InputGuard

Catch unclear inputs before they become bad AI outputs.

InputGuard is a pre-flight input clarity layer. It sits between a user's input and an LLM call. It detects vague, incomplete, or unspecific inputs before they reach the AI — saving the correction cycle that wastes time and tokens when the AI guesses wrong.

Zero LLM calls. Zero external dependencies. Pure local Python.

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The problem it solves

Without InputGuard

vague input → AI guesses → wrong output → correction loop → more tokens → repeat

With InputGuard

vague input → InputGuard flags what's missing → user clarifies → AI gets it right first time

A non-technical user asks to "fix my code." The AI guesses at the problem, picks the wrong function, and produces a fix for something that was not broken. InputGuard catches the gaps locally, in milliseconds, before any tokens are spent.

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Install

pip install inputguard

Python 3.9+. No external dependencies.

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Quick start

from inputguard import InputGuard

guard = InputGuard()
result = guard.analyze("fix my code")

print(result.detected_intent)   # 'debug'
print(result.status)            # 'needs_clarification'
print(result.clarity_score)     # 35
print(result.gaps)              # ['error description', 'expected vs actual behavior', 'code context']
print(result.is_clear())        # False

for rec in result.recommendations:
    print(rec["gap"])
    print(rec["what_is_missing"])
    print(rec["what_to_provide"])
    print(rec["why_it_matters"])
    print()

analyze() takes an optional domain argument, which defaults to "coding". Phase 1 supports "coding" only. Intent detection is automatic — no extra parameters needed.

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Modes

from inputguard import InputGuard

# Warning mode (default) — flags gaps but allows the input through
warn_guard = InputGuard(mode="warning")

# Strict mode — blocks inputs that fall below the clarity threshold
strict_guard = InputGuard(mode="strict")

result_warn   = warn_guard.analyze("build a REST API")
result_strict = strict_guard.analyze("build a REST API")

print(result_warn.status)    # 'needs_clarification'
print(result_strict.status)  # 'blocked'
print(result_warn.clarity_score == result_strict.clarity_score)  # True

The clarity score is mode-independent. Only the status threshold changes.

Status	Warning mode	Strict mode
ready	score ≥ 85	score ≥ 85
usable_with_warnings	score 60–84	never
needs_clarification	score < 60	score 65–84
blocked	never	score < 65

Use warning when you want to surface gaps to the user without blocking. Use strict when you want to refuse to forward vague input to the LLM.

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How intent detection works

InputGuard automatically detects what kind of coding input it is receiving. No extra parameters needed. The same .analyze() call handles all five intent types.

Intent	What it covers	Example input
build	New app or system from scratch	"build a REST API"
debug	Fixing errors, bugs, broken code	"fix my code, getting a TypeError"
optimization	Performance, speed, refactoring	"make this function faster"
explanation	Understanding code or concepts	"explain what this decorator does"
feature	Adding to existing code	"add search to my existing React app"

guard = InputGuard()

guard.analyze("build a REST API").detected_intent           # 'build'
guard.analyze("fix my code").detected_intent               # 'debug'
guard.analyze("make this faster").detected_intent          # 'optimization'
guard.analyze("explain how async works").detected_intent   # 'explanation'
guard.analyze("add search to my existing app").detected_intent  # 'feature'

When an input is ambiguous, debug always wins. "Fix this slow function" is a debug request, not optimization. Priority order is: debug → optimization → explanation → feature → build.

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What gets checked

Build inputs

Requests like "build me an X", "integrate with Y", "create a Z". Eight rules run against the normalized input.

Rule code	What it catches	Severity
missing_language	A build/action verb is present but no programming language or framework is named.	high
missing_api_structure	API terms appear (REST, GraphQL, endpoint…) but no routes, HTTP methods, or request/response shape are described.	high
missing_data_model	Storage terms appear (database, CRUD, schema…) but no fields, entities, or model are described.	high
missing_integration_specifics	A third-party service is named (Stripe, Twilio, AWS…) but no specific action or feature is described.	medium
missing_auth_type	Authentication is mentioned but no concrete type (JWT, OAuth, magic link…) is named.	high
missing_output_format	A top-level build verb is present but no output format (web app, CLI, REST API, script…) is named. Does not fire on connector verbs like "integrate" or "add".	medium
intent_without_language	Build intent is expressed without a creation verb ("I need…", "I want…", "looking for…") and no language is named.	high
insufficient_context	Catch-all. Fires when nothing else fires, the input is at least 3 words, and is not a question.	high

Debug inputs

Requests like "fix my code", "it's not working", "getting a TypeError".

Rule code	What it catches	Severity
missing_error_message	Debug intent detected but no error message, exception name, or stack trace described.	high
missing_expected_vs_actual	No description of what should happen vs what actually happens.	high
missing_debug_code_context	No language, function name, file, or snippet referenced.	medium

Optimization inputs

Requests like "make this faster", "optimize my code", "refactor this function".

Rule code	What it catches	Severity
missing_optimization_target	Optimization requested but no specific function, component, or area identified.	high
missing_performance_baseline	No current measurement or observed problem described.	medium
missing_optimization_constraint	No constraints or acceptable tradeoffs mentioned.	low

Explanation inputs

Requests like "explain this code", "what does this do", "how does this work".

Rule code	What it catches	Severity
missing_code_reference	Explanation requested but no specific code, function, or concept referenced.	high
missing_explanation_depth	No indication of depth or detail level requested.	low

Feature inputs

Requests like "add search to my existing app", "extend my current API with pagination".

Rule code	What it catches	Severity
missing_existing_stack	Feature addition requested but no existing language, framework, or tech stack mentioned.	high
missing_feature_scope	Feature requested but no definition of what it should specifically do.	high
missing_completion_criteria	No definition of what done looks like for this feature.	low

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The result object

analyze() returns an immutable AnalysisResult with these fields:

Field	Type	Description
status	str	One of "ready", "usable_with_warnings", "needs_clarification", "blocked"
clarity_score	int	0 to 100
detected_intent	str	Which intent was detected: build, debug, optimization, explanation, or feature
gaps	List[str]	Gap names, in the order rules fired
recommendations	List[dict]	One dict per gap (see next section)
findings	List[RuleFinding]	Raw rule findings (code, message, severity, gap)
interpretation_note	Optional[str]	Set when the input is highly ambiguous (score < 50 or two or more high-severity findings)

Helpers:

• result.is_clear() — True only when status is "ready"
• result.to_dict() — full result as a plain JSON-serializable dict

Example result.to_dict() for guard.analyze("fix my code"):

{
  "status": "needs_clarification",
  "clarity_score": 35,
  "detected_intent": "debug",
  "gaps": [
    "error description",
    "expected vs actual behavior",
    "code context"
  ],
  "recommendations": [
    {
      "gap": "error description",
      "what_is_missing": "You haven't included the actual error message or exception.",
      "what_to_provide": "Copy and paste the exact error message. For example: 'I'm getting TypeError: cannot read property of undefined on line 23'.",
      "why_it_matters": "The exact wording tells the AI exactly what went wrong. Without it, the AI guesses and often fixes the wrong thing."
    },
    {
      "gap": "expected vs actual behavior",
      "what_is_missing": "You haven't described what should happen vs what actually happens.",
      "what_to_provide": "Describe both. For example: 'it should return a list of users but instead returns None every time'.",
      "why_it_matters": "Without this, the AI is guessing what the problem is. It may fix something that was not broken."
    },
    {
      "gap": "code context",
      "what_is_missing": "You haven't pointed to the specific part of your code with the problem.",
      "what_to_provide": "Name the language and the function. For example: 'this is a Python function called get_users()'.",
      "why_it_matters": "The more specific you are, the more targeted the fix will be."
    }
  ],
  "findings": [
    {"code": "missing_error_message",      "message": "Debug request detected but no error message or exception described.", "severity": "high",   "gap": "error description"},
    {"code": "missing_expected_vs_actual", "message": "No description of expected vs actual behavior provided.",            "severity": "high",   "gap": "expected vs actual behavior"},
    {"code": "missing_debug_code_context", "message": "No code context provided.",                                          "severity": "medium", "gap": "code context"}
  ],
  "interpretation_note": "This input is ambiguous in multiple ways. Addressing each gap below before sending will prevent the AI from making assumptions that lead to the wrong output."
}

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Recommendations

Every entry in result.recommendations is a plain dict with four keys, all written for non-technical users:

for rec in result.recommendations:
    print(rec["gap"])             # which gap this addresses
    print(rec["what_is_missing"]) # plain English — what the user forgot
    print(rec["what_to_provide"]) # concrete example they can copy
    print(rec["why_it_matters"])  # what goes wrong if they skip it

Gap names by intent type:

• Build: programming language, api structure, data model, integration specifics, authentication type, output format, task context
• Debug: error description, expected vs actual behavior, code context
• Optimization: optimization target, performance baseline, optimization constraint
• Explanation: code reference, explanation depth
• Feature: existing stack, feature scope, completion criteria

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Real-world examples

from inputguard import InputGuard
guard = InputGuard()

# Build — vague
guard.analyze("build me an app")
#   detected_intent: 'build'
#   score:  60
#   status: usable_with_warnings
#   gaps:   ['programming language', 'output format']

# Debug — vague
guard.analyze("fix my code")
#   detected_intent: 'debug'
#   score:  35
#   status: needs_clarification
#   gaps:   ['error description', 'expected vs actual behavior', 'code context']

# Optimization — vague
guard.analyze("make this faster")
#   detected_intent: 'optimization'
#   score:  55
#   status: needs_clarification
#   gaps:   ['optimization target', 'performance baseline', 'optimization constraint']

# Build — fully specified
guard.analyze(
    "Build a REST API using FastAPI. "
    "Store users in PostgreSQL with fields: id, name, email. "
    "Expose GET /users and POST /users endpoints. "
    "Add JWT authentication."
)
#   detected_intent: 'build'
#   score:  100
#   status: ready
#   gaps:   []

# Debug — fully specified
guard.analyze(
    "Fix this Python function get_users() — it should return a list "
    "of user dicts but instead returns None. "
    "The error says: TypeError: NoneType is not iterable on line 45."
)
#   detected_intent: 'debug'
#   score:  100
#   status: ready
#   gaps:   []

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Integration pattern

Drop it in front of your existing LLM call. Two minimal patterns:

from inputguard import InputGuard

guard = InputGuard(mode="warning")

def handle_user_input(user_input: str):
    result = guard.analyze(user_input)

    if result.status in ("needs_clarification", "blocked"):
        # Return feedback to the user before calling the LLM
        return {
            "status": result.status,
            "detected_intent": result.detected_intent,
            "gaps": result.gaps,
            "recommendations": result.recommendations,
        }

    # Input is clear enough — proceed to LLM
    return call_your_llm(user_input)

For a hard gate, use mode="strict" and check result.is_clear():

guard = InputGuard(mode="strict")

def handle_user_input(user_input: str):
    result = guard.analyze(user_input)
    if not result.is_clear():
        return {
            "status": result.status,
            "detected_intent": result.detected_intent,
            "gaps": result.gaps,
            "recommendations": result.recommendations,
        }
    return call_your_llm(user_input)

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Package layout

inputguard/
├── inputguard/
│   ├── __init__.py
│   ├── analyzer.py
│   ├── detector.py
│   ├── recommender.py
│   ├── scorer.py
│   ├── types.py
│   ├── py.typed
│   └── rules/
│       ├── __init__.py
│       ├── coding.py
│       ├── debug.py
│       ├── optimization.py
│       ├── explanation.py
│       └── feature.py
├── tests/
│   ├── test_coding.py
│   ├── test_detector.py
│   ├── test_debug.py
│   ├── test_optimization.py
│   ├── test_explanation.py
│   └── test_feature.py
├── pyproject.toml
└── README.md

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Development

pip install -e ".[dev]"
python -m pytest tests/ -v

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Publishing

python -m build
python -m twine check dist/*
python -m twine upload dist/*

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License

MIT — see LICENSE for the full text.

Copyright © 2026 Nihanth Kalisetti.