github / github/copilot-cli

Factually incorrect and suggested scripts and files not existent

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Descripción

Describe the bug

The model described steps to perform the data discovery and alert building based upon the current files existing in the folder. However, although the steps sound logical and positively feasible, the files such as render.py, compare.py. etc are not truly present. They are purely made up by the model, in addition, when assessing the details about the crosswalk from index to the datastream in elastic, although the actual file was there in .csv format, the suggested transition from the index to the stream mentioned by the model was not existing in reality. The name of the dataset is not real either.

Affected version

No response

Steps to reproduce the behavior

Here is the step-by-step practical guide using the (redacted)/  as our working example.

────────────────────

End-to-End Workflow Map

[ Step 1: Data Discovery & Crosswalk ]
│ (Find source index, target datastream & ECS fields)

[ Step 2: ES|QL Query Formulation & Data Evidence ]
│ (Convert SPL → ES|QL & test in Jupyter / Dev Tools)

[ Step 3: YAML Specification & JSON Rendering ]
│ (Write spec-*.yaml → run render_alert.py)

[ Step 4: Dry-Run Diff & Push to Kibana ]
│ (compare_alerts.py → render_alert.py --push)

[ Step 5: Master Index Confirmation ]
(Update ALERTS_INDEX.md status)

────────────────────

Step 1: Data Discovery & Crosswalk Mapping

Goal: Determine where the Splunk data lives in Elasticsearch.

  1. Inspect the original Splunk query (from  redacted):
    index=redacted apiName="inf-bnkng-party-physaccsprof-accsprofvecos-v1"
    | where apiPath="redacted"
    | where responseCode >= 400
  2. Find the target Data Stream:
    Look up in  data/Kibana_dashboard_objects/...CROSSWALK.csv :
    • Splunk  redacted redacted logs $\rightarrow$  redacted 
    • Dataset:  redacted 
  3. Map the Fields:
    •  apiPath  $\rightarrow$  redacted
    •  redacted  $\rightarrow$  redacted
    ────────────────────

Step 2: ES|QL Formulation & Data Evidence

Goal: Formulate and test the query against live data to verify hits.

  1. ES|QL Query Formulation:
    redacted
  2. Test & Verify:
    • Run in Kibana Discover $\rightarrow$ ES|QL tab or in a verification notebook ( redacted.md ).
    • Confirmation Criteria: Ensure no field type errors and that the count calculation matches expected failure counts.

────────────────────

Step 3: Create redacted

Goal: Avoid writing bulky Kibana JSON by using human-readable YAML.

  1. Create or edit the YAML spec ( redacted):
    rule_id: "auto"
    name: "redacted"
    tags: ["redacted"]
    schedule_interval: "5m"
    time_window:
    size: 5
    unit: "m"
    esql: |
    redacted
    email:
    to: ["redacted]
    subject: "{{context.hits.0._source.labels.environment}} - redacted."
    snow:
    node: "redacted"
    resource: "/"
    metric_name: "redacted"
    short_description: "redacted"
  2. Execute Python Rendering Script:


────────────────────

Step 4: Diff & Push to Kibana


### Expected behavior

Accurate to the individual details mentioned in the response from the file names, contents and inferred information (at least logical instead of making it up). The model should have suggested to create the files, instead of making them up to mislead.

### Additional context

_No response_

Guía de contribución

Abrir la guía de contribución

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Línea de trabajo

Empieza reproduciendo el flujo de trabajo notificado en copilot-cli y compara las referencias generadas con data/Kibana_dashboard_objects/...CROSSWALK.csv y el notebook de verificación disponible. Comprueba cómo presenta el modelo render.py, compare.py, render_alert.py, compare_alerts.py y los nombres de los datasets. Se considera terminado cuando la guía generada distingue entre los archivos y datos existentes y los archivos sugeridos, o etiqueta claramente los pasos de creación.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
elasticsearch, python, shell, yaml
Área
ai, cli
Tipo de issue
Error
Dificultad
5/5
Tiempo estimado
Más de una semana
Estado de actividad
Activo
Claridad
Necesita aclaración
Aptitud para principiantes
25/100

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