run_retrospective¶
Perform a structured post-engagement retrospective analysis.
Read-only: Yes (optionally writes to disk)
Description¶
Produces five structured outputs:
- Inference rule suggestions — patterns the graph shows that existing rules missed
- Skill gap analysis — skills unused vs. techniques attempted without skills
- Context-improvement recommendations — where context, logging, validation, and coverage should improve
- Attack path report — client-deliverable markdown (timeline, findings, recommendations)
- Heuristic RLVR traces — state→action→outcome triplets with explicit confidence and trace quality
Parameters¶
| Parameter | Type | Default | Description |
|---|---|---|---|
write_to_disk |
boolean |
false |
Save all outputs to files |
output_dir |
string |
"./retrospective/" |
Directory for output files |
Returns¶
| Field | Type | Description |
|---|---|---|
summary |
string |
High-level summary |
inference_suggestions |
array |
Suggested new inference rules with evidence |
skill_gaps |
SkillGapReport |
Unused skills, missing skills, failed techniques |
context_improvements |
ContextImprovementReport |
Frontier observations, context gaps, OPSEC observations, logging quality |
training_traces_count |
number |
Number of RLVR training traces |
trace_quality |
TraceQualityReport |
Quality assessment of training traces |
report_preview |
string |
First 500 chars of the markdown report |
output_dir |
string |
Output directory (if write_to_disk: true) |
Output Files (when write_to_disk: true)¶
Written to <output_dir>/<engagement_id>/:
| File | Content |
|---|---|
report.md |
Client-deliverable attack path report |
inference-suggestions.json |
Suggested inference rules |
skill-gaps.json |
Skill gap analysis |
context-improvements.json |
Context improvement recommendations |
training-traces.json |
RLVR training traces |
trace-quality.json |
Trace quality assessment |
summary.txt |
Summary text |
Usage Notes¶
- Run at the end of an engagement or after significant progress
- The attack path report (
report.md) is designed for client delivery - Inference suggestions can be applied to future engagements via
suggest_inference_rule - Training traces can be used for model fine-tuning (RLVR)
- Also available as a CLI:
npm run retrospective