triage

name	triage
type	skill
category	instruction
description	Triage a corpus, classify, and dispatch outputs. Three modes: retro (transcript review → issues), trend (longitudinal performance analysis), sweep (GitHub issue triage → fix-epics). Delegates execution to pauli for all three modes to keep main context clean.

/triage — Unified Triage Skill

Triage a corpus, classify findings, and dispatch outputs according to the selected mode.

Mode	Corpus	Primary output
retro	Session transcripts (one at a time)	GitHub issues filed via gh
trend	Many sessions / audit files	Trend report + recommendations
sweep	Open GitHub issues	PKB tasks, fix-epics, closures

Privacy Rule: the anonymisation clause in .agents/skills/debug/forensic-scope.md binds every mode here, not only retro.

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Dispatch Model

Every mode dispatches to pauli: the premise test these reviews turn on, and the graph mutation sweep performs, are both hers (plugins/pkb/agents/pauli.md).

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Mode: retro

Perform a critical, forensic review of a single session transcript, apply immediate fixes where appropriate, and file the tracking GitHub issues.

1. Transcript Selection & Quality Gate

• Explicit Target Requirement: You must only review the specified session ID, transcript path, or current session context passed in the prompt. Do NOT fall back to selecting a random unreviewed transcript. If no session context, ID, or path is provided, halt and report an error.
• Same-Session Review Allowed: the current session may be reviewed by a fresh subagent, whose detached context is what makes the review honest. What is never allowed is the same agent grading its own work in the same context.
• Verify $AOPS_SESSIONS is set and $AOPS_SESSIONS/transcripts exists. If not, stop and ask the user.
• Resolve the target session ID against $AOPS_SESSIONS/transcripts/YYYY-MM/. Each session has a markdown, an HTML, and a JSON sidecar artifact — see specs/transcript-pipeline.md. Read the markdown.
• Quality Gate: Verify the transcript is complete and usable before analyzing it. If it isn't, name the failed condition and stop. Never silently fall back to the raw .jsonl as a workaround — a forensic review on a degraded transcript yields false findings; proceed on raw JSONL only with explicit user confirmation.

2. Forensic Analysis & Immediate Fixes (Fix AND File)

• Read the entire transcript.
• The forensic read, the in-scope/out-of-scope split between fixing the reviewed session and changing the framework, and the "fix and route" invariant are stated in .agents/skills/debug/forensic-scope.md. Apply it as written. In retro, "route the lesson" means the destination chosen in §4, and the record it names is the filed GitHub issue.

2a. Classified recurrence — bad-premise approval (attribute the miss to the reviewer)

Good, working, well-tested work done for a bad idea is a bad-premise approval, and the miss is scored against the reviewer who passed it, not only the author. Every review surface (arch-fit / /verify / rbg / pauli) carries a forced step-0 premise test — was this worth building at all, in this shape? — so a PASS on a bad premise means that test was skipped or rationalised past, and test-passing is never the excuse.

The filed issue names the approving surface as the locus of the miss, alongside the premise that should have been bounced. A deterministic rig — regex/threshold/NLP/checklist — built for a call a smart agent should just make (judgment-non-delegable) is one worked instance, not the whole class.

2b. Framework/behavioral changes are never a retro fix

Retro's job stops at naming the gap precisely in the filed issue. Deciding on a framework change — including which mechanism carries it and the spec update .agents/rules/RULES.md requires — is a separate, deliberate pass outside retro.

3. Output Requirements

Produce a review in this exact format. Keep text concise:

## Transcript Review: <filename>

**Session**: <session_id> **Date**: <date> **Project**: <project>
**Verdict**: [EXCELLENT | GOOD | ADEQUATE | POOR | FAILING]

### Findings

[Concise description of failures/successes, grouped by structural cause.]

### Patterns (Optional)

[Upstream/structural root cause spanning multiple findings.]

4. File Issues & Apply Changes

• Search existing issues/PRs using gh issue list and gh pr list to avoid duplication.
• If a match exists, comment with a concise delta comment (new date, facts, and impact). Edit structurally using gh issue edit.
• If no match, create a bug issue (cap at 3 per session). Title must be Bug: <brief-slug>.
• Issue body must contain only forensic fields: Incident facts, Structural shape, and Impact. Do not propose solutions in the issue report.
• Record review provenance in the daily note as a single, self-contained semantic chunk (e.g., an H3 heading or list item) to allow the PKB to index it. Write the full verbatim text including the reviewed_by block (fields: agent, date, verdict, issues_filed, session ID, transcript path) under a heading like ### Retro review stamp: session () with tags #retro #reviewed #triage-retro #. This indexed entry serves as the durable already reviewed signal that prevents re-triaging the same transcript.
• Execution & Validation:
  • For any immediate fixes applied to the codebase, run the test suite (e.g., uv run pytest) to verify no regressions were introduced.
  • Commit the changes and open a PR with a description referencing both the fix and the filed GitHub issue(s).

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Mode: trend

Review multiple sessions to identify systemic effectiveness and trends.

Corpus selection — prompt mining vs trend reading. If the goal is to extract what the user typed (prompts, /command invocations, skill usage patterns), start with the structured summaries corpus at $AOPS_SESSIONS/summaries/YYYY-MM/*.json. Read the top-level user_prompts array ([{timestamp, text}]) or filter timeline_events[type="user_prompt"] to system_injected=false — across ALL clients, no client-name filter needed. This is faster and more reliable than grepping raw transcripts. Read the field set off a sidecar itself; there is no schema doc. Raw transcripts ($AOPS_SESSIONS/transcripts/) are the fallback for content the sidecars don't capture (agent reasoning, tool calls).

1. Sampling & Reading

• Select 8 to 15 files spanning both early and recent periods, including size and project diversity.
• Extract: Context, component behavior, accuracy (TP/FP/FN), and trajectory impact.

2. Synthesis & Output

Analyze aggregate true/false rates, temporal trends, coverage, and cost-benefit. Produce a report in this format:

# Trend Review: <Component Name>

**Question**: <review question> **Date**: <today>
**Corpus**: <N> files, <date range> **Sample**: <N> files (criteria: ...)

## Executive Summary

[3-5 sentences summarizing if the system is working and the main trend/issues.]

## Objectives Verdict

| Objective | Verdict                                      | Evidence |
| --------- | -------------------------------------------- | -------- |
| [obj 1]   | ANSWERED / PARTIALLY ANSWERED / UNANSWERABLE | [why]    |

## Individual Assessments

### <filename> (<date>)

- **Context**: ... **Component behavior**: ... **Accuracy**: [TP/FP/FN] **Impact**: ...

## Aggregate Analysis

[Signal Quality / Temporal Trends / Coverage Map / Cost-Benefit]

## Recommendations

[Actionable, prioritized recommendations with evidence citations.]

## Confidence and Limitations

Save the report to $AOPS_SESSIONS/reviews/<component>-trend-<date>.md.

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Mode: sweep

Triage and process open issues on nicsuzor/academicOps (batch limit: ≤20 issues).

1. Issue Triage & Dispositions

Fetch open issues, focusing on the focal issue first if a directed focus is provided. Classify using this rubric:

Disposition	Criterion	Action	Label
close-as-stale	>90d old, no activity, or describes retired behavior.	Close issue with explanatory comment.	triaged-stale
consolidate-duplicate	Describes the same underlying problem.	Merge details into canonical issue; close duplicate with cross-link comment (#N).	triaged-duplicate
aggregate	Multiple issues sharing one root cause/fix surface.	Fold into one fix-epic task (leave queued); close source issues with pointer comments.	triaged-aggregate
evidence-bump	Accumulates evidence for a related open issue/epic.	Leave open; add comment citing canonical issue (#N).	triaged-evidence-bump
single-task	Atomic task (AC clear, ≤3 files, obvious fix).	File polecat task with Closes #N.	triaged-single
fix-epic	Multi-step, multi-file, or design-required work.	Create epic task, leave at inbox for the user to brief.	triaged-epic
defer	Real but blocked or low-criticality.	Apply defer label and revisit-by date.	triaged-defer

• Execution: Apply low blast-radius dispositions autonomously. Gate ONLY on:
  • Ambiguous classification (Needs human triage).
  • Add-or-escalate enforcement proposals (requires step 2b review).
  • Hard halts (locked merge gates, irreversible operations).

2b. Enforcement-Escalation Review (Legislative Role)

For proposals that add or escalate a rule, perform this review before assigning the disposition:

1. Generalize the failure into a Root Cause Category.
2. Map to existing mechanisms in the enforcement map and axioms.
3. Classify: Propagation failure (fix via L1 propagation) vs. Escalation candidate (requires CBA: ≥3 recurrence links).
4. Default to the cheapest tier (L1 propagation). Flag enforcement changes not reflected in specs/enforcement/enforcement.md as a pipeline gap.

2c. Output Cycle Report

Log results in the following format:

## Cycle <N> — applied (open before: <K>; batch: <M>)

### Applied autonomously (done)

- Consolidate-duplicate: #R → unique detail merged into #S, #R verified state:closed
- Aggregate: #A, #B, #C → folded into fix-epic <id> (queued)
- Evidence bump: #T → bumped #U
- Close (stale): #P
- Defer: #Q (revisit-by YYYY-MM-DD)
- Single-task: #X → "<title>"
- Fix-epic (queued): <title> ← #D, #E

### Needs human triage / decision (waiting)

- #Z (rubric ambiguous: <reason>)
- <add-or-escalate proposal>: cost-ladder reasoning + ≥3 recurrence links

3. Execution Rules

• Task creation: Omit severity on tasks created during the sweep — severity belongs on target milestones, not ordinary tasks. Created tasks default to inbox; ready is computed downstream, never hand-written.
• Priority: Leave priority at its default on swept tasks. Never infer, estimate, or propagate a band — only the principal sets intent. To make a swept task more important, raise the stated_weight of its contributes_to edge, never the priority.
• Verification: Confirm closed issues actually reached state: closed.
• Handoff: After the cycle, Skill(skill="verify", args="Verify cycle <N> of the issue sweep.")
• Halt: Exit after completing exactly one cycle.