Cycle Grade

Produce a final grade and assessment of the complete ACE cycle.

Inputs

Pipeline-only: grades one closed run end to end from that run's artifacts (§ Process).

Process

1. Read all opportunity artifacts from GDrive, including learnings summary, the PDD's archetype: field, and the PDD's ## Evidence Model section. The archetype determines which dimensions and rubric to use (see ## Archetypes below). The Evidence Model determines what "the work was done" actually means: every Layer A entry should appear as an explicit pass/fail in the FLW Performance grade evidence; Layer B/C entries should appear in the Intervention Effectiveness / Research Quality (focus-group) evidence.

2. Grade across dimensions:

   • Intervention Effectiveness (0-10) — did the intervention achieve its goals?
   • App Quality (0-10) — were the Learn/Deliver apps well-designed and functional?
   • LLO Execution (0-10) — did LLOs execute effectively?
   • FLW Performance (0-10) — did FLWs deliver quality data/services?
   • Process Efficiency (0-10) — how smoothly did the ACE process run?
   • Communication Quality (0-10) — was communication with LLOs effective?
   • Overall Grade — weighted average with narrative assessment

3. Self-evaluate (LLM-as-Judge):

   • Is the grading fair and evidence-based?
   • Are the recommendations actionable?
   • Does the grade accurately reflect the opportunity's outcomes?

4. Generate recommendations:

   • Top 3 things that went well (keep doing)
   • Top 3 things to improve (for next cycle)
   • Specific recommendations for each ACE skill that was used

5. Write final report to ACE/<opp-name>/runs/<run-id>/10-closeout/cycle-grade.md.

5.5. Write phases.closeout.products.cycle_grade to the current run's run_state.yaml so downstream readers (ace-web summary in particular — the hero status chip flips to "closed" with the actual grade letter once this lands) get the headline grade from typed state.

phases:
  closeout:
    products:
      cycle_grade:
        letter: <e.g. "A" | "A-" | "B+">
        overall_score: <weighted average from Step 2, 0-10>
        headline: <one-sentence narrative summary of the outcome>
        archetype: <atomic-visit | longitudinal-visits | focus-group | multi-stage>
        scorecard_file_id: <Drive fileId of cycle-grade.md>

Apply via mcp__plugin_ace_ace-gdrive__update_yaml_file with merge: 'deep' (partial phase-child patch — deep preserves the phase's status/steps and sibling products; two-level would drop them, #572/#587). Sole writer of products.cycle_grade.

6. Email admin group with the full cycle grade report.

Archetypes

The grading dimensions and rubric depend on the PDD's archetype: field. The 6 dimensions in step 2 above are the atomic-visit defaults. Other archetypes need different dimensions or different rubrics for the same dimensions.

atomic-visit

Use the 6 dimensions as written:

• Intervention Effectiveness, App Quality, LLO Execution, FLW Performance, Process Efficiency, Communication Quality.

FLW Performance is graded on submission volume, data quality, cap compliance, and per-FLW outliers. Intervention Effectiveness is graded on whether quantitative success metrics from the PDD were met.

longitudinal-visits

Use the 6 dimensions, with two re-anchored:

FLW Performance is graded on caseload coverage and sequence integrity, not submission volume alone — an FLW submitting steadily while their cases stall is not performing well, and volume-only grading scores that as success.

Intervention Effectiveness is graded on entity progression (did the entities move through their lifecycle as designed?), which is usually the partner's own headline indicator, rather than on submission counts.

focus-group

The same 6 dimensions, but FLW Performance and Intervention Effectiveness need different rubrics:

• FLW Performance is graded on facilitation quality — depth of probing, balance across participants, summary specificity, audio completeness, neutral framing — not submission volume. Number of sessions facilitated is a floor (did they hit the planned count), not a quality signal.
• Intervention Effectiveness is graded on research yield — did the focus groups produce findings that meaningfully informed the next stage or the team's understanding? Are the themes specific, segment-differentiated, and supported by quotes? Did the PDD's research questions get answered? This is a qualitative grade and the LLM-as-Judge should quote actual session content as evidence.

Add a 7th dimension for focus-group:

• Research Quality (0–10) — Were the session outputs of high enough quality to be the input to a downstream synthesis or decision? Are themes triangulated across segments? Is there visible saturation? Are quotes attributed and verbatim?

multi-stage

Grade each stage's archetype separately, then produce an overall cycle grade that considers the stage-gate transitions as well: did Stage 1's findings actually flow into Stage 2 design? Was the stage gate honored or skipped? This is a process-quality signal that doesn't appear in any single stage's grade.

MCP Tools Used

• Google Drive: drive_read_file, drive_create_file, drive_list_folder

Mode Behavior

• Auto: Generate grade, email report, mark opportunity as closed
• Review: Present grade for team review and discussion

Dry-Run Behavior

When --dry-run is active:

• Grade report is still generated and written to GDrive as normal
• Write the admin group email (recipients, subject, body with grade report) to comms-log/dry-run-cycle-grade.md instead of sending
• State tracks as dry-run-success

Change Log

Date	Change	Author
2026-04-08	Add ## Archetypes section: focus-group grading uses facilitation-quality and research-yield rubrics for FLW Performance / Intervention Effectiveness, plus a 7th Research Quality dimension; multi-stage grades stage-gate transitions	ACE team (PM scout, focus-group framework lens)
2026-04-08	Read PDD ## Evidence Model in step 1; Layer A drives FLW Performance evidence, Layer B/C drive Intervention Effectiveness / Research Quality evidence	ACE team (PM scout, focus-group framework lens)