hired.alignment.base#

Schemas for JD-vs-candidate alignment analysis.

A job description is decomposed into Requirement records; each is analysed into a RequirementRecord carrying a three-valued MatchState, a Bucket, grounding Evidence, and the fields the elicitation loop needs. The whole analysis is an AlignmentReport — verdict-first, evidence-quoted, banded (never a single false-precision percentage).

See misc/docs/DESIGN.md §6.

Classes

`AILeverage`(*values)	How much the candidate's standing AI-agent capability lowers the ramp.
`AlignmentReport`(**data)	A complete, verdict-first alignment analysis for one JD.
`Bucket`(*values)	The four alignment buckets.
`Clarification`(**data)	A question to ask the candidate to resolve a decision-relevant unknown.
`CloseMethod`(*values)
`Closeability`(*values)	How hard a gap is to close (Axis B), after the AI-leverage modifier.
`Evidence`(**data)	A verbatim grounding span for a match judgment.
`FieldCompleteness`(*values)	Local closed-world flag: may a missing value be read as a real negative?
`FitBand`(*values)
`InterviewPrep`(**data)
`MatchType`(*values)	How the candidate's background relates to the requirement (Axis A).
`NextAction`(**data)
`Recommendation`(*values)
`Requirement`(**data)	One atomic requirement extracted verbatim from a job description.
`RequirementClass`(*values)	How decision-critical a requirement is.
`RequirementRecord`(**data)	The analysis of one requirement against the candidate's knowledge.
`ScoreSummary`(**data)
`SkillType`(*values)
`TimeToClose`(*values)
`Transferability`(*values)
`Verdict`(**data)

class hired.alignment.base.AILeverage(*values)[source]#

Bases: str, Enum

How much the candidate’s standing AI-agent capability lowers the ramp.

Applies to codified / knowledge-breadth gaps, not tacit-judgment or enduring-aptitude gaps (those it cannot rescue).

class hired.alignment.base.AlignmentReport(**data)[source]#

Bases: BaseModel

A complete, verdict-first alignment analysis for one JD.

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.Bucket(*values)[source]#

Bases: str, Enum

The four alignment buckets.

class hired.alignment.base.Clarification(**data)[source]#

Bases: BaseModel

A question to ask the candidate to resolve a decision-relevant unknown.

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.CloseMethod(*values)[source]#: Bases: str, Enum

class hired.alignment.base.Closeability(*values)[source]#

Bases: str, Enum

How hard a gap is to close (Axis B), after the AI-leverage modifier.

class hired.alignment.base.Evidence(**data)[source]#

Bases: BaseModel

A verbatim grounding span for a match judgment.

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.FieldCompleteness(*values)[source]#

Bases: str, Enum

Local closed-world flag: may a missing value be read as a real negative?

class hired.alignment.base.FitBand(*values)[source]#: Bases: str, Enum

class hired.alignment.base.InterviewPrep(**data)[source]#

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.MatchType(*values)[source]#

Bases: str, Enum

How the candidate’s background relates to the requirement (Axis A).

class hired.alignment.base.NextAction(**data)[source]#

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.Recommendation(*values)[source]#: Bases: str, Enum

class hired.alignment.base.Requirement(**data)[source]#

Bases: BaseModel

One atomic requirement extracted verbatim from a job description.

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.RequirementClass(*values)[source]#

Bases: str, Enum

How decision-critical a requirement is.

class hired.alignment.base.RequirementRecord(**data)[source]#

Bases: BaseModel

The analysis of one requirement against the candidate’s knowledge.

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.ScoreSummary(**data)[source]#

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class hired.alignment.base.SkillType(*values)[source]#: Bases: str, Enum

class hired.alignment.base.TimeToClose(*values)[source]#: Bases: str, Enum

class hired.alignment.base.Transferability(*values)[source]#: Bases: str, Enum

class hired.alignment.base.Verdict(**data)[source]#

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].