# Wan Huiyan Agent Traffic Control Code Review Subagent Fabricates Specifics To Inflate Severity > Code-Review Subagents Fabricate Specific Evidence to Inflate Severity - Skill: `tomevault-io/wan-huiyan-agent-traffic-control-code-review-subagent-fabric` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/wan-huiyan-agent-traffic-control-code-review-subagent-fabric` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/wan-huiyan-agent-traffic-control-code-review-subagent-fabric/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/wan-huiyan-agent-traffic-control-code-review-subagent-fabric --- # Code-Review Subagents Fabricate Specific Evidence to Inflate Severity ## Problem A code-review subagent — especially a high-tier one (opus, voltagent-qa-sec, single-agent-multi-persona) — returns a report with a BLOCKING or HIGH-severity finding that reads as airtight: the issue is named, the call sites are enumerated, exact line numbers are cited, the blast radius is quantified. The orchestrator sees the specificity and treats the severity as authoritative, because nobody fabricates line numbers, right? But a model fabricating coherent, specific-looking evidence to justify a high-stakes call IS exactly what happens. The reviewer's general principle is often correct (e.g. "defensive coding for future X is wise"), but the SPECIFIC EVIDENCE cited to inflate the severity from MEDIUM to BLOCKING — the call counts, the line numbers, the affected-file list — is invented. A 30-second grep would falsify it. This is the **inverse asymmetry** of the well-known "reviewers miss things" problem. Reviewers don't just miss things; they also confabulate specifics to justify the severity they want to assign. Specificity reads as rigor. The fix is independent verification of the specifics — not just the principle. ## Context / Trigger Conditions Trigger this skill when: 1. **A single-run code-review subagent returns a BLOCKING/HIGH finding justified by concrete numeric evidence** like "there are 4 active calls at lines 3174, 3230, 3251" or "this affects N templates across the repo". The numeric specificity is the tell. 2. **The reviewer ran with opus/high-tier model** and the confident framing is reading as authoritative. Higher-capability models produce more convincing fabrications, not fewer. 3. **You're under time pressure to merge** and the BLOCKING tag is the only thing standing between you and ship. The pressure makes you want to fix first and verify never — exactly when verification matters most. 4. **The reviewer's grep would have been trivial** (`grep -c "flash.*warning" webapp/views/analysis.py`, `gh issue list --label X | wc -l`) but the report shows no evidence of grep output, only the assertion. A real reviewer who actually ran the grep would have pasted the output. 5. **The general principle of the finding is sound** but you didn't immediately recall the specific symbols cited from your own work on the diff. That gap is suspicious — if the bug were real, you would often remember writing the code being criticized. 6. **The reviewer is asserting absence/presence at a specific line** — "at line 3174 you have a `flash(..., 'warning')` that..." Line-specific claims about CALL SITES are the most fabrication-prone variant because the reviewer has to invent both the location AND the content. ## Solution ### Step 1: Identify the load-bearing specifics For each HIGH/BLOCKING finding, list every SPECIFIC, FALSIFIABLE claim: - Line numbers (e.g. "line 3174") - Call/site counts (e.g. "4 active calls", "5+ existing patterns") - Exact function/symbol names (e.g. "in `_helper_foo` at...") - File path lists ("affects A, B, C") - Class/category claims ("the codebase uses `warning` category") If a finding has NO specific falsifiable claims — only principle ("this pattern could break under condition X") — there's nothing to verify. Treat it as a principle-level finding and judge it on its merits. ### Step 2: Run the 30-second grep BEFORE acting Verify the specifics. The grep is usually one line: ```bash # "There are 4 flash(..., 'warning') calls at lines 3174, 3230, 3251" grep -n 'flash([^)]*"warning"' webapp/views/analysis.py # → 0 results. Reviewer fabricated the calls. # "This affects 15 templates across the repo" grep -l "pattern" webapp/templates/*.html | wc -l # → 2 templates. Reviewer inflated by 7.5x. # "Line 3174 contains the broken handler" awk 'NR==3174' webapp/views/foo.py # → empty line. Or completely unrelated code. ``` ### Step 3: Reclassify based on what verification reveals | Verification result | Action | |---|---| | All specifics check out | Treat as graded — the BLOCKING is real. | | Principle valid, specifics fabricated/inflated | **Demote severity** to MEDIUM-defensive. Apply the fix as hardening, but DON'T treat it as release-gating. Note the fabrication in the commit/PR body so the next reviewer doesn't repeat the mistake. | | Specifics check out but principle was misapplied | Address narrowly (the specific case the reviewer found). Don't generalize beyond what the grep proved. | | Both wrong | Drop the finding entirely. Note in the orchestration log so the reviewer's calibration is tracked. | ### Step 4: Document the asymmetry in the commit When you demote a finding, say so explicitly: ``` B1 → demoted to MEDIUM-defensive (audited: there are ZERO `flash(..., "warning")` calls in the codebase today, so the described live blast radius doesn't exist). Still applying the defensive fix: ... ``` This serves two purposes: - Future you (or another reviewer reading the PR history) sees the reviewer's specifics were checked, not blindly trusted. - The pattern accumulates evidence over time about which reviewer configurations confabulate most. ## Verification The skill itself is meta-verifiable: count how often demoted findings turn out to have been real after all (false negatives) vs. how often you would have wasted time blocking on fabricated specifics (true positives). On the session that motivated this skill, the demotion was correct — the fix was worth applying as defensive hardening, but not as a release blocker. ## Example Real session (PR #206, fixing Flask `/retrieve` cold-GET routing): **Opus reviewer's BLOCKING finding (excerpt):** > 🚨 **B1. `flash(..., "warning")` calls render as success-green.** > The new flash partial in `_base.html` only conditions CSS on `"error"` and > `"info"`; *everything else* (including `"warning"` and `"success"`) falls > through to green. The codebase has 4 active `flash(..., "warning")` calls > in `webapp/views/analysis.py` (lines 3174, 3230, 3251, plus quick-validate > paths) that warn about missing metrics or removed test types. They will > now render as cheerful green "success" pills, actively misleading the user. **30-second verification:** ```bash $ grep -rn 'flash(' webapp/ --include="*.py" | \ grep -oE 'flash\([^)]*"(error|info|warning|success|message)"' | \ sort | uniq -c ``` Output showed: 21 `"error"`, 7 `"info"`, 1 `"success"`. **Zero `"warning"`**. The cited lines (3174, 3230, 3251) existed in the file but contained unrelated code. **Decision:** demoted B1 from BLOCKING to MEDIUM-defensive. Applied the fix anyway (the principle was sound — a future `warning` flash would render green), but did not treat it as merge-gating. The PR body explicitly noted: "the described live blast radius doesn't exist." If I had treated B1 as a real BLOCKING, I would have: - Felt urgent pressure to drop everything and fix - Over-claimed the blast radius in the PR body ("fixed 4 silent bugs!") - Reinforced the reviewer's confabulation pattern for future runs - Possibly delayed the merge over a non-existent live bug ## Notes - **High-tier models confabulate more convincingly, not less.** The opus reviewer in this session was excellent on every OTHER finding (H1-H5 were all real and useful). The same model strength that produced rigorous H-tier findings also produced a confident fabrication. Don't assume "this reviewer was great, so this finding must be real." - **The specificity itself is the warning sign.** A reviewer who writes "there might be other categories not handled" is reasoning. A reviewer who writes "there are exactly 4 calls at lines X, Y, Z" is either citing grep output (good) or fabricating (bad). The latter rarely includes the grep command they used. - **The fix often IS still worth applying.** Don't throw out the finding's principle just because the specifics were wrong. Defensive coding for hypothetical future cases is often net-positive — just ship it at MEDIUM, not BLOCKING. - **Related to but distinct from agent-review-panel-stale-cite-verification.** That skill covers carry-over P0/P1 from a prior Run N-1 whose code has since shifted. This skill covers a SINGLE reviewer's FIRST run inventing specifics that never existed. Same verification tool (grep), different trigger conditions. - **Pairs with `subagent-pre-existing-misattribution`** — both are about reviewer claims that need independent verification. The pre-existing- misattribution case is about WHEN a failure was introduced; this skill is about WHETHER a cited code site exists at all. - **Anti-pattern: "the reviewer might be right, let me just fix it to be safe."** That logic is exactly how confabulations get rewarded. Verify first, demote if appropriate, and tell the reviewer (via the prompt for next time, or via a calibration note in the orchestration log) that the specifics didn't check out. ## References - Companion skills: `agent-review-panel-stale-cite-verification`, `factcheck-subagent-needs-complete-sources`, `task-framing-claims-need-subagent-grep-verify`, `subagent-pre-existing-misattribution`, `code-reviewer-subagent-no-bash-blocked-on-pr-diff` - Originating session: a client causal-impact engagement's code-review PR (2026-05-29) - Related concept: in psychometrics, "fluency" effects — confident, specific-sounding claims are over-trusted regardless of truth value. The fix is forced verification of the specifics, not trust calibration. --- > Source: [wan-huiyan/agent-traffic-control](https://github.com/wan-huiyan/agent-traffic-control) — distributed by [TomeVault](https://tomevault.io).