adversarial-baseline-audit
Skill0 starsChanged 2 months agoMITView on GitHub
Challenge promoted reverse-engineering baseline claims by trying to disprove them, detect over-generalization, stale evidence, test-as-runtime mistakes, and persistent hallucination before high-impact migration or design work.
In this repository33 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- architecture-drift-detectionSkill
- architecture-reconstruction-and-diagramsSkill
- artifact-detailed-designSkill
- baseline-source-of-truth-maintenanceSkill
- claim-verification-and-promotionSkill
- configuration-source-traceSkill
- contract-impact-test-designSkill
- current-state-tdd-synthesisSkill
- design-artifact-decompositionSkill
- design-artifact-decomposition-verificationSkill
- detailed-design-verificationSkill
- full-reverse-engineeringSkill
- implementation-readiness-certificationSkill
- incident-root-cause-analysisSkill
- integration-selection-analysisSkill
- observability-traceability-analysisSkill
- patch-impact-reviewSkill
- persistence-schema-reverse-engineeringSkill
- post-readiness-adversarial-auditSkill
- regression-test-designSkill
- requirement-compliance-reviewSkill
- resolve-high-impact-unknownsSkill
- resume-reverse-engineeringSkill
- reverse-engineering-bootstrapSkill
- reverse-engineering-coverage-auditSkill
- run-engineering-workflowSkill
- runtime-binding-verificationSkill
- runtime-flow-analysisSkill
- technical-design-proposalSkill
- technical-design-verificationSkill
- wbs-generationSkill
- wbs-verificationSkill
The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 8 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Do not try to confirm it. Try to break it.
- Distinctive: line 51
If a no-impact claim exists, were all source-visible invocation sites closed?
- Distinctive: line 61
verify the condition independently per surface/operation
- Distinctive: line 62
search for sibling operations that share code but have different requirements
- Distinctive: line 116
all 5 mandatory diagram artifacts exist and contain Mermaid.
- Distinctive: line 150
Do not silently rewrite.
- Distinctive: line 154
remove canonical claim only with explicit evidence note
- Distinctive: line 156
add corrected candidate claim
- Very specific: line 161
Before declaring a high-impact promoted claim HOLDS:
- Very specific: line 176
If an applicable item is unresolved, do not use HOLDS.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Adversarial Baseline Audit — 4.0
Mission
Assume the canonical baseline may contain mistakes.
Do not try to confirm it. Try to break it.
Input
Review:
00_current_understanding.md00_evidence_ledger.md00_master_decision_matrix.md
Select high-impact PROMOTED claims.
Prioritize:
- routing/client selection
- protocol/external boundary
- business behavior
- compatibility/error rules
- externally observable field/data behavior
- claims used by migration/design
- claims reused by patch/compliance/test-design work
Attack Each Claim
Ask:
- Is scope broader than evidence?
- Was a condition proven for one endpoint/operation generalized to another?
- Is evidence stale against current commit?
- Was a test mistaken for production wiring?
- Are there alternate implementations or alternate caller paths?
- Are there hidden config branches?
- Is fallback missing?
- Does a different operation behave differently?
- Does target/model/version/mode change behavior?
- Is there an inactive/legacy path confused with active?
- Does current source contradict the memory statement?
- For an externally observable field/data claim, was full provenance actually proven?
- Could the claimed conditional output be implemented earlier by conditional retrieval/mapping?
- Was one representative external entry point mistaken for complete exposure?
- If a no-impact claim exists, were all source-visible invocation sites closed?
Mandatory Attack Dimensions
Scope Leakage Attack
For claims that mention conditions, modes, parameters, or endpoint-specific behavior:
- reconstruct a compact scope matrix
- verify the condition independently per surface/operation
- search for sibling operations that share code but have different requirements
- downgrade any claim whose wording is broader than the proven scope
Field / Data Provenance Attack
For claims about output fields, persisted values, messages, identifiers, or conditional data:
trigger
→ retrieval/source
→ parse/decode
→ transform/normalize
→ mapping/storage
→ serialization/output
→ observable resultAttack every link.
A missing explicit serializer/output condition is NOT evidence of a gap if earlier conditional retrieval or mapping already guarantees the behavior.
Boundary → Exposure Closure Attack
For claims that a downstream boundary is or is not externally reachable:
impacted / important boundary
← every invocation/construction site
← every caller chain
← every external entry point or proven internal rootDo not accept:
- one representative REST endpoint as complete exposure
NO_DIRECT_*_IMPACTwhile an invocation site remains unresolved- a downstream CLI integration being mislabeled as the product's own CLI entry point
Premature Verdict Attack
Attack claims that appear to have been promoted before:
- full runtime/control-flow trace
- field/data provenance where applicable
- alternate-path search
- exposure closure where applicable
- current-source reverification
Consumer Usability Attack
Attack the generated current-state model, not only the factual claims.
Verify:
CURRENT_STATE_TDD.mdis a technical system model rather than an audit dump.- all 5 mandatory diagram artifacts exist and contain Mermaid.
- diagrams show real verified relationships rather than generic boxes.
- major subsystem responsibilities and dependencies are explicit.
- primary runtime behavior is understandable without reading the whole repository.
- external boundaries and runtime selection are visible.
- state/persistence/lifecycle ownership is visible.
- material diagram nodes/edges have evidence anchors.
- large inventories/schema dumps are supporting artifacts rather than the main architecture narrative.
Run:
python .ai-engineering/tools/validate-reverse-engineering-quality.py --artifact-root docs/reverse-engineeringA validator failure prohibits BASELINE_READY.
Audit Matrix
| Evidence ID | Promoted Claim | Scope Attack | Provenance Attack | Exposure Attack | Counterevidence | Verdict | Action |
Verdict:
- HOLDS
- OVER-GENERALIZED
- PROVENANCE_INCOMPLETE
- EXPOSURE_INCOMPLETE
- STALE
- CONTRADICTED
- INSUFFICIENTLY_VERIFIED
- UNKNOWN
Action
Do not silently rewrite.
If contradicted:
- mark evidence REJECTED or SUPERSEDED
- remove canonical claim only with explicit evidence note
- preserve history in ledger
- add corrected candidate claim
- require re-promotion
Completion Gate
Before declaring a high-impact promoted claim HOLDS:
[ ] claim scope matches evidence scope
[ ] current source still supports the claim
[ ] alternate implementations/caller paths were searched
[ ] field/data provenance is complete when the claim is data/output-related
[ ] boundary/exposure closure is complete when reachability is claimed
[ ] no sibling operation contradicts the generalized rule
[ ] tests were not substituted for production wiring
[ ] consumer-quality validator passes
[ ] mandatory diagram set is complete
[ ] CURRENT_STATE_TDD is the canonical consumer baselineIf an applicable item is unresolved, do not use HOLDS.
Output
docs/reverse-engineering/audits/<date-or-baseline>-adversarial-audit.md
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