Code Audit Fanout

code-audit-fanout

When to use

USE for any code review request where correctness matters more than speed. Especially:

• ML training loops, loss functions, custom layers
• Signal-processing pipelines (FFT, filters, modulation)
• 5G PHY simulators (channel models, MIMO precoding, decoders)
• Any function touching complex-valued tensors, dB scales, or hardware-determinism primitives
• Diffs > 50 lines before merge

SKIP for trivial edits (typo fix, rename, formatting).

The 5 specialist agents

Each spawned in parallel in ONE assistant message. Each has a narrow scope, a required source list, and a citation contract.

Specialist 1 — static-analyzer

Scope: Read-only static analysis. Tools: Read, Grep, Glob, Bash Checks:

• ruff check --select ALL output
• mypy --strict output (if config present, else mypy)
• pyright if available
• Function-level cyclomatic complexity > 10
• Bare except:, except Exception:, swallowed errors
• Mutable default args
• Print statements left in code (vs logging) Citation requirement: every flagged rule must reference the exact ruff / mypy / pyright rule ID and link to the rule docs.

Specialist 2 — library-api-auditor

Scope: Verify every imported API call against official docs. Tools: Read, Grep, WebFetch, WebSearch Required sources (in order of precedence):

• PyTorch — https://docs.pytorch.org/docs/stable/
• NumPy — https://numpy.org/doc/stable/reference/
• SciPy — https://docs.scipy.org/doc/scipy/
• JAX — https://docs.jax.dev/en/latest/
• Sionna — https://nvlabs.github.io/sionna/
• HuggingFace — https://huggingface.co/docs/
• pandas — https://pandas.pydata.org/docs/ Procedure:

1. Extract every imported symbol and every X.Y.Z(...) call.
2. For each, WebFetch the official doc page.
3. Check: function exists? signature matches? deprecated? known gotchas in the docs (the "Note" / "Warning" boxes)?
4. Flag mismatches with: file:line — call — expected signature per docs URL — quoted doc passage. Citation requirement: every finding cites the exact doc URL

• section anchor + a verbatim quote. HARD RULE: if a fetch fails or the page doesn't mention the symbol, mark UNVERIFIED. Do NOT guess.

Specialist 3 — standards-spec-auditor

Scope: For wireless / signal-processing code only. Verify the code's constants and procedures against published standards. Tools: Read, WebFetch, WebSearch Required sources:

• 3GPP TS portal — https://www.3gpp.org/specifications-technologies
• ETSI standards — https://www.etsi.org/standards
• IEEE Xplore (citation only — no full-text fetch unless user provides access)
• IETF RFCs — https://www.rfc-editor.org/
• ITU-R recommendations — https://www.itu.int/rec/R-REC/en Checks:
• Numerology constants (subcarrier spacing 15/30/60/120/240/480 kHz)
• Resource grid dimensions (PRB = 12 subcarriers, slot = 14 symbols)
• LDPC base graph selection thresholds (TS 38.212)
• Modulation mapping tables (TS 38.211 §5.1)
• Channel-model parameters (TR 38.901 tables)
• TBS / MCS index tables (TS 38.214) Citation requirement: every numeric constant or procedure verified must cite TS/TR number + clause + table + quoted line. HARD RULE: if the user's code is not wireless, this specialist returns "NOT APPLICABLE" and does not invent issues.

Specialist 4 — reproducibility-auditor

Scope: Ensure the code will produce the same result on a second run. Tools: Read, Grep, WebFetch Required sources:

• https://docs.pytorch.org/docs/stable/notes/randomness.html
• https://docs.jax.dev/en/latest/jep/263-prng.html
• https://numpy.org/doc/stable/reference/random/index.html Checks (cite the relevant doc section for each):
• All seeds set: random.seed, np.random.seed, torch.manual_seed, torch.cuda.manual_seed_all
• Env vars: PYTHONHASHSEED, CUBLAS_WORKSPACE_CONFIG=:4096:8
• torch.use_deterministic_algorithms(True) present
• torch.backends.cudnn.deterministic = True and benchmark = False
• DataLoader: worker_init_fn + generator=torch.Generator() set
• JAX: explicit PRNGKey threading; no global jax.random calls
• Non-deterministic ops used without acknowledgment: scatter_add_, index_add_, bincount, embedding-bag backward, CTC loss, pooling backwards
• Resumed-from-checkpoint code: re-seeds AND saves RNG state Citation requirement: each finding cites the exact PyTorch / JAX / NumPy doc URL + the warning passage that justifies the rule.

Specialist 5 — numerical-correctness-auditor

Scope: Catch silent numerical bugs. Tools: Read, Grep, WebFetch Required sources:

• https://numpy.org/doc/stable/reference/generated/numpy.fft.fft.html
• https://docs.pytorch.org/docs/stable/generated/torch.fft.fft.html
• https://nvlabs.github.io/sionna/ (for complex-tensor conventions)
• Any user-supplied papers/notes the code claims to implement Checks:
• Complex dtypes preserved end-to-end (no silent cast to real)
• FFT normalization explicit (norm="ortho" vs "backward" vs "forward")
• dB ↔ linear conversions named (lin2db, db2lin); no bare 10*log10 or 10**(x/10)
• Sample-rate / FFT-size consistency across the pipeline
• Tensor-shape order matches the library convention (Sionna: [batch, rx, tx, subcarrier, symbol])
• In-place ops on autograd tensors (breaks backward)
• Mixed precision: explicit autocast scope, gradient scaler used
• Numerical-stability tricks: log-sum-exp, log1p, expm1 where appropriate Citation requirement: every claim about a library behavior cites the docs URL + quoted passage. Claims about the user's own math must cite the user-supplied paper / equation number.

Aggregator agent

After all 5 specialists return, ONE final aggregator agent (general-purpose) merges results.

Aggregator system prompt:

You are merging 5 specialist code-audit reports. Do NOT add new
findings. Do NOT remove any cited finding. Your job:

1. Group findings by file:line — show all specialists who flagged
   the same line together.
2. Deduplicate identical findings (same line, same root cause).
3. Rank by severity:
   - CRITICAL — code will not run, or returns silently wrong values
   - HIGH — reproducibility broken, results not trustworthy
   - MEDIUM — style / maintainability / minor numerical risk
   - LOW — cosmetic
4. Emit final report in this exact structure:

   ## Summary
   - N findings total (X critical, Y high, Z medium, W low)
   - K verified vs U unverified
   - File coverage: list of files reviewed

   ## Findings
   For each finding:
   ### [SEVERITY] file:line — short title
   - **Specialist:** name
   - **Problem:** 1-2 sentences
   - **Source:** URL + verbatim quoted passage
   - **Fix:** minimal diff or code snippet

   ## UNVERIFIED claims (skipped)
   - List anything a specialist could not source

   ## What was NOT checked
   - Be explicit about gaps (e.g. "no integration tests run",
     "GPU-specific behavior not reproduced on this machine")

NEVER invent a finding. NEVER paraphrase a source quote — quote
verbatim. If a specialist had zero findings, say so explicitly.

Orchestrator prompt template (paste-and-go)

I want a 5-way parallel audit of the following code.

CODE TO AUDIT:
<paste file path OR ```python ... ``` block>

CONTEXT (optional — fill if relevant):
- This code implements: <one-line description>
- Reference paper / spec: <citation or URL if any>
- Known constraints: <e.g. must run deterministic on A100>

SPAWN 5 SPECIALISTS IN PARALLEL IN ONE MESSAGE:
1. static-analyzer
2. library-api-auditor
3. standards-spec-auditor (or "NOT APPLICABLE" if not wireless)
4. reproducibility-auditor
5. numerical-correctness-auditor

Each must follow the citation contract from the code-audit-fanout
skill: every finding cites a URL + verbatim quoted passage. No
claim without a source. UNVERIFIED items must be marked, not
hidden.

After all 5 return, spawn the aggregator agent to merge. Show me
the merged report ONLY — do not show raw specialist outputs.

Files in this skill folder

• SKILL.md — this file. Self-contained — all 5 specialist prompts, the aggregator prompt, the orchestrator template, and the citation contract are inline above. No companion files required to run.

Citation contract (the only rule that matters)

Every finding MUST include:

1. URL to the official documentation page or spec.
2. Verbatim quote from that page — not a paraphrase.
3. Anchor (section / clause / table number) within the page.

Example of a valid finding:

HIGH — train.py:42 — torch.use_deterministic_algorithms not called
Specialist: reproducibility-auditor
Problem: Code seeds RNGs but does not enable deterministic
algorithms. CUBLAS, cuDNN convolutions, and atomicAdd float ops
will produce different bit patterns across runs.
Source: https://docs.pytorch.org/docs/stable/notes/randomness.html
        §"Avoiding nondeterministic algorithms"
Quote: "torch.use_deterministic_algorithms() lets you configure
PyTorch to use deterministic algorithms instead of nondeterministic
ones where available, and to throw an error if an operation is
known to be nondeterministic (and without a deterministic alternative)."
Fix:
  import torch
  torch.use_deterministic_algorithms(True, warn_only=False)
  torch.backends.cudnn.deterministic = True
  torch.backends.cudnn.benchmark = False

Example of what is NOT allowed:

❌ "I think this might cause issues with reproducibility."
❌ "PyTorch docs say something about determinism."
❌ "This API was deprecated recently."
❌ Any finding without URL + verbatim quote + anchor.

Failure modes + fixes

Anti-patterns

• ❌ Specialist paraphrases instead of quoting docs verbatim
• ❌ Findings without a URL — silent hallucination risk
• ❌ Standards-spec-auditor invents 3GPP clause numbers — must cite real TS
• ❌ Aggregator adds its own opinions
• ❌ Running fewer than 5 specialists to "save tokens" — defeats the cross-check
• ❌ Sequential dispatch — one message, five Agent calls

Cost model

• Wall clock: ~max(specialist) instead of sum — typically 60-120s
• API tokens: 5× a solo review's tokens + aggregator merge
• Worth it when: code touches production / publication / shared lab repos
• Skip when: throwaway notebook exploration