HR & RECRUITING

Assemble Interviewer Scorecards into a Hiring-Decision Packet

When the final interview task closes in Asana, this collects every interviewer's scorecard, computes a weighted average and consensus signal.

CategoryHR & Recruiting
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFinal-round task marked complete in AsanaAsanaAsana
  • ActionFetch all interviewer scorecard subtasks for candidateAsanaAsana
  • LogicCompute weighted score, recommend tally, and competency spread
  • LogicFlag split panel if variance exceeds threshold
  • OutputPost decision packet to hiring manager in SlackSlack

What it does

Watches the candidate's Asana project for the "Final round complete" task to be marked done, then pulls all interviewer scorecard subtasks attached to that candidate. It normalizes each rating, computes a weighted overall score and a recommend/no-recommend tally, flags any score variance above your threshold as a split panel, and delivers a clean decision packet to the hiring manager.

When to use it

Use this when interviewers log structured feedback as Asana subtasks and your hiring manager currently has to open five tabs to reconstruct where a candidate stands. It turns scattered scorecards into one timestamped, auditable summary the moment the loop is closed.

How it works

  1. 1Asana fires when the final-round task is completed.
  2. 2Fetch all scorecard subtasks for the candidate and parse each interviewer's rating fields.
  3. 3Compute the weighted average, recommend tally, and per-competency spread.
  4. 4If score variance exceeds the split-panel threshold, branch to flag the disagreement for a debrief.
  5. 5Post the consolidated decision packet to the hiring manager's Slack channel with a clear recommend/hold/reject signal.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AsanaTasks, projects, milestones — everywhere.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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