trainingsites/campus-ai-os/results-aggregator

Claude Code and agent skill by trainingsites/campus-ai-os

Trust tier 3
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

Reads every execution team's results.json ledger (the BACK SOCKET) for the Strategy and Research Team (Atlas) and aggregates them by run_id into a roll-up. Internal skill, invoked by outcome-reporter. Tolerates pre-spec ledgers.

Install

npx vskill@latest install trainingsites/campus-ai-os/results-aggregator

SKILL.md

results-aggregator — read the back socket

Kernel preamble §5 applies: references/kernel-preamble.md.

Internal. Scans the handoff location for every team's results.json, parses them, and groups by run_id so one topic's whole journey across teams is one row. It NEVER rewrites another team's ledger — append-only, per-team (Team Interop Spec v1 §3).

CRITICAL DIRECTIVES

  • Tool-agnostic; zero hardcoded paths. Read-only over the ledgers.
  • Tolerate pre-spec ledgers: missing run_id → mint from slug; missing schema_version → assume "1.0". Never fail on an older shape.
  • The envelope is ONE file: schemas/results.schema.json (v1.1 = assets[] + run-level completion + optional bindings[]). Classify, don't guess: python3 tools/validate_results.py --campus-root . --json returns each ledger as v1.1-valid / v1.1-invalid / legacy-1.0 / ledger / foreign / malformed. Aggregate v1.1-* and legacy-1.0; skip ledger/foreign (job logs, not envelopes); report malformed and v1.1-invalid counts in one line, never silently.

Steps

  1. Resolve the handoff location: Connected → outputs/{YYYY-MM}/runs/**/results.json; standalone/peer → ./teams-shared/runs/**/results.json. Read both if both exist.
  2. Parse each ledger: run_id, team, slug, assets[] (type, status, tier, url, optional performance), run-level completion (three booleans; absent on asset-only 1.0 ledgers — derive nothing, report "no completion"), and bindings[] when present (who ran each stage — carry employee/employee_type into the roll-up so outcomes attach to the employee that produced them).
  3. Group by run_id. For each topic: list teams_touched, count assets_total / assets_published, and roll up any reported outcomes (opens, clicks, attendance, replies, completion, sales).
  4. Pull live performance where a connector is present and capability-gated (sales_stats, list_campaigns, course_analytics…); otherwise leave outcomes at user-reported or zero and flag no-data.
  5. Cross-reference brief score if the matching brief.json is on disk (adds brief_score).
  6. Hand the grouped structure to outcome-reporter to verdict and write the roll-up.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
51
Repository forks
10
7-day trend score
25

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/trainingsites/campus-ai-os/results-aggregator/badge)](https://verified-skill.com/skills/trainingsites/campus-ai-os/results-aggregator)

Something wrong with this skill or its page? Tell us.