Metadata-Version: 2.4
Name: aura-pce
Version: 0.1.0
Summary: The I CARE self-test gate: an operational advisor that runs four self-tests before it speaks, and refuses to speak when it cannot verify itself.
Author: Illia Hladkyi
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or Derivative
                  Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright 2026 Illia Hladkyi
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: Homepage, https://github.com/myfjin/aura-pce
Project-URL: Project, https://realityoptimizer.app
Keywords: monitoring,observability,self-testing,advisor,sysadmin,telemetry,honest-ai
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: System Administrators
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: System :: Monitoring
Classifier: Topic :: System :: Systems Administration
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: live
Requires-Dist: numpy>=1.21; extra == "live"
Requires-Dist: sentence-transformers>=2.2; extra == "live"
Requires-Dist: psutil>=5.9; extra == "live"
Provides-Extra: embed
Requires-Dist: numpy>=1.21; extra == "embed"
Requires-Dist: sentence-transformers>=2.2; extra == "embed"
Provides-Extra: dev
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Dynamic: license-file

# aura-pce — the "I CARE" self-test gate

[![CI](https://github.com/myfjin/aura-pce/actions/workflows/ci.yml/badge.svg)](https://github.com/myfjin/aura-pce/actions/workflows/ci.yml)
[![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/)

> An operational advisor that **runs four self-tests before it speaks, and refuses to
> speak when it cannot verify itself.** This repository is the open-mechanism half of
> AURA's Pattern Composition Engine — the part you can install, run, read, and check today.

Most monitors and AI copilots answer whether or not they had grounds, and their reliability
is asserted by their vendors rather than measured in the open. This engine inverts that.
Before emitting any advice ("this is a real anomaly", "this recovery procedure applies") it
pauses and runs the **I CARE** suite:

1. **Axiom** — is this advice backed by a verified rule?
2. **Type fit** — do the situation's input types actually match what the rule consumes?
3. **Robustness** — is the recognition stable when the input is perturbed with noise?
4. **Non-discrimination** — does the answer survive swapping only the identities in it?

If any check cannot pass, the gate stays silent — or defers out loud ("no verified rule").
Silence and deferral are first-class outputs, not failures.

## Install

```bash
pip install aura-pce            # the core: pure standard library, zero dependencies
pip install "aura-pce[live]"    # + a sentence embedder + psutil, for `watch` on your box
```

The **core** needs nothing but Python ≥ 3.10 — the provenance proof, the self-tests and the
earned-metric reports all run on the standard library alone. The **`[live]`** extra adds the
one thing recognition needs (a sentence embedder) plus a cross-platform telemetry reader, so
`aura-pce watch` can gate the real state of the machine you run it on.

## Quick start

```bash
aura-pce prove                 # the provenance proof: the self-tests survive their own
                               # linter, and each of the four checks has a can-fail witness
aura-pce level                 # the two earned metrics — never conflated
aura-pce selftest              # run every self-test this install can run
```

`aura-pce prove` is the honest headline: it proves the self-tests are real and prints the
validity level *with its n*. On a fresh install that level is `unproven [n=0]` for the advice
gate — because no advice emissions have been graded here — which is the correct, non-inflated
output. There is no code path that prints an unearned number.

### Point it at your box

```bash
pip install "aura-pce[live]"
aura-pce init                          # write the sample registry + demo ledger
aura-pce watch --source auto           # gate REAL local telemetry (Ctrl-C to stop)
aura-pce watch --source psutil --log   # cross-platform; log gated EMITs for grading
aura-pce watch --source node_exporter:http://localhost:9100/metrics
```

`watch` samples a telemetry source, keeps a rolling per-metric baseline, and runs every
anomaly through the same gate — a calm machine stays silent, a genuine spike is recognised,
type-checked, and either **EMITted** (gated advice), **WITHHELD** (recognised but not
verifiable) or **DEFERred** (no matching axiom). Grade what it emits:

```bash
aura-pce list                          # gated emits awaiting a human grade
aura-pce grade <hash> real|noise       # dispose each one; the precision earns its n
```

## What this is, and is not

- **It is** the mechanism, and the mechanism is provably honest: the self-tests pass an
  independent linter over themselves (a self-test that cannot fail is not a self-test), and
  each of the four checks ships with a demonstrated can-fail witness.
- **It is not** a correctness oracle. A deterministic self-test verifies *diligence* (the
  engine checked itself in four ways), not *correctness* (that the advice is right). Advice
  can pass all four checks and still be wrong on unusual data — which is exactly what outcome
  grading captures.
- **It does not** ship a reliability percentage. Reliability is *earned* over graded outcomes
  and always reported with its sample size `n`; at `n=0` the code prints `unproven`, never a
  naked 50%.

Two metrics, kept on separate axes and never conflated:

| metric | question | today, on the sample |
|--------|----------|----------------------|
| **validity LEVEL** | of gated advice, how often did it *hold*? (outcome-blind stop-to-think) | `unproven [n=0]` |
| **mesh precision** | of flagged anomalies, how many were *real*? (human-graded outcome) | `0.6 [n=3]` (demo ledger) |

## The sample vs. the real knowledge

This repository ships a small, hand-written **sample registry** (six sysadmin axioms) and a
**synthetic graded ledger** — enough to run every command above and watch the mechanism work.
They are clearly labelled demonstration data.

They are **not** the AURA knowledge base. The full deployment recognises against sovereign
registries of hundreds of machine-verified rules (verified by compiler or real execution, not
by opinion), and earns its reliability number on live infrastructure. That knowledge and that
earned number are the project's product; they are not in this repository. What is here is the
honest machinery, open for anyone to audit.

## Live recognition & the embedder

Recognition matches a free-text situation to an axiom by embedding. In the full deployment a
private module supplies the embedder; here [`embedder.py`](embedder.py) is the public
reference stand-in (`[live]` extra), exposing exactly `embed(list[str]) -> ndarray` of
L2-normalised vectors. It uses `sentence-transformers` (or `chromadb`) — whichever is
installed. The core never imports it, so the base install stays dependency-free.

## Recognition calibration

The acceptance floor is **measured, not guessed**. [`calibrate.py`](calibrate.py) runs a
labelled in-domain / out-of-domain probe set through the real embedder and derives a floor (and
a domain-confidence margin) that separates the two, then writes `calibration.json` into the
data home. On the shipped sample the two clouds separate cleanly (strongest out-of-domain
`0.164` < weakest in-domain `0.291`); the margin gate is the knob that additionally curbs
over-fire on a *large* registry. Re-run it for your own embedder or registry:

```bash
aura-pce calibrate --report            # measure + print the before/after, don't write
aura-pce calibrate                     # + write calibration.json into the data home
```

See [docs/CALIBRATION.md](docs/CALIBRATION.md) for the method and the measured numbers.

## Layout

| File | Role |
|------|------|
| `i_care.py` | the four-check gate + the earned validity level |
| `logic_lane.py` | axiom recognition over a registry of verified rules (the embedder seam) |
| `ontology.py` | the noun type system used by the type-fit check |
| `mesh_bridge.py` | renders telemetry into typed situations and gates them (`gate_typed`) |
| `mesh_grade.py` | logs each emission, grades it, reports precision with `n` |
| `sources/` | telemetry adapters: `proc`, `psutil`, `node_exporter`, `journal` |
| `embedder.py` | the reference sentence embedder for live recognition (`[live]`) |
| `calibrate.py` | derives the recognition floor from a measured distribution |
| `outcomes.py` | the Beta earned-frequency estimator (unproven at n=0) |
| `wisdom.py` | the append-only fire log the gate writes to |
| `assert_linter.py` | classifies assertions by strength; proves the self-tests real |
| `cli.py` | the `aura-pce` console entry point |
| `paths.py` | resolves the one data home (env → clone `./data` → user data dir) |
| `make_sample_data.py` | generates the demonstration registry + ledger |

More: [docs/QUICKSTART.md](docs/QUICKSTART.md) · [docs/API.md](docs/API.md) ·
[docs/CALIBRATION.md](docs/CALIBRATION.md).

## About

Part of **AURA**, a research program in self-testing infrastructure by Reality Optimizer —
[realityoptimizer.app](https://realityoptimizer.app). Sibling open tools:
[folder-nature](https://pypi.org/project/folder-nature/) (semantic identity + signing for file
trees), [whypass](https://pypi.org/project/whypass/) (a claim-discipline linter),
[copresence](https://pypi.org/project/copresence/).

AURA is developed by a small human–AI working group; AI collaborators are named contributors
in its repositories, and every claim in its documentation is written to be checkable rather
than believed.

## License

Apache-2.0. See [LICENSE](LICENSE).
