The pilot (Phase 2) is shipped; Phase 3 is now unblocked and ready for Tier 2 dispatch.
5 new files in video_analysis_deob_apply_20260621/:
- spec.md: updated to reference the new files (lightweight scaffold)
- plan.md: 6-phase pipeline (init → read → apply A cluster → apply B cluster → apply C cluster → apply E+D+synthesis → final report + verify) with 25 tasks
- metadata.json: scope, 14 verification criteria, 5-item risk register, 10 user directives
- state.toml: 6 phases + 25 tasks + 10 verification flags + 11 user-directives-logged entries
- TIER2_STARTER.md: dispatch prompt with file-read order, the 2 user refinements (decompress names + operator reference), the 3 pilot process improvements, the 8 refinements + 5 gaps to apply, the 11 inputs (10 videos + 1 synthesis), when-stuck guide, copy-paste-ready block
CRITICAL context for Tier 2 (the 2 user refinements + 3 pilot improvements):
1. **Decompress names AND expressions** (per 2026-06-23): use DESCRIPTIVE names, NOT single letters. Multi-line constructions preferred.
2. **Use the operator reference** (report.md §9): 13 categories of operators with behavior + type signatures. The LLM should consult this when applying the de-obfuscation.
3. **3-column translation tables** (pilot improvement #1)
4. **Tier-categorized decoders** (pilot improvement #2)
5. **Split apply_report.md** into 3 sections (pilot improvement #3)
The 11 inputs: 10 remaining Pass 1 reports + 1 cross-cutting synthesis. Produces 34 deliverables (33 per-video 3-layer files + 1 apply report). This is the FINAL phase of Pass 2 — the result feeds Pass 3 (projection to applied domain, future, user-led).
Per user 2026-06-23 feedback on the pilot output:
1. **Decompress names AND expressions** (in prompt_template.md 'Your role'):
- Name-bound terms should be DESCRIPTIVE, not single letters, unless the single letter is universally obvious (e.g., x for input, f for function)
- Examples: p(X₁, ..., X_L) → language_model(sequence : Token^L) -> Probability : float64
W · h + b → output_projection = weight_matrix.matmul(hidden_state) + bias_vector
H(X) → entropy(distribution : Probability_Distribution) -> Entropy : float64
K(X) → kolmogorov_complexity(object : Object) -> Complexity : int64
- The LLM should NOT be afraid to translate expressions to multi-line definitions or build them up as constructions
2. **§9 Operator reference (indexed)** in report.md (new section):
- 13 categories covering every operator the de-obfuscation uses in practice:
arithmetic, comparison, logical, set-theoretic, type-theoretic, constructors, data-oriented, pipeline, sectors, type-class resolution, process, procedural/functional, why-this-exists
- Each operator: symbol, name, behavior, type signature, example
- Comprehensive expansion of the warmup's §3.3 14-primitive grammar
- The LLM is expected to use this as a reference when applying the de-obfuscation
3. The 'while' operator is explicitly BANNED (per Rule 1) — use 'for', 'iterate', or 'Stream' instead.
These 2 refinements will be propagated forward:
- prompt_template.md 'Your role' updated (the LLM's direct operating stance)
- The §9 operator reference added to report.md (the warmup's design doc; the lexicon's source)
- Phase 3 (apply) TIER2_STARTER will reference both
All 5 phases marked completed; 12 verification flags all true; shipped_commit 8f64127f
User approved 2026-06-23.
Pilot produced 7 deliverables:
- 2 videos × 3 files (translation + deobfuscated + decoder) = 6 files, 1,566 LOC
- pilot_report.md (438 LOC) with 8 refinements + 5 gaps + 3 process improvements
- end-of-track report
All 4 verification criteria met for both videos (Lossless, Bounded, Constructively typed, Etymology-cited)
Plus the 3 additional criteria (Encoding-explicit, Form-anchored, User-specific conventions applied only when appropriate).
Phase 3 (apply) is now unblocked (consumes pilot_report.md refinements).
The lexicon child (Phase 1) is shipped; Phase 2 is now unblocked and ready for Tier 2 dispatch.
5 new files in video_analysis_deob_pilot_20260621/:
- spec.md: updated to reference the new files (lightweight scaffold)
- plan.md: 5-phase pipeline (init → read → apply to cs229 → apply to entropy_epiplexity → refine + verify) with 20 tasks
- metadata.json: scope, 11 verification criteria, 5-item risk register, 9 user directives
- state.toml: 5 phases + 20 tasks + 12 verification flags + 9 user-directives-logged entries
- TIER2_STARTER.md: dispatch prompt with file-read order, the 5 rules + 4 verification criteria, the principled/user-specific distinction context, 2 pilot videos, when-stuck guide, copy-paste-ready block
CRITICAL context for Tier 2: the lexicon (Phase 1) honored the surgical edits:
- 16 [user-also-accepted] tags in lexicon.md
- 4 [principled] + 4 [user-preferred] tags in dedup_map.md
- §3.5 Sectored Language moved to Appendix B
- Esoteric content (Witness/Vessel/Aether) excluded per secular sanitization
Phase 2 must preserve this distinction. The LLM produces the principled re-encoding by default; user-specific form is opt-in. Esoteric content stays in cluster_0_twitter.md only.
The 2 pilot videos: cs229_building_llms (broad-and-shallow) + entropy_epiplexity (narrow-and-deep, tests boundedness on measure theory).
Scaffolds the Phase 1 (lexicon) child track with full Tier 2 dispatch support, matching the warmup's pattern.
- plan.md: 5-phase pipeline (init → read warmup → refine → codify → user review → verify) with 22 tasks
- metadata.json: scope, verification criteria, 6-item risk register, 9 user directives
- state.toml: 5 phases + 22 tasks + 12 verification flags + 10 user-directives-logged entries
- TIER2_STARTER.md: dispatch prompt with file-read order, 10 critical user directives, 6 key risks, hard constraints, sandbox conventions, 14 verification criteria, 5-phase execution plan, when-stuck guide, copy-paste-ready dispatch prompt
CRITICAL context for Tier 2: the warmup's 2026-06-23 surgical edits distinguished principled re-encodings (from the 5 rules) from user-specific re-encodings (Sectored Language, GA, classical Greek/Latin). Phase 1 FORMALIZES this distinction; it does NOT undo it.
- Tag each user-specific entry with [user-also-accepted]
- Move §3.5 (Sectored Language operator terms) to Appendix B
- DO NOT re-include esoteric content (Witness/Vessel/Aether) in the public lexicon
- DO NOT re-survey the samples; the cluster sub-reports are the evidence base
Per user 2026-06-23 review: the Tier 2 over-cited the user's specific implementations (Sectored Language V1, LLM session patterns, GA reinterpretations, classical Greek/Latin) as the canonical scheme, when they should be optional output conventions.
Changes:
1. report.md §3.4 — added Reading guide: Tier 4 mixes principled re-encodings (from the 5 rules) with user-specific re-encodings (from samples). The principled forms are scheme-canonical; the user-specific are optional output conventions.
2. report.md §3.5 — added Reading guide: Sectored Language operator terms are USER preferences, not scheme-canonical. The scheme produces principled re-encodings; the Sectored Language is one way to express them.
3. report.md §4.4 — added Reading guide: 'Real = Imaginary = Bivector' is the user's GA reinterpretation, not a scheme-canonical dedup. The principled forms are bivector (with grade annotation) + quantity(<value>) : <encoding>.
4. report.md §6.2 — added Reading guide: 4-layer output format is OPTIONAL (the user's preferred convention for etymological trails). The scheme's baseline is the 3-layer format.
5. prompt_template.md 'Your role' — removed 'Construct, not Invent' (was a user preference, not scheme-canonical). Added a 'Scheme-canonical vs. user-specific' bullet that makes the distinction explicit.
6. prompt_template.md 'The Sectored Language Operator Names' — labeled OPTIONAL; added Reading guide explaining it's one of several ways to express the scheme's principled re-encodings.
7. prompt_template.md verification checklist — replaced 'Sectored-language-named' with 'User-specific conventions applied only when appropriate'.
Phase 1 (lexicon child) will formalize this distinction further (e.g., moving §3.5 to Appendix B, marking each user-specific entry with [user-also-accepted]). The principled spine (5 rules + 6 noise-dedup maps + form-anchor examples + etymology rule + lossless preservation) is intact.
- tracks.md: new row 29 for the de-obfuscation campaign (priority A, research, awaits user samples)
- Pass 1 spec §11.1: superseded 2026-06-21; now points to the dedicated Pass 2 umbrella spec for the full handoff contract. The 'user must rediscover math encoding' action item is replaced by 'user provides 3-10 samples of past de-obfuscation notes; warmup derives the lexicon'
The MVP brute-force on code_path_audit_20260607 produced a working
AUDIT_REPORT.md (6797 lines, real per-aggregate numbers) but left:
1. 2 in-scope failing audit gates (weak_types regression of 5;
generate_type_registry --check drift).
2. 3 carry-over code smells (duplicate import json; dead DSL parser
with arity bugs; dead compute_result_coverage).
3. No behavioral test for the headline SSDL number (4.01e22).
4. Stale state.toml + tracks.md + spec_v2.md claiming v2 DSL shipped.
This track addresses all 4: 5 phases, 12 tasks, 12 atomic commits.
Out of scope (documented in metadata.json::known_issues): the 4
pre-existing exception-handling violations in other files; the 7
pre-existing Optional[T] violations in mcp_client.py/ai_client.py;
the 7-file split refactor.
Proposals analyzed:
- A (this): tight audit-gate cleanup, 30-60 min, 5 atomic commits.
- B: A + 7->1 refactor. Rejected: user said small.
- C: A + B + cross-cutting convention fixes. Rejected: crosses into
other tracks' territory.
MVP pipeline simplification:
- render_rollups() now produces ONLY summary.md + AUDIT_REPORT.md
- run_audit() now produces only per-aggregate .md (no .dsl/.tree)
- New src/code_path_audit_gen.py generates the single coherent report
Stale artifacts moved to _stale/ subdirectory (preserved for history):
- 13 per-aggregate .dsl files (redundant with .md)
- 13 per-aggregate .tree files (redundant with .md)
- 9 old top-level rollups (cross_audit_summary, decomposition_matrix,
candidates, field_usage, call_graph, hot_paths, dead_fields,
ssdl_analysis, organization_deductions - all superseded by sections
inlined in AUDIT_REPORT.md)
- _stale/README.md explains what happened
Meta-audit updated to check .md files (14 required H2 sections per
aggregate) instead of .dsl files. 0 violations on 10 real profiles.
Tests: 131 passing. New MVP report: 5000+ lines.
Three real bugs fixed:
1. FunctionRef always used line=0. Now passes node.lineno from AST.
2. P3_pass results were discarded with bare pass. Now stored in
ProducerConsumerGraph.field_accesses.
3. Field-access detector only saw entry['key']; missed entry.get('key')
which is the dominant pattern in this codebase. Now handles both.
Plus _extract_type_name() helper handles Optional[T], dict[str, T],
list[T], Result[T], Union[T, ...], and T | None (PEP 604) so P1/P2
catch more annotation patterns.
Real numbers (Metadata aggregate):
- producers: 77 -> 117
- consumers: 35 -> 66
- field-access sites: 130 -> 173
- line numbers: all real (line 1281, 1746, etc.)
AUDIT_REPORT.md grew 2009 -> 3140 lines with real evidence.
Total audit output: 5176 lines / 50 files (was 2415 / 49).
All 131 tests still passing.
The 272-line report was a summary, not a report. The user wanted
the actual evidence inlined. This version embeds:
- Full per-aggregate .md profiles (15 sections each)
- Full SSDL analysis rollup
- Full organization deductions
- Full call graph
- Full hot paths
- Full field usage
- Full decomposition matrix
- Full cross-audit summary
- Full dead fields
- Full candidates
- Full top-level summary
Total: 2009 lines. The user can read it as a single document or
grep for specific aggregates/sections.
The audit output is a database dump (49 files, 3 redundant formats
each). The user wanted ONE thing they can read. This is the
narrative version: 1 file that opens with the verdict, walks
through findings by severity, gives the Metadata deep dive, and
ends with prioritized restructuring routes.
Original 49 files (10 top-level rollups + 13 aggregates x 3 formats)
preserved as supporting detail. See Section 10 'See Also' for
the full artifact inventory.
Replaces passive 'what we shipped' framing with active 'what the
audit tells us about the codebase organization' deductions.
Headline finding: 0 of 10 real aggregates are well-organized.
Metadata aggregate has 1.13e18 effective codepaths (2^251 from
251 branch points across 35 consumers), 6 nil-check functions,
and 0% field-access efficiency. Three concrete refactor routes:
nil sentinel [N], generational handles, immediate-mode cache.
Replaces the prior TRACK_COMPLETION (which was written before the
real-data analyzers landed). Documents the 4 new analyzer modules,
the 2136-line output report, the per-aggregate table with real
producer/consumer counts, the audit gates status, the known
gaps, and the 5 follow-up tracks.
Total report now exceeds the 2k-line threshold the user asked
for (2136 lines of audit content + this 200-line summary).
The previous code did Path(src_dir) / function_ref.file, which
double-prefixed (e.g. src/src/project_manager.py) and silently
returned empty. Fixed: if function_ref.file exists as
CWD-relative, use it directly. Only join if it doesn't exist.
Now 130 real field accesses detected across 35 Metadata consumers
in the 2026-06-22 audit output (was 0 before).
The aggregate_findings function now does 3-tier mapping:
1. Function lookup (find_enclosing_function) -> exact match
2. File-level fallback: if the finding's file has any
producer/consumer of the aggregate, bucket it there
3. Unbucketed (the file has no aggregate refs)
Handles both 'file' and 'filename' keys (v1 audit scripts use
'filename'; spec fixtures use 'file'). Path normalization
for Windows paths.
Generated the 6 real audit_inputs from scripts/audit_*.py
against real src/. The Metadata aggregate now shows:
- 1 unique weak_types finding (1 site, from ai_client.py:159)
- 1 unique exception_handling finding (76 sites from PARAM_OPTIONAL)
mcp_client.py shows 0 because no Metadata producer/consumer
exists in the PCG for mcp_client (P1/P2 only detect typed
parameter signatures, not internal field access). The next
gap is expanding P3 to capture internal field use.
Loops over audit_weak_types + audit_exception_handling from
the 6 audit_inputs, calls aggregate_cross_audit_findings per
audit, sums the buckets per profile.
Cross-audit aggregation is per-aggregate-flat (all findings go
into 1 bucket per audit). The 3-tier finding-to-aggregate
mapping (find_enclosing_function + type registry + file
heuristic) is the next gap - requires per-finding site
classification.
Previous version checked for field names (weak_types, etc.)
in DSL content. That's wrong - those are bucket names that
only appear when there are findings. New version just checks
the 14 required section markers + the cross-audit-findings
count line. Skips candidate aggregates.
Meta-audit now passes clean on the 2026-06-22 audit output.
The Optional[T] ban enforcement script. Was referenced in the
v2 audit's INPUT_JSON_CONTRACTS as a fixture input but the
script itself was never committed (the v1 spec assumed it
existed on master; it didn't). This commit CREATES the
script from scratch per the v2 audit's contract.
Baseline files (4 total):
- src/mcp_client.py (refactored 2026-06-06)
- src/ai_client.py (refactored 2026-06-06)
- src/rag_engine.py (refactored 2026-06-06)
- src/code_path_audit.py (this track; v2 audit) <- NEW 4th file
The audit AST-scans function signatures for Optional[X] usage:
- RETURN_OPTIONAL: strict violation (forbidden by error_handling.md)
- PARAM_OPTIONAL: warning (informational only)
Current state: 7 return-type Optional[T] violations in
mcp_client.py + ai_client.py (pre-existing from the v1
refactor; NOT introduced by code_path_audit.py). My new
file passes clean.
--strict mode exits 1 on any RETURN_OPTIONAL violation.
Default mode prints the report and exits 0.
ed