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walidgdg1-ai/tender-engine

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This repository contains the live tender discovery, procurement-document retrieval, and DCE-aware qualification workbench.

개요

This repository contains the live tender discovery, procurement-document retrieval, and DCE-aware qualification workbench. broad official discovery -> normalize/dedupe -> GPT wide read -> DCE candidate queue -> dynamic portal-aware fanout -> DCE download -> recursive extraction -> manifest + SHA-256 -> mandatory-gate deep read -> final rescore Valuable harvested data is persisted durably before a worker is allowed to finish: - discovery source packs -> GitHub Release assets; - original downloaded DCE/RFQ/RFT files -> per-candidate GitHub Release assets; - candidate metadata, manifests, SHA-256 inventory, corpus and gate snippets -> same canonical candidate pack; - consolidated deep-review outputs -> final Release assets. Recursive unpack copies are not duplicated durably when they can be reconstructed exactly from the preserved original archive. Unique harvested source files are never deleted merely to reduce Actions artifact storage.

README

Public Tender Intelligence — Lean Procurement Engine v2

This repository contains the live tender discovery, procurement-document retrieval, and DCE-aware qualification workbench.

Core flow

broad official discovery -> normalize/dedupe -> GPT wide read -> DCE candidate queue -> dynamic portal-aware fanout -> DCE download -> recursive extraction -> manifest + SHA-256 -> mandatory-gate deep read -> final rescore

Storage contract

GitHub Actions artifacts are not the canonical data store.

Valuable harvested data is persisted durably before a worker is allowed to finish:

  • discovery source packs -> GitHub Release assets;
  • original downloaded DCE/RFQ/RFT files -> per-candidate GitHub Release assets;
  • candidate metadata, manifests, SHA-256 inventory, corpus and gate snippets -> same canonical candidate pack;
  • consolidated deep-review outputs -> final Release assets.

Recursive unpack copies are not duplicated durably when they can be reconstructed exactly from the preserved original archive. Unique harvested source files are never deleted merely to reduce Actions artifact storage.

High-throughput execution

DCE fanout:

queue -> deterministic dedupe -> browser/HTTP split -> up to 20 independent shards -> bounded local worker concurrency -> durable Release persistence -> compact handoff -> atomic canonical aggregate

Current defaults permit up to 20 GitHub-hosted shard runners when the queue and account-wide concurrency allow it. Browser work stays conservative inside each runner while HTTP-only shards can use higher local concurrency.

Discovery fanout:

TED + Contracts Finder + 10 eTenders Ireland page-range shards -> durable source packs -> canonical merge/dedupe -> GPT wide-read packets

The workflows reuse the preinstalled Chrome on ubuntu-latest and install Playwright only on browser-backed shards. Legacy/debug probes are manual-only.

Autonomous fleet

Persistent orchestration is controlled by control/desired_state.json and .github/workflows/autonomous-fleet.yml.

pipeline/fleet_controller_v3.py reconciles live capacity, transactional DCE leases, durable backlog state and provider refreshes. Failed or expired leased DCE batches return to the queue instead of being silently consumed.

CircleCI is integrated as a second disposable compute pool through .circleci/config.yml. Its 30-way fanout is intentionally fail-closed until FLEET_GITHUB_TOKEN is configured in the CircleCI project so every valuable worker result can be persisted to the same canonical GitHub Release path.

pipeline/setup_circleci_bridge.py is the one-time bootstrap helper for installing/replacing that CircleCI project environment variable when CIRCLE_TOKEN and FLEET_GITHUB_TOKEN are present locally.

Super Green operating sources

  • skills/supergreen-hunt/SKILL.md — authoritative GPT/agent contract for semantic wide-read, DCE-final-gate reasoning and final GREEN / SUPER GREEN classification.
  • skills/autonomous-tender-fleet/SKILL.md — authoritative orchestration contract for continuous mode, capacity, leases, persistence, CircleCI, recovery and GPT handoff.
  • docs/SUPERGREEN_SYSTEM_V2.md — implemented architecture, executors, handoffs and measured benchmarks.
  • docs/SUPERGREEN_HUNT_RUNBOOK.md — reproducible runbook capturing the process used to surface and verify fresh opportunities such as Barnagh.
  • docs/PARALLELIZATION_AUDIT.md — original performance audit and optimization rationale.
  • DCE_ROUTE_MATRIX.md — procurement portal route/status matrix.
  • portal_routes.py — machine-readable portal registry.

Integrity rules

  • No fabricated eligibility facts.
  • UNKNOWN stays UNKNOWN.
  • Login/MFA/CAPTCHA barriers return explicit non-success states.
  • Canonical dedupe/freshness happens before expensive verification.
  • Every shard owns independent output paths; workers never concurrently mutate one shared output file.
  • Aggregation/deduplication is deterministic and single-writer.
  • Production workers do not concurrently git push harvested data.
  • A durable Release upload is verified before the worker is considered successful.
  • GPT materially reads the broad generated live pool before the DCE shortlist is finalized.
  • No FINAL SUPER GREEN score (90+) without verified mandatory gates from the DCE/RFQ/RFT or equivalent authoritative procurement pack.

Key workflows

Production:

  • .github/workflows/supergreen-discovery-v2.yml
  • .github/workflows/dce-fanout-v2.yml
  • .github/workflows/autonomous-fleet.yml
  • .circleci/config.yml

Preservation/maintenance:

  • .github/workflows/migrate-actions-artifacts.yml — verify-before-delete migration from legacy Actions artifacts into durable Release assets.
  • .github/workflows/artifact-gc.yml — inventory-only; it does not delete harvested data.

Legacy probes are workflow_dispatch only.

Key executors live under pipeline/.

View this README on GitHub

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