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Coevolution workplan extension

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Bernd Worsch
2026-04-29 00:40:02 +02:00
parent c070951c68
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workplans/RREG-WP-0003-automatic-repository-exploration.md
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@@ -8,7 +8,7 @@ status: active
owner: codex
topic_slug: foerster-capabilities
created: "2026-04-26"
updated: "2026-04-28"
updated: "2026-04-29"
state_hub_workstream_id: "c121d462-f2e4-45d3-9d2d-9c04a3556953"
---
@@ -102,7 +102,7 @@ export.
```task
id: RREG-WP-0003-T04
status: todo
status: done
priority: medium
state_hub_task_id: "076385fe-4dbf-4aca-b89f-c7372d9eebd9"
```
@@ -131,3 +131,65 @@ that produce only weak candidates.
Acceptance: trying the product on repo-registry itself feels understandable and
useful even when a scan finds gaps or weak evidence.
## P1: Expectation Gap Feedback Loop
```task
id: RREG-WP-0003-T07
status: todo
priority: medium
```
Capture the gap between what a curator expected to see and what deterministic
analysis actually produced. Treat these gaps as first-class scanner optimization
inputs: a user should be able to record missing expected abilities, capabilities,
features, facts, or classifications for an analyzed repository. The system should
preserve the source of the expectation (`human`, `llm-assisted`, or `comparison`)
and link it to the analysis run that missed it.
Acceptance: after inspecting a repository such as `llm-connect`, a curator can
record that expected concepts like `OpenRouter provider support`, `Claude model
usage`, or `provider fallback policy` were missing. The gap is visible from the
repository/review UI and can be used to create deterministic scanner regression
fixtures.
## P1: Provider-Aware Deterministic Scanning
```task
id: RREG-WP-0003-T08
status: todo
priority: medium
```
Extend deterministic scanning and content indexing to identify provider and
integration concepts that generic language/framework/file facts miss. Initial
targets are LLM infrastructure repositories: OpenRouter, Anthropic/Claude,
OpenAI, Gemini, model-provider registries, credential environment variables,
adapter classes, routing rules, and fallback policies. These should appear as
source-linked facts and map into useful candidate capabilities/features without
requiring LLM assistance.
Acceptance: analyzing `llm-connect` with LLM assistance disabled can surface
source-linked facts and candidate graph entries for OpenRouter support, Claude or
Anthropic support where present, provider configuration/credentials, and any
explicit model fallback behavior found in code, docs, or config.
## P1: Scanner Coevolution Regression Harness
```task
id: RREG-WP-0003-T09
status: todo
priority: medium
```
Create a repeatable improvement loop where reviewed expectation gaps become
fixtures and tests. For each trial repository, store a small expectation profile
that lists important concepts the deterministic scanner should eventually detect.
Compare deterministic outputs against optional LLM-assisted or human-curated
expectations, then promote confirmed misses into scanner/candidate-generator
regression tests.
Acceptance: the repository has at least one expectation fixture for an LLM
infrastructure repo and a test that fails if deterministic analysis stops
surfacing expected provider concepts. The workflow remains LLM-optional: LLMs may
suggest expectations, but deterministic tests encode the accepted learning.