Engine

EDPA Engine — Evidence-Driven Calculation

What this does

Computes derived hours for all team members for a given iteration by running the deterministic engine script. Per ADR-003 ("heavy compute / file generation → directly script"), this skill is a thin wrapper: it resolves the iteration argument, shells out to .edpa/engine/scripts/engine.py, and interprets the output. It never re-implements the calculation.

One successful run writes, under .edpa/:

Arguments

$ARGUMENTS = iteration ID (e.g., "PI-2026-1.3") or "latest" for most recent closed iteration.

Argument resolution (when $ARGUMENTS is empty)

If $ARGUMENTS is empty, blank, or "help":

1. Call MCP tool edpa_iterations (or read .edpa/iterations/*.yaml directly). PI/iteration timeline data is reconstructed at runtime from those per-PI and per-iteration YAML files — edpa.yaml no longer carries pis[].
2. Present available iterations with status and dates:

   Available iterations:
     PI-2026-1.1  [closed]   2026-04-06–2026-04-17
     PI-2026-1.2  [closed]   2026-04-20–2026-05-01
     PI-2026-1.3  [closed]   2026-05-04–2026-05-15
     PI-2026-1.4  [active]   2026-05-18–2026-05-29  <-- suggested
     PI-2026-1.5  [planned]  2026-06-01–2026-06-12  (IP)
   

3. Default suggestion: the iteration with status: active. If none is active, suggest the latest closed.
4. Ask user: "Which iteration to compute? [suggested-id]"
5. If user confirms or provides an ID, proceed. If .edpa/config/edpa.yaml does not exist, inform user to run /edpa setup first.

Prerequisites

• .edpa/config/people.yaml exists (run /edpa:setup first)
• .edpa/config/cw_heuristics.yaml exists (seeded by project_setup.py; a legacy heuristics.yaml is still accepted as fallback)
• Backlog items carry js: (Job Size) in their YAML — V2 keeps Job Size in the backlog files, not in any GitHub field
• The iteration has Done stories/defects/tasks and/or recorded gate transitions
• Evidence is materialized in the item YAML (see "Where the signals come from" below)

Run the engine

python3 .edpa/engine/scripts/engine.py --edpa-root .edpa --iteration <iteration-id>

Never hand-compute. Do not load config with inline Python, do not hand-calculate scores or hours, and do not hand-write edpa_results.json. The script is the single deterministic implementation — it enforces the invariants, stamps the methodology version into results and snapshot, and produces the XLSX + content-hashed snapshot the audit trail relies on.

Useful variants:

# Setup doctor — what is configured, what is missing (read-only)
python3 .edpa/engine/scripts/engine.py --status

# Worked example with built-in sample data (writes no files)
python3 .edpa/engine/scripts/engine.py --demo

# Explain one person's allocation from already-computed results
python3 .edpa/engine/scripts/engine.py --edpa-root .edpa \
  --iteration <iteration-id> --explain <person-id> [--explain-item <item-id>]

--output <path> overrides the results JSON path.

Interpret the output

1. Summary table — stdout ends with a per-person summary (capacity, derived hours, items, invariant status). Relay it to the user.
2. Invariants — on a hard invariant failure the engine reports it and exits 1 (all_invariants_passed: false in the JSON). Report which check failed; never "fix" numbers by hand.
3. Snapshot line — Snapshot frozen: … on first freeze; refreshed (same content, frozen_at updated) on an identical rerun; new revision (content changed); previous: <ID>.json when inputs changed. Frozen snapshots are immutable — changed reruns create <ID>_rev<N>.json instead of overwriting.
4. Excel export skipped (install openpyxl for XLSX output) — XLSX needs openpyxl; the JSON results and snapshot are unaffected.

Then:

• Suggest /edpa:reports <iteration-id> to render per-person timesheets.
• Nothing is auto-committed. The engine only writes files; commit the generated reports/ + snapshots/ outputs as part of the iteration-close batch.

Background — what the script computes

For reference when explaining results (the script does all of this; you never re-do it):

• Pure reader. The engine reads the materialized evidence[] / contributors[] blocks persisted in each item's YAML. It does not scan git (or call gh) at compute time — so the report equals the persisted state, deterministically, on any machine.

• CW comes from detect_contributors.py aggregation: additive signal weights from cw_heuristics.yaml (contribution_score[P, item] = Σ signal_weight), normalized per item so Σ_persons cw[*, item] = 1.0. Manual /contribute weights stack additively. Role labels (owner/key/reviewer/consulted) are display-time projections, never stored.

• Score per person P:

  Score[P, done_item]  = JobSize[item]   × CW[P, item]                  # Story / Defect / Task at Done
  Score[P, gate_event] = JobSize[parent] × gate_weight × CW[P, parent]  # Feature/Epic/Initiative transition
  Score[P, activity]   = JobSize[story]  × credit_factor × CW[P, story] # in-flight Story yaml_edit activity
  

  When git history records no transitions and no yaml_edit activity, only Done-item credit fires — the calculation degenerates gracefully to Done-only behaviour (the pre-v1.14 --mode selector is gone).

• Hours: DerivedHours[P, item] = (Score[P, item] / Σ Score[P, *]) × Capacity[P].

• Invariants (hard ones halt the run): Σ DerivedHours[P, *] = Capacity[P] ± 0.01, share ratios sum to 1.0, no negative hours. Missing Job Size warns and skips the item.

Where the signals come from (not the engine's job)

The post-commit hook (local_evidence.py) materializes commit_author, /contribute, yaml_edit, and state_transition signals as each commit lands. To backfill history, commits made with EDPA_NO_LOCAL_EVIDENCE=1, or signals from another machine, run /edpa:materialize (MCP tool edpa_materialize, or local_evidence.py --materialize --iteration <id> / --all-iterations) — idempotent, deduped by ref. PR-thread signals (pr_reviewer, issue_comment) are materialized by the optional edpa-contribution-sync CI workflow. The engine then just reads the result.

Error handling

• Script errors with --edpa-root or (--iteration + --capacity + --heuristics) required → always pass --edpa-root .edpa in a V2 project (the flag has no default).
• PI id instead of iteration id (e.g. "PI-2026-1" not "PI-2026-1.3") → the engine refuses: a PI label would silently drop every item tagged <pi>.N. Use /edpa:close-pi <PI> for PI rollups.
• No items in iteration → "No closed items found for {iteration}. Check iteration label."
• Missing Job Size → warn per item, excluded from calculation.
• Person with 0 relevant items → 0h derived (process issue, not math issue).
• Evidence missing / contributors empty → the engine does not scan git to recover it; run /edpa:materialize <iteration> to persist the signals into evidence[], then re-run the engine.