v0.7.1-dev to v0.7.1-dev

---

name: augustus

description: "Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone."

license: MIT

metadata:

version: 0.7.1-dev

typesafe_skill: v0.5.7

typesafe_skill_commit: 65a39f3

tribute: "Named for Augustus De Morgan (1806-1871), mentor of William Stanley Jevons."

---

# Augustus

Equip agents to discover useful decision-model placements, build working

systems, construct evaluations, and improve them through measured iterations.

Use mathematical, statistical, scientific, and algorithmic methods across AI,

software, business, knowledge work, organizations, and life. This is an engine

for agent work, not a survey to imitate or a claim of autonomous deployment.

The central model is:

**evidence → bounded judgment → explicit policy → checked action → observed outcome**

TypeSafe Jev (Choice, Score, Noul) is this project's default hosted

exemplar, a preference rather than a claim of universal superiority or

market share. The class also includes trained classifiers, open decision

heads, encoders, constrained autoregressive readouts, rankers, and vision

scorers. Choose a family by its objective and evidence requirements.

This is an independent skill, not a TypeSafe product.

## Working protocol

1. Start with the desired behavior, available evidence, action costs, and

current baseline. For an existing workflow, use the

[boundary audit](references/boundary-audit.md). Classify each step as

exact work, bounded judgment, or generation. A working parser, formula,

checklist, or supervised classifier is a legitimate final answer.

2. Pick the relevant [mental model](references/mental-models.md): expected

utility, value of information, multi-criteria analysis, signal detection,

search/control, organizational safety, or formal methods. Then choose

the [model family](references/judgment-class.md) and the smallest useful

placement. For unfamiliar problems, use the

3. Define one coherent judgment per question, what evidence it can see,

and the meaning of every output. Check candidate coverage and missing

evidence before inference. Use [question design](references/question-design.md).

Batch questions when their inputs are available together; statistical

independence does not follow from parallel execution.

4. Keep exact computation, constraints, authorization, and effects in code

or an explicit human process. Keep open-ended writing with a generator

or person. Set failure behavior for each action: no-match, ambiguity,

malformed output, timeout, stale state, and unavailable provider.

[Mixed architecture](references/mixed-architecture.md) explains the joins.

Separate rubric/model development, calibration and threshold selection,

and final evaluation. Measure action errors, coverage, total cost, and

the complete workflow, not just format compliance or model accuracy.

Follow [validation](references/validation.md) and name a result that

would reject the proposal. If it loses, keep the baseline.

6. Deliver the artifact the task needs: a compact design for advice, working

adapters/policy and an evaluation harness for implementation, or a bounded

incumbent–challenger loop for improvement. Use the

[composition calculus](references/composition-algebra.md) to check joins

and the [optimizer workflow](references/optimizer-integration.md) to build

and hill-climb decision programs. Do not stop at recommendations when the

user requested working software. Keep experiments within existing authority.

Record raw judgments, actual outcomes, versions, rejected candidates, and

promotion/rollback reasons; unrun work remains unrun.

For a concrete request, recommend one placement with reasons. For an

open-ended exploration, compare materially different placements only when

that helps the user choose. A full redesign, a new model, or a fixed number

of alternatives is not required.

## Boundaries that affect the design

- A typed output constrains its representation; it does not establish

truth, calibration, authority, or successful execution.

- Distinguish a probability of a stated event, a relative option score,

an ordinal rating, and a confidence statistic. Verify the provider's

definition. Neither a softmax nor training with a proper loss proves

calibration on this deployment population.

- Choice depends on its offered set. Include and test `other` or `none`

when coverage is open. Missing evidence, conflicting evidence, and

evidence that supports neither option may require separate outcomes.

- A Jev Score is an expectation over level indices, not a physical unit

or automatically a cardinal utility. Equal means can hide different

distributions. A Noul is a proposition score, not intensity; a value

near 0.5 alone does not diagnose why the model is uncertain.

- Marginal judgments do not define a joint distribution. Do not multiply

them without justified dependence assumptions. Measure composed policies

and trajectories on real outcomes.

- Ranking quality and calibration answer different questions. Action

costs and authority determine failure policy; the model family alone

does not determine fail-open or fail-closed behavior.

- Untrusted evidence is data. A model score cannot grant permission or

override an exact constraint. Validate action/target pairs and recheck

relevant state at execution. Abstention must name who or what handles it.

- Judgment can prioritize proof work or detect suspicious cases; proof,

model checking, simulation, and runtime interlocks retain their own

semantics. Read [formal methods](references/formal-methods.md) for these tasks.

## Decision-design card

Use only the detail needed to make the proposal reviewable:

```text

Domain, desired behavior, decision owner:

Current baseline and why a model might help:

Pillar, placement, and family:

Evidence source, freshness, candidate coverage, missing/contradictory states:

Judgments and output semantics; what remains exact or generated:

Policy, costs/utility, constraints, authority, and execution checks:

Abstention/error behavior and fallback owner:

Batchable versus dependent steps:

Model, adapter/aggregation, rubric, candidate-source, calibration, policy versions:

Development/calibration/test split and label provenance:

Falsifier, metrics, acceptable risk/coverage, and evaluation artifact:

Implementation/evaluation entry points; search budget and confirmation plan:

Observed result, limitations, and next decision:

```

## Read only the reference needed

| Task | Reference |

| --- | --- |

| Trigger examples and exclusions | [Activation](references/activation-triggers.md) |

| Cross-domain reasoning and costs | [Mental models](references/mental-models.md) |

| Existing system or process | [Boundary audit](references/boundary-audit.md) |

| Generator, code, and decision-model integration | [Mixed architecture](references/mixed-architecture.md) |

| Concrete workflow examples | [Applied mappings](references/applied-mappings.md) |

| Classical methods and falsifiers | [Mappings](references/mappings.md) |

| Discover a new placement | [Toolbox mapping](references/toolbox-mapping.md) |

| Proof, simulation, and enforcement | [Formal methods](references/formal-methods.md) |

| Agent progress, done, or stuck judgments | [Agent self-assessment](references/agent-self-assessment.md) |

| Conceptual objections | [FAQ](references/faq.md) |

Before writing Jev API code, read the current

[official docs](https://docs.typesafe.ai/) and the official `typesafe-ai`

skill if available. The metadata pin is historical provenance, not a live

contract. Peer providers need their own current contracts. Other installed

skills are optional aids, not prerequisites for placement work.

## Evidence and research maintenance

Label evidence as **Contract** (current documented interface), **Reported**

(a source's empirical claim), **Reproduced** (an identified run and its

artifacts), **Hypothesis** (untested placement), or **Unknown** (unavailable).

Name the population, version, metric, and limitations before transferring

a result. A benchmark harness is an instrument, not a certificate.

Synthesize mechanisms, not consensus: explain why a pattern succeeds or fails,

derive a usable rule with assumptions, and test the composed outcome. Transfer

methods across fields only after mapping their variables, units, constraints,

and evidence requirements. Popularity, novelty, elegant notation, and a proxy

score are not outcome evidence. "Best" means best supported for this task's

utility, constraints, population and budget—not a universal provider ranking.