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Description
What happened
On PR #2179, the review agent detected that the PR description claimed to fix a pre-existing broken link (-inference.md → getting-inference.md) in docs/guides/README.md, but this change was not present in the diff. The agent correctly identified this discrepancy but rated it as Info severity under the broken-link-fix tag. The human reviewer approved the PR without addressing this or any other finding.
The review agent ran as workflow 27355898627 (2026-06-11 14:55–15:09 UTC). The human approved on 2026-06-12 with an empty review body.
What could go better
When a PR description explicitly claims a change that isn't reflected in the diff, this indicates one of two problems: (1) the change was accidentally omitted and should be included, or (2) the PR description is inaccurate and misleading for future readers. Either case is more significant than Info severity.
I'm fairly confident this is a repeatable pattern. Agent-generated PRs include structured summaries, and human-authored PRs often describe intended changes that may not all land in the final diff. Catching these discrepancies at a higher severity would increase the chance of human engagement.
Uncertainty: I don't have data on how often this pattern occurs across other PRs. It's possible this is rare enough that escalating severity would add noise rather than signal. A sample of 10-20 PRs with Info-level findings would help validate frequency.
Proposed change
In the review agent's finding classification logic (likely in the code-review skill or review sub-agent definitions), add a heuristic that detects when the PR description/body explicitly claims a change (e.g., 'Fixes a broken link in X', 'Adds Y to Z') that cannot be corroborated by the diff. When detected, classify this as Medium severity with a tag like description-diff-mismatch rather than Info.
The detection should look for action verbs in the PR body (fixes, adds, removes, updates, changes) paired with specific file or path references, then verify those files appear in the diff with corresponding changes. This is a targeted check, not a general NLP problem — PR descriptions tend to use predictable phrasing.
This change belongs in the review agent's finding evaluation layer, not in per-repo configuration.
Validation criteria
Over the next 20 review agent runs on PRs with non-trivial descriptions:
- At least 1-2 PRs should trigger the
description-diff-mismatchfinding when applicable. - False positive rate should be below 20% (i.e., at most 1 in 5 triggered findings should be incorrect).
- When triggered, the finding should be rated Medium, not Info.
- Spot-check 5 triggered findings manually to confirm they identify genuine discrepancies between description and diff.
Generated by retro agent from #2179
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fullsend-ai-triage commentedon Jun 15, 2026
🤖 Finished Triage · ✅ Success · Started 6:28 AM UTC · Completed 6:34 AM UTC
Commit:
4e21a60· View workflow run →fullsend-ai-triage commentedon Jun 15, 2026
Per the request to re-check relevance: this remains a valid, unimplemented gap. As of 2026-09-22, the intent-coherence finding taxonomy in fullsend-ai/agents's
skills/pr-review/SKILL.mdstill has nodescription-diff-mismatchcategory, andskills/pr-review/sub-agents/intent-coherence.mdstill contains no logic for cross-checking PR-description claims against the diff. No PR (open or merged) in either fullsend-ai/fullsend or fullsend-ai/agents addresses this, and there is no closing PR reference on this issue.Two related retro-agent issues have since been filed and remain open: one proposing a Low-severity check for PR descriptions with structured Changes tables that list files absent from the diff, and another proposing a Low-severity check for explicit default-value claims that contradict the diff. Both are narrower variants of the same underlying description-vs-diff verification idea proposed here at Medium severity. Since this issue predates both, it is not being treated as a duplicate, but a maintainer may want to consolidate the three into a single implementation effort rather than building three overlapping heuristics.
This is left open for human prioritization rather than auto-dispatched, consistent with the request to treat this as a stale-issue relevance check rather than an implementation trigger.
Labels: The issue proposes new review-agent capability (not a defect fix), matching the type/feature label; existing agent/review, triaged, and component/harness labels remain accurate.
Next steps:
/fs-code— agent creates a PR to implement this issue/fs-code <your instruction>— agent implements with your specific guidancePrevious run
Triage Summary
Severity: Medium · Category: Feature
Problem
The review agent rates PR-description-vs-diff discrepancies as Info severity, which means they are routinely overlooked during human review. When a PR description claims a change that isn't in the diff, this indicates either an accidentally omitted change or a misleading description — both warrant reviewer attention.
Root Cause
The
intent-coherencefinding dimension in the pr-review skill lacks a dedicated category for description-diff mismatches. The closest existing category (misleading-label) covers issue label mismatches, not PR body accuracy. Without a specific category and severity mapping, these findings default to Info.Recommended Fix
description-diff-mismatchcategory to theintent-coherencedimension inskills/pr-review/SKILL.md(finding category taxonomy, around lines 195–202)skills/pr-review/sub-agents/intent-coherence.mdwith a heuristic that detects action verbs in the PR body (fixes, adds, removes, updates, changes) paired with file/path references, then verifies those files appear in the diffProposed Test Case
Validation Criteria
The issue includes well-defined validation criteria: over 20 review runs, expect 1–2 true positives with <20% false positive rate. Spot-check 5 triggered findings manually to confirm genuine discrepancies.
Labels: This is a review agent feature request for improving finding classification in the pr-review skill/harness.
fullsend-ai-prioritize commentedon Jun 15, 2026
RICE Priority Score: 1.2
Score breakdown
Formula: (1.5 x 1 x 0.8) / 1 = 1.2
ralphbean commentedon Sep 22, 2026
/fs-triage lots of things have changed between when this was originally filed and now across both the https://github.com/fullsend-ai/fullsend and https://github.com/fullsend-ai/agents repos. Is this still relevant? This is a stale-issue review, not an implementation request: even if the issue is still a valid bug and has sufficient information, do not recommend or trigger ready-to-code yet. Leave it for human prioritization after confirming whether it remains relevant. Do not close a still-relevant bug merely to avoid coding it.
fullsend-ai-triage commentedon Sep 22, 2026
🤖 Finished Triage · ✅ Success · Started 1:57 PM UTC · Completed 2:01 PM UTC
Commit:
b9e5099· View workflow run →Runtime: claude · Model: sonnet → claude-sonnet-5 · Effort: high · Cost: $0.29