agentleFS
jwilgerjwilger/agent-skills/skills/factory-review/SKILL.md

factory-review

Skill4 starsChanged 8 months agoCC0-1.0View on GitHub

Structures the human review experience for factory-mode builds with dashboard-style audit trail summaries (slice counts, rework rates, gate failure distribution), PR digests (per merged PR with gate results), retrospective synthesis (recurring rework causes, pair effectiveness, domain hotspots), quality trend tracking (session-over-session comparison), escalation review (rework history with resolve/override/send-back options), and autonomy tuning interface. Use during Phase 3 human review of factory-mode builds or when assessing autonomous build quality. Triggers on: "review the build", "factory review", "audit trail", "quality trends", "escalation review", "PR digest", "autonomy tuning", "rework rate". NOT for: running the pipeline (use pipeline), individual code reviews (use code-review).

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 5 are distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read from .factory/audit-trail/ and present a concise build summary:

    line 45
  2. Distinctive:

    Keep each digest to 3-5 sentences.

    line 68
  3. Very specific:

    Read from .factory/audit-trail/metrics/ and present trends:

    line 88
  4. Very specific:

    Validate: Check that the change is consistent (e.g., cannot enable auto-merge at conservative autonomy level.

    line 105
  5. Distinctive:

    Apply or reject: If valid, apply the change.

    line 110
  6. Very specific:

    Never apply config changes silently.

    line 113
  7. Very specific:

    No silent config changes: [H]

    line 134
  8. Distinctive:

    If you have a concern, present it separately as your own analysis, clearly labeled.

    line 142
  9. Distinctive:

    After completing a factory review session, verify:

    line 151
  10. Distinctive:

    If any criterion is not met, revisit the relevant practice.

    line 161

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Factory Review

Value: Communication -- factory mode concentrates the human's attention on decisions that matter. This skill ensures the review interface is clear, concise, and actionable so the human can provide meaningful oversight without re-reading every line of code.

Purpose

Structures the human review experience during Phase 3 of factory-mode builds. Produces audit trail summaries, PR digests, retrospective synthesis, quality trend reports, and an autonomy tuning interface. The goal is high-signal review in minimal time.

Practices

Audit Trail Summary

Read from .factory/audit-trail/ and present a concise build summary:

  • Slices completed: Count and list (by slice ID and title)
  • Rework rate: Percentage of slices that required rework cycles
  • Gate failure distribution: Count of failures by gate type (tdd, review, ci, mutation)
  • Escalations pending: Count and brief description of each unresolved escalation

Format as a dashboard-style summary. Lead with the numbers, follow with details only if the human asks.

PR Digest

For each merged PR in the build, produce a one-paragraph summary:

  1. What changed: Feature or fix description in plain language
  2. Which slice: The vertical slice ID this PR implements
  3. Which pair: The two engineers who built it
  4. Gate results: Pass/fail for each quality gate (tdd, review, ci, mutation)
  5. Rework count: Number of rework cycles before all gates passed

Keep each digest to 3-5 sentences. The human should be able to scan all PR digests in under a minute for a typical build session.

Retrospective Synthesis

Aggregate findings from team retrospectives (stored in .factory/audit-trail/retrospectives/) and surface patterns:

  • Recurring rework causes: What kinds of issues triggered the most rework?
  • Pair effectiveness: Which pairings produced the fewest rework cycles?
  • Domain hotspots: Which areas of the domain model generated the most discussion or revision?
  • Process friction: Any team-identified impediments or improvement suggestions

Present as bullet points grouped by theme. Do not editorialize -- report what the team said.

Quality Trend Tracking

Read from .factory/audit-trail/metrics/ and present trends:

MetricCurrentPreviousTrend
Mutation score%%up/down/stable
Rework rate%%up/down/stable
Cycle time per slicedurationdurationup/down/stable
Gate failure rate by typecountscountsup/down/stable

"Previous" means the last completed build session. If no previous session exists, omit the comparison column.

Tuning Interface

Accept adjustments to .factory/config.yaml during review. For each proposed change:

  1. Validate: Check that the change is consistent (e.g., cannot enable auto-merge at conservative autonomy level; cannot disable a gate that another setting depends on)
  2. Explain implications: What will change in behavior if this setting is modified? Be specific.
  3. Apply or reject: If valid, apply the change. If invalid, explain why and suggest the nearest valid alternative.

Never apply config changes silently. Always confirm with the human before writing to .factory/config.yaml.

Escalation Review

Present each pending escalation with full context:

  1. Which gate: The quality gate that triggered the escalation
  2. Rework attempts: How many rework cycles were attempted
  3. What was tried: Brief summary of each rework attempt
  4. Current state: What the code looks like now (diff or description)
  5. Recommendation: The team's suggested resolution (if any)

The human decides: resolve, override, or send back for more rework.

Enforcement Note

Advisory in all modes. Summaries and trend reports are self-enforced formatting disciplines.

Hard constraints:

  • No silent config changes: [H]

Constraints

  • "No editorializing": Report what the team said, not what you think about what the team said. "The team discussed X and decided Y" is reporting. "The team wisely decided Y" is editorializing. "The team discussed X but missed the important point about Z" is editorializing. If you have a concern, present it separately as your own analysis, clearly labeled.
  • Config validation: "Never apply config changes silently" means every config change goes through explicit user confirmation, even if the user previously said "go ahead and configure things." Each individual change is confirmed. Blanket permission does not exist for config changes.

Verification

After completing a factory review session, verify:

  • Audit trail summary was presented with current build metrics
  • Every merged PR has a digest (no PRs skipped)
  • Retrospective synthesis covers all team retrospectives from the session
  • Quality trends include comparison to previous session (if available)
  • All pending escalations were presented with full context
  • Any config changes were validated before being applied
  • Human confirmed or overrode each escalation

If any criterion is not met, revisit the relevant practice.

Dependencies

This skill requires the pipeline skill for factory mode infrastructure. It integrates with:

  • pipeline: Reads audit trail files and metrics produced by the pipeline orchestrator during Phase 2
  • ensemble-team: Retrospective synthesis reads team retro output; pair effectiveness data comes from pairing history
  • ci-integration: Gate failure data includes CI_RESULT packets from the ci-integration skill

Missing a dependency? Install with:

npx skills add jwilger/agent-skills --skill pipeline

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.