Retrospect

Retrospect

Turn completed-cycle evidence into a better next cycle: benchmark, diagnose, propose, verify, monitor. No retrospective theatre or one dated log per run. Scale depth to risk/friction: clean routine cycles may return no change; substantial runs, escaped defects and repeated human corrections need the full pass.

Evidence boundary

Start from approved spec, run receipts, checks/evals, review/repair history, human corrections, escaped defects, production observations, routing failures, resource use and retained artifacts. Use only authorised sources. Mark absent dimensions unknown; never infer success from a clean final answer.

For substantial+ or repeated cycles, create RETROSPECT.json from the [template](templates/RETROSPECT.template.json) and validate with scripts/validate_retrospect.py. It is evidence, not diary/project truth.

Review dimensions

• outcome and acceptance-criteria success;
• trajectory compliance, safety and escaped defects;
• human attention/corrections/rework, gate latency, unnecessary interruption and

blocked time;

• test/eval and reviewer effectiveness;
• orchestration, model routing, delegation, tool reliability;
• context size, compaction, handoff and artifact hygiene;
• skill triggering, usefulness, overlap and missing capability;
• documentation, project memory, canonical-state freshness;
• cost and latency when receipts contain trustworthy measures.

Flywheel

1. Benchmark against declared acceptance criteria/eval thresholds, baseline

and comparable runs. Separate output quality from trajectory.

1. Diagnose recurring failures by root-cause cluster: product/code,

specification, test/eval, skill, instructions, routing/adapter, tool, context/memory, documentation or authority/process.

1. Propose the smallest change for each supported cluster. Each proposal

names evidence, owner, risk, destination and success measure.

1. Verify: turn representative failures into deterministic tests/versioned

eval cases; run regressions before claiming improvement.

1. Monitor the next comparable cycle for recurrence, regressions, cost and

new failure modes. Feed supported attention/gate changes into the next scope cycle, never a parallel process diary.

improved requires authorised intervention, passing regression gate and enough comparable later cycles meeting predeclared target/guard metrics. Underpowered or confounded evidence is inconclusive.

Route unclear intent to scope, deterministic defects to implement, stochastic behaviour to evaluate, skill evidence to skill-craft, and context/docs cleanup to session or engineering-docs.

Learning and authority

Promote durable conclusions to their canonical owner: spec/ADR, runbook, project instructions, state/context digest, test/eval fixture, skill or routing policy. Merge with existing truth; never append a parallel diary. Project facts never live only in private memory; cross-project preferences follow harness memory policy.

This skill is proposal-first and read-only by default. Apply project/global harness changes only under explicit authority or an enclosing implement run; material cross-project changes return through user-approved scope. Finish with a compact table: finding, evidence, root cause, change, regression gate, owner/destination, status (promote, experiment, defer, reject).

Adapter-absent path

Without optional Console, Herdr or GitHub, use canonical project artifacts and emit the skill-owned kind in [portable-workflow.v1.json](portable-workflow.v1.json). It records retrospective evidence; improvements still require normal authority.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mblauberg
• Source: mblauberg/provenant
• License: MIT

Install and usage instructions live in the source repository linked above.