task-appraisal

name	task-appraisal
description	How pre-mortems and post-mortems work, what verdicts mean, and how to act on appraisal results.
agents	cognitive

What Are Pre-Mortems and Post-Mortems?

The system automatically appraises your tasks at two points:

Pre-mortem — fires when you activate a task (if it's complex enough). Imagines the task has failed and identifies: predicted failure modes, blind spot assumptions, dependencies at risk, and information gaps. This is stored on the task and feeds the Forecaster's health evaluation.

Post-mortem — fires when a task completes, fails, or is abandoned. Evaluates whether the goal was achieved, compares pre-mortem predictions against reality, extracts lessons learned, and creates a CBR case from the outcome. A sensory event reports the verdict.

Neither blocks you. Both run as fire-and-forget processes after the lifecycle transition. If they fail (LLM error, timeout), the task proceeds normally.

When They Fire

Transition	What fires	Condition
Task activated	Pre-mortem	Complexity >= medium, OR 3+ steps, OR part of an endeavour
Task completed	Post-mortem	Always. Simple tasks get deterministic "Achieved". Medium+ gets full LLM evaluation
Task failed	Post-mortem	Always full LLM, regardless of complexity
Task abandoned	Post-mortem	Always full LLM — abandonment has the most to teach
Endeavour completed	Endeavour post-mortem	Synthesises across all task post-mortems

Simple 1-2 step standalone tasks skip the pre-mortem entirely and get a lightweight deterministic post-mortem. This is by design — the cost of an LLM call isn't justified for trivial work.

Verdicts

Post-mortems produce an AppraisalVerdict:

Verdict	Meaning	What to do
Achieved	Goal fully met	Nothing — the system created a Strategy CBR case
PartiallyAchieved	Goal partly met, gaps remain	Consider a follow-up task for the gaps. Check lessons learned
NotAchieved	Goal not met	Review contributing factors. Check if the approach was wrong or the goal was unrealistic. A Pitfall CBR case was created
AbandonedWithLearnings	Stopped early, but something was learned	The learning is the value. Check lessons — they feed future planning

Reading the Sensory Event

When a post-mortem completes, you'll see a sensory event like:

<event name="post_mortem" title="Research pricing — partially_achieved">
  Task task-abc123 post-mortem: partially_achieved. Key lesson: Two of three
  competitor sites required authentication.
</event>

This is your cue to decide:

1. Achieved — no action needed, move on
2. PartiallyAchieved — decide if the gaps matter. If yes, create a follow-up task. If not, accept the result
3. NotAchieved — investigate. Use get_task_detail to see the full post-mortem. Consider a different approach if retrying
4. AbandonedWithLearnings — the learning has been captured in CBR. Future similar tasks will benefit from this case

How Pre-Mortems Help Planning

When you're about to start a complex task, the pre-mortem predictions are stored on the task. The Forecaster uses them as a baseline — if a predicted risk materialises, the health score reflects it. You can view predictions with get_task_detail.

When planning similar future tasks, recall_cases will surface Pitfall cases from past post-mortems. Read the lessons before committing to an approach.

Configuration

Controlled by [appraisal] in config.toml:

• min_complexity — minimum task complexity for pre-mortem (default: "medium")
• min_steps — minimum steps for pre-mortem (default: 3)
• model — LLM model for appraisals (defaults to task_model)
• max_tokens — max tokens per appraisal call (default: 4096)