CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

workflow-retro

Weekly workflow retro over sessions, evals, memory. Trigger: retro, auditar semana.

67

Quality

84%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Workflow Retro

One question: what friction repeated this week, and what single change removes it?

Query via ywai. Raw DB reads only for the freshness check in references/queries.md.

Rules

  1. Fix scope first: days (default 7), project or worktree. No scope, no retro.
  2. Collect every source before concluding. One failed source never stops the run.
  3. Validate freshness before analyzing. A blind tracker is finding #1, not a footnote.
  4. Cluster only on >=3 evidences. One anecdote is not a finding.
  5. Falsify before proposing. Each hypothesis states what would disprove it; check that first.
  6. One friction = one lever (skill|agent|tool|code) + one experiment. Never two changes in one experiment. Classify first: mechanical (fixed syntactic pattern, banned API, import shape, file-location rule) goes to a deterministic check (lever code|tool: repo linter rule, pre-commit hook, or CI job); judgement call (cross-file consistency, matches surrounding style, needs intent) goes to skill|agent. Default to building the check. Place the fix where it runs. Review imposes standards. Implementation follows pointers. AGENTS.md holds pointers only.
  7. Report as HTML (see references/report.md). OS temp dir, open it, report the absolute path. Nothing lands in the repo.
  8. mem_save only the top 3 as pattern. The detail lives in the report.

Process

1. Scope

Ask at most: project/worktree + days. Default days=7. Record the exact filters in the report header.

2. Collect

Run in order, --json always. Full command list in references/queries.md:

  • Freshness: session_v2 vs session counts + max dates, raw SQL, read-only.
  • ywai eval run --days <n> --worktree <w> --json — sessions aggregate.
  • GET /api/evals/runs + /api/evals/summary — hard scores, turns, invalid, cost, attempt errors.
  • GET /api/engram/search?q=<symptom> + /prompts + /observations + POST /api/engram/memory-evals — recall quality.
  • git log --since=<n>.days — oneline + name-only + authors.
  • Guardrail: repo check command + hooks + CI workflow (lint/check scripts, pre-commit hook, CI lint/typecheck/test job). Read before proposing a new check. An unwired or broken check is the finding, not a reinvention.

3. Validate

Apply references/thresholds.md: thin window → widen AND retitle; stale tracker → finding #1; thin memory → Speculative. Pick ONE primary window here; everything downstream uses it.

4. Cluster

Rules first (references/thresholds.md), LLM second. Method in references/analysis.md: rework done right, early-death vs hard-task, cost-vs-overhead, cross-source joins.

5. Interrogate

For each cluster, write the disproof before the proposal: the one check that would kill your hypothesis (attempt error field, revert list, author split). Run it. Dead hypotheses die here, silently.

6. Propose

Survivors become cards: evidence (3 sessions + metrics) / hypothesis / lever / ONE experiment / success metric. Rank by frequency × impact × fixability. Fewer Strong findings beats padded ones. A repo with no guardrail (no pre-commit hook and no CI job running lint/typecheck/test) is itself a finding, not a neutral default.

7. Report + save

Render assets/report-template.html with the data. Buttons for PDF + PNG ship in the template. Then mem_save top 3.

Repository

YoizenSA/dev-ai-workflow

Path

ywai/skills/workflow-retro/SKILL.md

Last updated
First committed

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.