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feat: add cheatsheet_knowledge_queue_item adapter and Librarian dry-run - #1134
This PR adds a Cheat Sheet → knowledge_queue adapter and an end-to-end Librarian dry-run path, allowing extracted CheatsheetRecord objects to be emitted in the Module B knowledge_queue JSONL format and exercised through the Module C pipeline.
Internally uses the existing CheatsheetRecord → Section adapter.
Uses SourceType.url / LocatorKind.url for Cheat Sheet sources and locators.
Returns a KnowledgeQueueItem.
Fields such as llm_label, confidence, and llm_reasoning are supplied explicitly/defaulted because these are Module B / classifier-specific fields and cannot be meaningfully derived from a CheatsheetRecord.
llm_label defaults to KNOWLEDGE and confidence defaults to 1.0
because a CheatsheetRecord represents a known security-knowledge source.
llm_reasoning is defaulted as null because CheatsheetRecord do not have reasoning
from the Module B LLM classifier.
source_repo and source_commit_sha are null because Cheat Sheets use URL-based provenance rather than GitHub based identifiers; post_guid is null because they are not RSS feed items; span_index=0 and span_total=1 because each CheatsheetRecord is emitted as a single span.
Adds write_knowledge_queue_jsonl(), which converts CheatsheetRecord objects into KnowledgeQueueItem rows and writes them as JSONL. This provides the queue representation that can later be supplied to the Librarian when running against a live hub.
2. cheatsheet_knowledge_queue_item_test.py
Adds coverage for the adapter and queue boundary:
Happy path — verifies a valid CheatsheetRecord produces a KnowledgeQueueItem.
Empty headings — ensures an empty heading list does not accidentally become the JSON string "[]" in span_heading_path.
C.0 validation — verifies the generated queue item is accepted by the Librarian's C.0 section_from_queue_row() boundary.
JSONL / FixtureKnowledgeSource integration — verifies the generated JSONL can be consumed by the Librarian fixture knowledge source used by the dry-run.
The Librarian fixture set has also been updated with:
Uses the existing cheatsheet_dry_run.py infrastructure for the C.1/C.2 pipeline setup.
Uses deterministic C.3 confidence calibration (0.95) to exercise the intended decision path without requiring the full production calibration setup.
Uses the real Librarian pipeline and C.4 decision logic.
Verifies that the generated Cheat Sheet knowledge-queue rows survive the complete C.0 → C.4 path without being rejected at any stage.
The dry-run keeps the computationally heavy/production-dependent parts lightweight while exercising the actual Librarian pipeline interfaces and integration path.
Testing
Confirmed locally — adapter tests passed and the dry run executed successfully
Notes
Source/locator representation: Currently, module_c_contract.md documents only the github and rss source paths. However, this adapter uses source_type=SourceType.url / locator_kind=LocatorKind.url for Cheat Sheet rows, since a Cheat Sheet is currently identified by a webpage URL rather than a GitHub repository path or RSS feed entry for provenance. These are valid members of the existing SourceType / LocatorKind enums and have been verified to pass the Module C dry run.
feed_url representation: The adapter currently sets feed_url=str(section.locator.url). While feed_url is documented as RSS-specific in module_c_contract.md, the current C.0 validation and source reconstruction do not prohibit its use for source_type="url".
Flagging this for confirmation and recommending that the contract be updated accordingly so future contributors have an explicit reference.
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Summary by CodeRabbit
New Features
Cheat sheet content can be converted into knowledge queue entries with source details, section headings, and content metadata.
Entries can be saved as JSONL and run through the knowledge pipeline in a dry run, with a summary of processing results.
Tests
Added coverage for field mapping, missing headings, validation, and reading generated queue data.
Walkthrough
The pull request adds conversion from CheatsheetRecord to KnowledgeQueueItem, JSONL writing, tests and fixture records, and a script that runs the Librarian pipeline against the generated queue. It also changes the regression workflow’s Python setup version to 3.12.
Changes
Cheatsheet Knowledge Queue
Layer / File(s)
Summary
Queue item conversion and validation application/utils/external_project_parsers/parsers/cheatsheet_knowledge_queue_item.py, application/tests/cheatsheet_knowledge_queue_item_test.py, application/tests/librarian/fixtures/sample_cheatsheets_knowledge_queue.jsonl
The converter maps record content, identifiers, provenance, locator, headings, and classification fields to queue items. The writer outputs JSONL records in input order. Tests cover field mappings, empty headings, C.0 validation, and fixture-source reading. The fixture contains three cheat sheet queue records.
The script extracts records from fixtures, fills missing timestamps, skips extraction failures, writes a JSONL queue, and runs the Librarian pipeline with a fixture source, fixed-confidence scaler, and null safety guard. It reports pipeline results and returns a failure status when extraction failures, dropped rows, skips, or errors occur. The regression workflow requests Python 3.12 instead of 3.12.3.
OWASP/OpenCRE#1120: Changes source-row processing, retrieval, and ranking behavior in the LibrarianPipeline used by this pull request.
OWASP/OpenCRE#1088: Changes retrieval, safety evaluation, and decision behavior in the pipeline that processes generated cheat sheet rows.
Merge Risk: Minimal · previous review 595d9
No merge-blocking issue was established. Strengthening the JSONL round-trip test remains a useful follow-up.
Pre-merge checks: 5
Check name
Status
Explanation
Title check
The title clearly identifies the adapter and Librarian dry-run as the main changes.
Description check
The description covers the adapter, tests, fixture, and Librarian dry-run.
Docstring Coverage
Docstring coverage is inconclusive: files from earlier reviews still need to be rechecked within the check's analysis limits. Not blocking.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
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.github/workflows/librarian_regression.yml
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Assert that the JSONL reader returns the written row.
FixtureKnowledgeSource.items() skips malformed rows. If it rejects the generated row, list(...) returns an empty list and this test still passes. Assert that the reader returns one item with the written item’s id. This makes the test detect a broken serialization boundary.
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Review comment at @application/tests/cheatsheet_knowledge_queue_item_test.py at
line 118:
Update the test around FixtureKnowledgeSource.items() to assert that it returns
exactly one item and that the item’s id matches the written item’s id. Do not
rely on converting the reader output to a list without checking its contents.
🪄 Fix CodeRabbit comments on this PR
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @scripts/cheatsheet_knowledge_dry_run.py:
- Line 182: Before the successful return in the dry-run flow, compare
result.stats.total with the number of written items and check
LibrarianPipeline.run()’s skipped and errored counts; return a failure status if
rows were skipped or errored, or if the pipeline total differs from the
written-item count.
---
Nitpick comments:
Review comments at @application/tests/cheatsheet_knowledge_queue_item_test.py:
- Line 118: Update the test around FixtureKnowledgeSource.items() to assert that
it returns exactly one item and that the item’s id matches the written item’s
id. Do not rely on converting the reader output to a list without checking its
contents.
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remove emojis ... and write the code in standard format
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Summary
This PR adds a Cheat Sheet →
knowledge_queueadapter and an end-to-end Librarian dry-run path, allowing extractedCheatsheetRecordobjects to be emitted in the Module Bknowledge_queueJSONL format and exercised through the Module C pipeline.ADDED
1.
cheatsheet_knowledge_queue_item.pyAdds
knowledge_queue_item_from_cheatsheet_record().CheatsheetRecordas input.CheatsheetRecord → Sectionadapter.SourceType.url/LocatorKind.urlfor Cheat Sheet sources and locators.KnowledgeQueueItem.llm_label,confidence, andllm_reasoningare supplied explicitly/defaulted because these are Module B / classifier-specific fields and cannot be meaningfully derived from aCheatsheetRecord.llm_labeldefaults toKNOWLEDGEandconfidencedefaults to1.0because a
CheatsheetRecordrepresents a known security-knowledge source.llm_reasoningis defaulted as null becauseCheatsheetRecorddo not have reasoningfrom the Module B LLM classifier.
source_repoandsource_commit_shaarenullbecause Cheat Sheets use URL-based provenance rather than GitHub based identifiers;post_guidisnullbecause they are not RSS feed items;span_index=0andspan_total=1because eachCheatsheetRecordis emitted as a single span.Adds
write_knowledge_queue_jsonl(), which convertsCheatsheetRecordobjects intoKnowledgeQueueItemrows and writes them as JSONL. This provides the queue representation that can later be supplied to the Librarian when running against a live hub.2.
cheatsheet_knowledge_queue_item_test.pyAdds coverage for the adapter and queue boundary:
CheatsheetRecordproduces aKnowledgeQueueItem."[]"inspan_heading_path.section_from_queue_row()boundary.The Librarian fixture set has also been updated with:
sample_cheatsheets_knowledge_queue.jsonl3.
cheatsheet_knowledge_dry_run.pyAdds an end-to-end dry-run covering:
Cheat Sheet → CheatsheetRecord → KnowledgeQueueItem / JSONL → FixtureKnowledgeSource → C.0 → C.1 → C.2 → C.3 → C.4cheatsheet_dry_run.pyinfrastructure for the C.1/C.2 pipeline setup.0.95) to exercise the intended decision path without requiring the full production calibration setup.The dry-run keeps the computationally heavy/production-dependent parts lightweight while exercising the actual Librarian pipeline interfaces and integration path.
Testing
Notes
Source/locator representation: Currently,
module_c_contract.mddocuments only thegithubandrsssource paths. However, this adapter usessource_type=SourceType.url/locator_kind=LocatorKind.urlfor Cheat Sheet rows, since a Cheat Sheet is currently identified by a webpage URL rather than a GitHub repository path or RSS feed entry for provenance. These are valid members of the existingSourceType/LocatorKindenums and have been verified to pass the Module C dry run.feed_urlrepresentation: The adapter currently setsfeed_url=str(section.locator.url). Whilefeed_urlis documented as RSS-specific inmodule_c_contract.md, the current C.0 validation and source reconstruction do not prohibit its use forsource_type="url".Flagging this for confirmation and recommending that the contract be updated accordingly so future contributors have an explicit reference.