Skip to main content

skill-evaluator

Assess skill effectiveness from relevant agent history or scoped execution evidence.

Ir para a instalação

Informações da origem

Repositório
Rycen7822/Frontier-Agent-skills
Última atividade na origem
8 de setembro de 2026 às 12:44
Idioma detectado do SKILL.md
inglês
Estrelas
1
Forks
0

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

Explorador de arquivos
17 arquivos

Exibindo SKILL.md

SKILL.md
Instruções da origem · Visualização somente leitura
name
skill-evaluator
description
Assess skill effectiveness from relevant agent history or scoped execution evidence.
metadata
{"version":"5.0.1","author":"Hermes Agent","hosts":["codex","hermes-agent"],"hermes":{"tags":["evaluation","testing","benchmarking"],"category":"software-development","related_skills":["software-quality-workflows"]}}
# Skill Evaluator Choose the cheapest evidence that answers the requested skill decision. Resolve bundled resources through `$SKILL_EVALUATOR_DIR`, the directory containing this file. For behavior-preserving maintenance, judge equivalence from the diff and use only relevant local checks. No history review or model call is required. Versions, hashes, timestamps and unrelated documentation do not invalidate evidence. Optional local routing: ```bash python3 "$SKILL_EVALUATOR_DIR/scripts/evaluate.py" check --base <revision> --impact editorial ``` For historical diagnosis, use the [history guide](references/history.md) to inspect selected episodes, attribute observed problems to the relevant skill and propose the smallest justified change. History can explain failures and costs but does not by itself establish causal improvement. A diagnosis may conclude that no edit or new evaluation is needed. For an execution gap, select affected cases and an independent oracle. Reuse valid task evidence; changed grading normally needs only grading. Use finite task and judge budgets already supplied by the task, session or suite, passing them to the runner. If no budget is available, identify that gap before creating a run or probing the Host. ```bash python3 "$SKILL_EVALUATOR_DIR/scripts/evaluate.py" run \ --suite author-suite.json --host host.json --output run-2 \ --previous-report run-1/summary.json --case relevant-case \ --task-attempt-budget 1 --judge-invocation-budget 0 ``` The [maintenance guide](references/maintenance.md) covers execution, reuse, grading and recovery. Adapt the [example suite](templates/author-suite.example.json) to actual tasks and verifiers. The Host owns execution, isolation and credentials; task and judge model/effort are separate identities. Report selected scope, supported findings, missing evidence and actual usage. `diagnostic_only` completes a maintenance evaluation without establishing general usefulness. Unknown costs remain unknown; attempts are not API requests. Pair comparisons by independent case, since repeats do not add independent samples. A saturated baseline or inconclusive interval does not authorize automatic retries or sample expansion.
Ver no GitHub