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technomaton

@technomaton source repo

5 published skills

  1. ▌
    Add · technomaton
    Create a new backlog item (Initiative / Epic / Feature / Story / Defect / Event / Risk) — V2 local-first. ID allocated from id_counters.yaml, parent hierarchy validated by MCP edpa_item_create, YAML written under .edpa/backlog/, auto-committed. No GitHub calls at create time; PR-derived signals arrive separately via the contribution-sync workflow.
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  2. ▌
    Setup · technomaton
    Initialize EDPA V2 governance for a project. Vendors the engine (scripts + schemas + templates) into `.edpa/engine/`, creates `.edpa/config/{edpa.yaml,people.yaml}`, seeds id_counters.yaml, and optionally copies the PR-signal CI workflow + registers git hooks (lefthook-aware; wires one `extends:` line instead of clobbering managed hooks). No GitHub Project provisioning (V1 path removed in 2.0.0).
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  3. ▌
    Engine · technomaton
    Run EDPA evidence-driven calculation for an iteration by invoking the vendored engine script (.edpa/engine/scripts/engine.py). The engine reads the materialized evidence[]/contributors[] persisted in each item's YAML (written by the post-commit hook / /edpa:materialize — it does not scan git at compute time), computes CW from cw_heuristics, calculates Score and DerivedHours, validates invariants, and writes results JSON + XLSX + a frozen snapshot. Use when closing an iteration, computing derived hours, or running "EDPA výpočet". Produces the input for the reports skill.
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  4. ▌
    Reports · technomaton
    Generate EDPA timesheets and PI summaries by invoking the vendored reports script (.edpa/engine/scripts/reports.py). Renders per-person timesheet-<person>.md files and the timesheet-team.md rollup from engine results, plus pi-summary-<PI>.md aggregation in --pi mode. Use when user asks for "reports", "výkazy", "timesheets", or "PI summary". Requires /edpa:engine results (edpa_results.json) as input.
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  5. ▌
    Autocalib · technomaton
    Auto-calibrate EDPA CW signal weights using the Monte Carlo + coordinate-descent optimizer (v1.11+). One target file (cw_heuristics.yaml.tmpl), one metric (MAD on a synthetic corpus), two phases (random sample → coordinate descent). Use when: user says "calibrate CW", "auto-calibrate", "optimize heuristics", "recalibrate signals". Synthetic corpus — runnable any time, no ground-truth file required. Re-run after a real PI close once team-confirmed CW corrections are available (see "Re-run with real data" below).
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