Ideale per
- Partire da un prompt strutturato e riutilizzabile invece di una chat vuota.
- Adattare il prompt ai propri input mantenendo intatto l'intento originale.
- Flussi di lavoro legati a Software Development, Debug & Testing.
Runner del loop di coding agentico
Compila questo promptEsegue un loop di coding agentico in cui attività, metodo di verifica e criterio di completamento sono selezionati in modo indipendente, iterando finché il controllo esterno non passa.
Modello di prompt
Execute the following task as an agentic loop. Do not ask for confirmation between iterations.
### TASK
Perform this action: {{Action: # Implement, Implement a new feature, Implement a missing function body, Write unit tests for existing code, # Fix, *Fix a failing test or bug, Fix all compiler warnings, Resolve linter violations, # Improve, Refactor without changing behavior, Optimize a slow code path, Migrate code to a newer API or framework version, Add missing error handling}}
### CONTEXT
{{Project Context: if used in an AI agent use the current project}}
### LOOP STRUCTURE
Repeat the following cycle:
1. ACT: Apply the smallest meaningful change toward the task.
2. VERIFY: Check the result using this method: {{Verification: # Automated, *Run the test suite, Compile or build the project, Run the linter and static analysis, Execute the script or program and inspect the output, Validate output against a schema or contract, # Manual proxy, Run a single reproduction case, Compare behavior before and after the change, Measure performance with a benchmark, Diff the output against a known-good reference}}
3. FEED BACK: If verification fails, take the raw error output (stack trace, test report, compiler message) unmodified into the next iteration. Do not summarize or interpret it before acting on it.
### COMPLETION CRITERION
Stop the loop only when this condition is met: {{Done When: *All tests pass with zero failures, Build succeeds with zero warnings, Linter reports zero violations, Program runs without exceptions on the given input, Output validates against the schema, Benchmark meets the target threshold, Reproduction case no longer fails, Behavior is identical before and after refactoring, All new code paths are covered by tests}}
### SAFEGUARDS
- Maximum {{Max Iterations:5}} iterations. If the limit is reached without success, stop and report the last error, what was attempted, and a hypothesis for the root cause.
- If the same error occurs in two consecutive iterations, stop immediately and report the stall instead of retrying.
- Touch only files required by the task.
### ADDITIONAL INFORMATION:
{{ Additional Information: none }}
After completion, summarize in 3 short bullet points: what was changed, how it was verified, and how many iterations were needed.
Guida al prompt
Come usarlo
- Copia il modello di prompt completo.
- Sostituisci ogni variabile con il tuo contesto o scegli una delle opzioni suggerite.
- Incolla il prompt completato in ChatGPT, Claude, Gemini o un altro assistente IA.
Variabili in questo prompt
Action
# Implement, Implement a new feature, Implement a missing function body, Write unit tests for existing code, # Fix, Fix a failing test or bug, Fix all compiler warnings, Resolve linter violations
Project Context
if used in an AI agent use the current project
Verification
# Automated, Run the test suite, Compile or build the project, Run the linter and static analysis, Execute the script or program and inspect the output, Validate output against a schema or contract, # Manual proxy, Run a single reproduction case
Done When
All tests pass with zero failures, Build succeeds with zero warnings, Linter reports zero violations, Program runs without exceptions on the given input, Output validates against the schema, Benchmark meets the target threshold, Reproduction case no longer fails, Behavior is identical before and after refactoring
Max Iterations
5
Additional Information
none
Esempio di partenza
Execute the following task as an agentic loop. Do not ask for confirmation between iterations. ### TASK Perform this action: # Implement ### CONTEXT if used in an AI agent use the current project ### LOOP STRUCTURE Repeat the following cycle: 1. ACT: Apply the smallest meaningful change toward the task. 2. VERIFY: Check the result using this method: # Automated 3. FEED BACK: If verification fails, take the raw error output (stack trace, test report, compiler message) unmodified into the next iteration. Do not summarize or interpret it before acting on it. ### COMPLETION CRITERION Stop the loop only when this condition is met: All tests pass with zero failures ### SAFEGUARDS - Maximum 5 iterations. If the limit is reached without success, stop and report the last error, what was attempted, and a hypothesis for the root cause. - If the same error occurs in two consecutive iterations...
Come funzionano le variabili
Sintassi delle variabili
Le variabili sono racchiuse tra {{ e }} e seguono questo schema:
{{variable name: option1, option2, option3 }}
Variabili predefinite
Una selezione può fare riferimento a un elenco di variabili predefinito usando le parentesi quadre. Appaiono in [arancione] e forniscono valori di uso comune come colori, toni o lingue.
{{Tone: [tones] }}
Elenchi di scelta personalizzati
Puoi anche fornire un elenco di scelte in linea, separate da virgole.
{{Format: bullet points, paragraphs, numbered list }}
Suggerimento: non hai bisogno dell'app PUCO per usare questi prompt! Copia semplicemente il modello e sostituisci ogni sezione {{…}} con il tuo testo direttamente in ChatGPT, Claude, Gemini o qualsiasi altro assistente IA.