir la source ↗

Métadonnées du fichier

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.

Voir le texte 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.

Examiner la source

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Licence: MIT
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Source du skill enregistrée

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Réviser avant installation: Éviter l’installation automatique

Licence: 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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Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: mgechev/skillgrade
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 26 août 2026
Registre mis à jour: 2 sept. 2026
Chemin des instructions: skills/skillgrade-graders/SKILL.md @ 8d9e5b8a275f

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

72/100

Solide

Confiance

65/100

Sandbox uniquement

Audit

77/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

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: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Plus de détails

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}

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Revendication du propriétaire

Revendiquer cette fiche de skill

Cette fiche Indexé par Registry est attribuée à mgechev, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.

Kit de partage

Kit de backlinks créateur

Ajoutez les badges de preuve à votre README

Affichez la fiche canonique, les signaux actuels de confiance et d’audit, ainsi que de vraies preuves Agent-Proven là où les développeurs évaluent le dépôt.

[![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)

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Signal de communauté

Indiquez si ce skill semble utile à votre workflow Agent. Les retours agrégés améliorent le classement au fil du temps.