This skill is reference documentation for the automated review outcome persistence hook. The llmem track-review CLI command fires automatically after every adversarial code review, creating self_assessment memories without requiring manual agent intervention. This skill documents how the hook works, the severity-to-category mapping, and the content format.
How Persistence Works
Persistence happens mechanically via llmem track-review (CLI). The agent must run this command after every adversarial code review — it is not optional.
CLI: llmem track-review
The primary mechanism. Run after every review:
# For a review with findings (batch mode):
llmem track-review --finding-file /tmp/review-findings.json --context "handler.py"
# For a single finding:
llmem track-review --category NULL_SAFETY --what-happened "missing null check" --context "handler.py:42" --severity Required --caught-by self-review
llmem track-review --context "handler.py"
See categories for a severity tier:llmem suggest-categories Required
The `--finding-file` expects a JSON array of finding objects, each with `category`, `what_happened`, and optional `severity` keys.
### Python API
The Python API for programmatic review outcome tracking is planned but not yet implemented. Use the `llmem track-review` CLI command as the primary interface. Once available, the API will follow this pattern:
```python
from llmem.store import MemoryStore
store = MemoryStore(db_path=db_path)
# Programmatic tracking will mirror the CLI:
# - Single finding → one self_assessment memory
# - Batch findings → one memory per finding
# - Clean review → REVIEW_PASSED memory
# - Category suggestions → via llmem/taxonomy.py constants
Verification
After an adversarial code review completes, verify that the post-review command was run:
-
Check that at least one
self_assessmentmemory was created:llmem search "review_tracker" --type self_assessment -
For each finding, confirm the category matches the severity tier mapping below.
-
For clean reviews, confirm one
REVIEW_PASSEDmemory with outcomes "all clear".
When This Skill Is Used
- After every adversarial code review completion — the hook must be run mechanically.
- To verify that the hook ran correctly (see Verification above).
- As reference for understanding the severity-to-category mapping and content format.
Severity-to-Category Mapping
The REVIEW_SEVERITY_TAXONOMY constant in llmem/taxonomy.py maps each severity tier to applicable error taxonomy categories. The llmem suggest-categories CLI command uses this mapping directly.
| Severity Tier | Applicable Categories | Guidance |
|---|---|---|
| Blocking | AUTH_BYPASS, RACE_CONDITION, DATA_INTEGRITY | Security holes, data corruption risks, logic errors |
| Required | NULL_SAFETY, ERROR_HANDLING, MISSING_VERIFICATION, EDGE_CASE | Quality gaps — slop, missing safety checks, unhandled cases |
| Strong Suggestions | PERFORMANCE, DESIGN | Suboptimal approaches, missing tests, unclear intent |
| Noted | OFF_BY_ONE | Minor style issues, small boundary errors |
| Passed | REVIEW_PASSED | Clean review with no findings — positive outcome |
These mappings are advisory — the agent should pick the most meaningful category for the actual finding, not follow them mechanically. A Required-tier finding about performance might still map to PERFORMANCE rather than NULL_SAFETY. Note that the reviewer's tier name is "Strong Suggestions" (not just "Suggestions"); the taxonomy key matches this exactly.
Content Format
Memories created by llmem track-review use the SELF_ASSESSMENT_FIELDS format from llmem/taxonomy.py:29-39, ensuring format parity with the llmem introspect manual mode:
Category: <ERROR_TAXONOMY category>
Context: <file:line or task identifier>
What_happened: <behavioral description>
Outcomes: <results or "all clear" for clean reviews>
What_caught_it: <how discovered, e.g. "self-review">
Estimates_vs_actual: <optional>
Recurring: <"yes" or "no">
Proposed_update: <optional>
Iteration_count: <optional integer>
Key References
- CLI command:
llmem track-review— the mechanical post-review hook - CLI command:
llmem suggest-categories— list categories for a severity tier - Error taxonomy categories:
llmem/taxonomy.py:3-15→ERROR_TAXONOMY - Severity mapping:
llmem/taxonomy.py:21-27→REVIEW_SEVERITY_TAXONOMY - Self-assessment fields:
llmem/taxonomy.py:29-39→SELF_ASSESSMENT_FIELDS - Reviewer severity tiers: Defined by the adversarial review skill (e.g., Blocking, Required, Strong Suggestions, Noted, Passed) — see your review skill's Severity Tiers section
- llmem introspect command:
skills/llmem/SKILL.md(manual and automatic modes)