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https://github.com/samuraiwriter7/structural-rumination-layer

A rumination and error-digestion protocol for AI-assisted specification workflows.
https://github.com/samuraiwriter7/structural-rumination-layer

ai-alignment ai-governance ai-safety error-handling github-actions human-ai-collaboration json-schema llm-agents recurrence-prevention validation

Last synced: 29 days ago
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A rumination and error-digestion protocol for AI-assisted specification workflows.

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README 

          
# Structural Rumination Layer



**Structural Rumination Layer** is a protocol for reducing repeated structural errors in AI-assisted specification workflows.



It does not aim to make AI perfectly error-free.



Instead, it helps AI systems and human operators verify outputs, reflect on mistakes, ruminate on repeated error patterns, convert them into recurrence-prevention rules, share those rules across multiple structural wings, connect them to AI agent execution boundaries, and gather release-readiness evidence before candidate releases.



In short:



> Mistakes may occur.

> But the same structural mistake should not pass through the workflow unchanged twice.



日本語では：



> ミスは起こる。

> だが、同じ構造的過ちは二度通さない。



---



## Purpose



AI systems can generate advanced reasoning, documentation, schemas, examples, validators, and release notes.



However, in practical GitHub specification work, they may still repeat basic structural mistakes, such as:



* referencing files that do not exist,

* adding validator targets before schema/example pairs are complete,

* drifting between README, CHANGELOG, and actual repository state,

* assuming `.github/workflows` exists when it does not,

* producing polished documentation before verifying concrete file paths,

* repeating YAML / JSON / Python syntax mistakes during rapid iteration,

* recommending releases before validation evidence is complete,

* allowing validation checks to become too broad or self-triggering,

* pushing repeated debugging work back onto the human operator.



This repository defines a minimal protocol for digesting those errors instead of merely correcting them once.



The goal is not binary perfection.



The goal is:



> Bias correction, recurrence reduction, cross-wing failure sharing, AI agent boundary checks, release-readiness gating, and human burden reduction through structured rumination.



---



## Core Concept



`structural-rumination-layer` is a minimal OS for treating AI development failures not as disposable logs, but as structural data that can be recorded, digested, converted into recurrence-prevention rules, shared across wings, connected to AI agent execution boundaries, and gathered into pre-release readiness gates.



The project evolves through these layers:



```text

v0.1 = Structural Rumination Record

       Failure becomes structured memory.



v0.2 = Executable Recurrence Rules

       Structured memory becomes executable prevention.



v0.3 = Multi-Wing Integration

       Recurrence knowledge becomes shareable across wings.



v0.4 = AI Agent Hooks

       Recurrence knowledge becomes agent-facing execution guidance.



v0.5 = Pre-release Rumination Gate

       Rumination evidence becomes release-readiness guidance.

```



In metaphorical terms:



```text

v0.1 = digestive layer

v0.2 = first immune response

v0.3 = cross-wing nervous system

v0.4 = agent-facing reflex layer

v0.5 = pre-release gate

```



---



## Core Lifecycle



The core lifecycle is:



```text

Forward Generation

  -> Reverse Trace Check

  -> Verification

  -> Reflection

  -> Rumination

  -> Breathing Alignment

  -> Error Visibility

  -> Next-Gate Registration

```



This turns AI-assisted work from a simple output loop into a learning workflow.



---



## 1. Forward Generation



The workflow moves from intention to output.



```text

Objective

  -> Schema

  -> Example

  -> Validator

  -> Documentation

  -> Changelog

  -> Release

```



Forward generation is useful for creating artifacts, but it can also cause AI systems to move too quickly.



Without later inspection, this can lead to polished but inconsistent output.



---



## 2. Reverse Trace Check



The workflow is then checked backward.



```text

Release

  -> Changelog

  -> Documentation

  -> Validator

  -> Example

  -> Schema

  -> Objective

```



Reverse trace checks whether the final output can be traced back to the original intention without broken links, missing files, or unsupported assumptions.



This is the “思考の巻き戻し” layer.



---



## 3. Verification



Verification checks what actually happened.



Examples:



* Did the referenced file exist?

* Did the validator pass?

* Did the example match the schema?

* Did README match the real repository structure?

* Did CHANGELOG match the actual changes?

* Did the executable recurrence checks pass?

* Did the agent hook require human review before release?

* Did the pre-release gate gather enough evidence before recommending a candidate state?



Verification is the eye of the workflow.



It sees the concrete state before interpretation begins.



---



## 4. Reflection



Reflection identifies why the error happened.



Examples:



* Was repository state assumed before checking?

* Was a validator target added too early?

* Was README updated before file paths were confirmed?

* Was a previous recurrence rule ignored?

* Was an AI agent allowed to proceed without a boundary check?

* Did a validation rule become too broad?

* Did a checker detect its own detector strings?

* Did the burden shift back to the human operator?



Reflection is not about apology.



It is about cause classification.



---



## 5. Rumination



Rumination converts the lesson into a recurrence-prevention rule.



Examples:



* Do not reference a path unless the file tree confirms it.

* Do not add validator targets until schema and example both exist.

* Do not propose workflow edits before confirming workflow existence.

* Do not proceed to release if README and CHANGELOG drift from actual files.

* Do not recommend a candidate tag before validation evidence is available.

* Do not detect malformed Python identifiers by raw substring matching.

* Do not allow the same structural error to recur without a new rule.



Rumination is the digestive layer of the workflow.



It turns mistakes into future guardrails.



---



## 6. Breathing Alignment



Breathing Alignment calms and rebalances the workflow before proceeding.



It asks:



* Is the output too large?

* Is uncertainty visible?

* Has human burden been reduced?

* Is the next step clear?

* Is the workflow lighter than before?

* Should an AI agent pause, warn, block, or ask for human review?

* Should a release gate request more evidence before proceeding?



This prevents reflection from becoming rigid perfectionism.



The purpose is not to punish errors.



The purpose is to restore flow.



---



## 7. Error Visibility



Errors must be visible before they can be digested.



The final check should make the following clear:



* What error occurred?

* Why did it occur?

* What burden did it create?

* What recurrence rule prevents it?

* What uncertainty remains?

* Can this recurrence pattern help another wing?

* Should this recurrence pattern become an AI agent hook?

* Should this recurrence pattern affect pre-release readiness?



Invisible errors cannot become reflection, rumination, recurrence prevention, cross-wing learning, agent-facing execution guidance, or release-readiness evidence.



---



## Rumination Sieve



Structural errors are passed through three sieve layers.



This is the protocol version of 三度目の正直.



```text

Pass 1: Existence Check

Pass 2: Consistency Check

Pass 3: Recurrence Check

```



### Pass 1: Existence Check



Question:



> Do the referenced files and paths actually exist?



Examples:



* Does the schema exist?

* Does the example exist?

* Does the docs path exist?

* Does `.github/workflows` exist before referencing it?



### Pass 2: Consistency Check



Question:



> Do the artifacts align with each other?



Examples:



* Does README match the real file tree?

* Does CHANGELOG match actual changes?

* Does the validator reference existing schema/example pairs?

* Does the example conform to the schema?



### Pass 3: Recurrence Check



Question:



> Is this the same structural error pattern as before?



Examples:



* repeated path mismatch,

* repeated validator target mismatch,

* repeated workflow assumption,

* repeated README/file-tree drift,

* repeated human-side debugging burden,

* repeated YAML / JSON / Python syntax corruption,

* repeated release recommendation before validation evidence,

* repeated over-broad checker logic.



A workflow should not proceed if the same structural error pattern appears again without a new recurrence-prevention rule.



---



## v0.1 — Structural Rumination Record



v0.1 defines the first minimal structure for recording, digesting, and reducing repeated structural errors in AI-assisted specification workflows.



It introduces:



* the core rumination lifecycle,

* the Rumination Sieve,

* final error visibility checks,

* a structural rumination schema,

* a validated example,

* local example validation,

* initial GitHub Actions validation.



In short:



```text

v0.1 = record and digest failure

```



---



## v0.2 — Executable Recurrence Rules



As of v0.2, `structural-rumination-layer` extends Structural Rumination Records into executable recurrence checks.



The project no longer treats implementation failures as passive logs.



Instead, repeated failure patterns are converted into rule definitions and validation scripts that can detect similar errors before release.



In short:



```text

v0.2 = detect recurrence

```



### What v0.2 adds



* `rules/recurrence-rules.yaml`



  * Defines recurrence patterns and executable recurrence rules.

* `scripts/check_recurrence_rules.py`



  * Validates the recurrence rule registry itself.

* `scripts/check_workflow_structure.py`



  * Detects recurring GitHub Actions workflow structure problems.

* `scripts/check_yaml_lists.py`



  * Detects malformed YAML list contamination such as unquoted `*` markers.

* `scripts/check_json_schema_syntax.py`



  * Ensures JSON Schema files are parseable as strict JSON.

* `scripts/check_python_syntax.py`



  * Checks Python scripts for syntax errors and suspicious malformed identifier patterns.

* `docs/executable-recurrence-rules.md`



  * Explains how recurrence patterns become executable validation checks.



These checks are derived from actual implementation failures encountered during the development of this repository, including workflow indentation issues, Python syntax corruption, JSON escaping errors, and YAML list marker contamination.



---



## v0.3 — Multi-Wing Integration



As of v0.3, `structural-rumination-layer` extends recurrence knowledge across multiple wings.



A wing may be:



* a GitHub repository,

* an AI agent,

* a GPT configuration,

* a validator,

* a schema module,

* a documentation layer,

* a governance layer,

* another structural protocol implementation.



The goal is to allow one wing’s failure pattern to become another wing’s prevention signal.



In short:



```text

v0.3 = share recurrence knowledge across wings

```



### What v0.3 adds



* `docs/multi-wing-integration.md`



  * Explains how recurrence knowledge can move across repositories, agents, validators, and structural protocol layers.

* `schemas/multi-wing-rumination-link.schema.json`



  * Defines a schema for linking recurrence patterns and recurrence rules across multiple wings.

* `examples/multi-wing-rumination-link.example.yaml`



  * Provides a validated example of a recurrence rule being adapted from one wing to another.

* `scripts/validate_examples.py`



  * Validates the multi-wing rumination link example.



v0.3 turns local recurrence prevention into cross-wing immune memory.



A failure pattern discovered in one context can now become a structural warning, reference, or adapted check in another.



---



## v0.4 — AI Agent Hooks



As of v0.4, `structural-rumination-layer` connects recurrence knowledge to AI agent execution boundaries.



An AI agent should not only generate outputs.



It should also pass through structural rumination boundaries before, during, and after execution.



These boundaries are called **AI Agent Hooks**.



In short:



```text

v0.4 = hook recurrence knowledge into AI agent execution

```



### What v0.4 adds



* `docs/ai-agent-hooks.md`



  * Explains how recurrence knowledge can be connected to AI agent execution boundaries.

* `schemas/ai-agent-rumination-hook.schema.json`



  * Defines a schema for representing AI agent rumination hooks.

* `examples/ai-agent-rumination-hook.example.yaml`



  * Provides a validated example of a pre-release agent hook.

* `scripts/validate_examples.py`



  * Validates the AI agent rumination hook example.



### Hook types



v0.4 defines the following hook types:



* `pre_run`



  * Runs before an AI agent begins a task.

* `pre_write`



  * Runs before an AI agent writes or modifies files.

* `post_run`



  * Runs after an AI agent completes a task.

* `pre_release`



  * Runs before a tag, release, or candidate state.

* `failure_capture`



  * Runs when an error occurs and may trigger rumination recording.



### Hook actions



An AI Agent Hook may:



* allow execution,

* warn,

* block,

* request validation,

* create a rumination record,

* escalate to human review.



v0.4 turns recurrence knowledge into agent-facing execution guidance.



It is the first step toward structural rumination gates for AI-assisted development.



---



## v0.5 — Pre-release Rumination Gate



As of v0.5, `structural-rumination-layer` combines rumination records, executable recurrence rules, multi-wing links, AI agent hooks, validation evidence, and human review boundaries into a unified pre-release gate.



A repository should not proceed to a candidate release only because files were generated.



Before release, the gate asks:



* Have known recurrence rules passed?

* Have schema/example pairs validated?

* Have README and CHANGELOG been aligned?

* Are multi-wing links structurally valid?

* Are AI agent hooks accounted for?

* Is human review required?

* Has any known structural failure repeated?



In short:



```text

v0.5 = decide release readiness through a rumination gate

```



### What v0.5 adds



* `docs/pre-release-rumination-gate.md`



  * Explains how rumination evidence is gathered before candidate release.

* `schemas/pre-release-rumination-gate.schema.json`



  * Defines a schema for pre-release gate evidence and release-readiness decisions.

* `examples/pre-release-rumination-gate.example.yaml`



  * Provides a validated example of a pre-release gate record.

* `scripts/check_pre_release_gate.py`



  * Runs the unified pre-release rumination gate.

* `.github/workflows/validate-examples.yml`



  * Runs the pre-release gate as part of continuous validation.



### Gate decisions



A Pre-release Rumination Gate may decide:



* `allow_candidate_tag`



  * All required checks passed and no blocking review remains.

* `warn`



  * Minor issues exist, but they do not block the candidate state.

* `request_human_review`



  * The system is structurally ready enough to review, but human confirmation is required.

* `block_release`



  * A known recurrence pattern, failed validation, or unresolved structural inconsistency blocks release.



v0.5 turns structural rumination into release-readiness evidence.



---



## Repository Structure



```text

docs/

  ai-agent-hooks.md

  executable-recurrence-rules.md

  final-error-visibility-check.md

  multi-wing-integration.md

  pre-release-rumination-gate.md

  rumination-sieve-protocol.md



rules/

  recurrence-rules.yaml



schemas/

  ai-agent-rumination-hook.schema.json

  multi-wing-rumination-link.schema.json

  pre-release-rumination-gate.schema.json

  rumination-record.schema.json



examples/

  ai-agent-rumination-hook.example.yaml

  multi-wing-rumination-link.example.yaml

  pre-release-rumination-gate.example.yaml

  rumination-record.example.yaml



scripts/

  check_json_schema_syntax.py

  check_pre_release_gate.py

  check_python_syntax.py

  check_recurrence_rules.py

  check_workflow_structure.py

  check_yaml_lists.py

  validate_examples.py



.github/

  workflows/

    validate-examples.yml



README.md

CHANGELOG.md

```



---



## Key Documents



* [`docs/rumination-sieve-protocol.md`](docs/rumination-sieve-protocol.md)



  * Explains the three-pass structural rumination sieve.

* [`docs/final-error-visibility-check.md`](docs/final-error-visibility-check.md)



  * Defines the final visibility check before moving forward.

* [`docs/executable-recurrence-rules.md`](docs/executable-recurrence-rules.md)



  * Explains how recorded recurrence patterns become executable validation checks.

* [`docs/multi-wing-integration.md`](docs/multi-wing-integration.md)



  * Explains how recurrence knowledge can be shared across wings.

* [`docs/ai-agent-hooks.md`](docs/ai-agent-hooks.md)



  * Explains how recurrence knowledge can be connected to AI agent execution boundaries.

* [`docs/pre-release-rumination-gate.md`](docs/pre-release-rumination-gate.md)



  * Explains how structural rumination evidence becomes pre-release readiness guidance.



---



## Schemas



### Rumination Record



```text

schemas/rumination-record.schema.json

```



Records:



* repository scope,

* forward trace,

* reverse trace,

* verification results,

* reflection root causes,

* rumination rules,

* breathing alignment state,

* visible error summary,

* next-gate conditions.



### Multi-Wing Rumination Link



```text

schemas/multi-wing-rumination-link.schema.json

```



Records:



* source wing,

* target wing,

* link type,

* linked recurrence rule,

* adaptation status,

* validation status,

* human review boundary,

* notes.



### AI Agent Rumination Hook



```text

schemas/ai-agent-rumination-hook.schema.json

```



Records:



* hook ID,

* agent identity,

* hook type,

* execution boundary,

* linked recurrence rules,

* linked rumination records,

* linked multi-wing references,

* hook action,

* validation status,

* human review boundary,

* notes.



### Pre-release Rumination Gate



```text

schemas/pre-release-rumination-gate.schema.json

```



Records:



* gate ID,

* target release,

* repository scope,

* rumination inputs,

* executable checks,

* validation evidence,

* gate checks,

* gate decision,

* human review boundary,

* notes.



---



## Examples



### Rumination Record Example



```text

examples/rumination-record.example.yaml

```



Demonstrates how to record a repeated structural error pattern and convert it into a recurrence-prevention rule.



### Multi-Wing Rumination Link Example



```text

examples/multi-wing-rumination-link.example.yaml

```



Demonstrates how a recurrence rule from one wing can be referenced, adapted, or reused by another wing.



### AI Agent Rumination Hook Example



```text

examples/ai-agent-rumination-hook.example.yaml

```



Demonstrates how recurrence rules and multi-wing rumination links can be connected to an AI agent’s pre-release execution boundary.



### Pre-release Rumination Gate Example



```text

examples/pre-release-rumination-gate.example.yaml

```



Demonstrates how rumination records, recurrence rules, executable checks, multi-wing links, AI agent hooks, validation evidence, and human review boundaries can be gathered before a candidate release.



---



## Validation



Install dependencies:



```bash

pip install jsonschema pyyaml

```



Run the full local validation suite:



```bash

python scripts/check_recurrence_rules.py

python scripts/check_workflow_structure.py

python scripts/check_yaml_lists.py

python scripts/check_json_schema_syntax.py

python scripts/check_python_syntax.py

python scripts/validate_examples.py

python scripts/check_pre_release_gate.py

```



Expected result:



```text

All checks pass.

```



These checks validate:



* the recurrence rule registry,

* GitHub Actions workflow structure,

* YAML list marker hygiene,

* JSON Schema syntax,

* Python syntax and suspicious malformed identifier patterns,

* schema/example consistency,

* multi-wing rumination link examples,

* AI agent rumination hook examples,

* pre-release rumination gate evidence.



---



## Continuous Validation



This repository includes a minimal GitHub Actions workflow for validating confirmed schema/example pairs, recurrence checks, and the pre-release rumination gate.



Workflow:



```text

.github/workflows/validate-examples.yml

```



The workflow runs on:



* push to `main`,

* pull requests targeting `main`,

* manual dispatch.



The workflow intentionally remains small.



It does not attempt to automate release generation, package publishing, broad multi-repository execution, or live AI agent orchestration yet.



Its purpose is narrower:



> Keep the rumination loop executable, visible, stable, agent-aware, and release-gated.



---



## CI Status



The validation workflow has passed after resolving and digesting multiple structural rumination errors.



The first-generation errors digested during this process included:



* GitHub Actions `steps:` indentation drift,

* `setup-python` cache assumptions without dependency files,

* Python double-underscore name corruption during code transfer,

* JSON Schema regex escape errors,

* YAML list syntax corruption caused by Markdown-style `*` bullets.



During v0.5, the pre-release gate also surfaced a checker-level false positive:



* Python syntax checking initially used overly broad raw substring matching,

* the checker detected its own detector strings,

* the check was refined to inspect Python identifier tokens instead of arbitrary text.



These were not treated as isolated fixes.



They were treated as rumination events and converted into recurrence-prevention knowledge.



In short:



> The repository has already begun using its own protocol to digest its own structural errors.



---



## Design Philosophy



This repository rejects binary perfectionism.



It does not say:



> AI must never make mistakes.



Instead, it says:



> Mistakes may occur.

> Repeated structural mistakes must be digested.

> Recurrence rules must become executable.

> Useful failure knowledge should be shareable across wings.

> AI agents should pass through structural rumination boundaries.

> Candidate releases should pass through a rumination gate.

> The workflow must become lighter after each cycle.



The goal is not zero error.



The goal is reduced recurrence, reduced human burden, better structural digestion, safer agent-assisted execution, and clearer release readiness.



---



## Relationship to AI-Assisted GitHub Specification Work



This protocol is intended for workflows involving:



* JSON Schema design,

* YAML examples,

* local validators,

* README updates,

* CHANGELOG updates,

* release notes,

* repository structure validation,

* AI-assisted documentation,

* recurring error pattern management,

* multi-agent or multi-repository specification work,

* AI agent pre-run, pre-write, post-run, pre-release, and failure-capture boundaries,

* candidate release readiness checks.



It is especially useful when AI systems repeatedly produce plausible but structurally inconsistent outputs.



---



## Minimal Scope



This repository intentionally grows in small verified steps.



Current scope:



```text

v0.1 = Structural Rumination Record

v0.2 = Executable Recurrence Rules

v0.3 = Multi-Wing Integration

v0.4 = AI Agent Hooks

v0.5 = Pre-release Rumination Gate

```



Not included yet:



* automated release generation,

* external package publishing,

* full multi-repository orchestration,

* live AI agent runtime integration,

* automatic rule propagation across repositories,

* autonomous blocking of external agent actions,

* fully automated semantic release judgment.



These are deferred to avoid adding structure faster than it can be verified.



---



## Version Position



```text

v0.1 = Structural Rumination Record

       Failure is recorded and digested.



v0.2 = Executable Recurrence Rules

       Failure is converted into executable recurrence checks.



v0.3 = Multi-Wing Integration

       Failure knowledge is linked across structural wings.



v0.4 = AI Agent Hooks

       Recurrence knowledge is connected to AI agent execution boundaries.



v0.5 = Pre-release Rumination Gate

       Rumination evidence is gathered before candidate release.

```



---



## Status



Current version:



```text

v0.5.0-candidate

```



This version introduces the first validated structure for gathering structural rumination evidence before candidate releases.



---



## Summary



Structural Rumination Layer exists to give AI-assisted work a digestive system, an immune response, a cross-wing nervous system, an agent-facing reflex layer, and a pre-release gate.



It helps transform:



```text

Output

  -> Error

  -> Correction

  -> Repeated Error

```



into:



```text

Output

  -> Error Visibility

  -> Reflection

  -> Rumination

  -> Recurrence Prevention

  -> Cross-Wing Learning

  -> Agent Boundary Check

  -> Pre-release Gate

  -> Lighter Workflow

```



In one sentence:



> Verify with the eyes, reflect with the mind, ruminate with the stomach, circulate through the wings, hook into the agent boundary, pass through the release gate, and align the breath before moving forward.