Feedback to Jobs

Raw feedback is a list of feature requests in disguise. This skill reads the pile, finds the jobs underneath it, and hands you a drafted Job Spec per job: the durable artefact the rest of the system runs on.

It does the thinking with judgement, not a parser. There are no scripts: you read the feedback, cluster by job, rate, and draft. The references hold the structure so the output is consistent run to run.

When to use this

• You have a folder of feedback items (one file or one line each) and no map of what it’s about.
• You keep getting feature requests and suspect several of them are the same job wearing different clothes.
• You need to ground a roadmap in jobs, and you need the Job Specs that The Org as an API and Agentic Delivery assume already exist.

The move

The whole skill is one reframe, applied at scale: stop reading requests, start reading jobs.

A request says what the customer asked for. A job says what they were trying to get done, and why they can’t. Ten requests for “more export options” and “a CSV button” and “send me the data” are one job: get my data out so I can work with it elsewhere. Cluster on the job, and the pile collapses from forty items to five real things.

Input

Point the skill at a folder of feedback. Each item is short: a sentence or a paragraph. Sources don’t matter and aren’t kept: a support line, a sales note, a survey row, a call snippet all reduce to the same thing once you read for the job. Strip names and identifiers as you go; the output is about jobs, not accounts.

feedback/
  001.md   "Can't see who's free across the team at a glance"
  002.md   "Need a combined availability view for all staff"
  003.md   "Export the monthly roster to CSV"
  ...

The workflow

1. Read every item for the job, not the ask

Go through the pile once. For each item, write down, in your head or a scratch list, the job it implies: when I [situation], I want to [motivation], so I can [outcome]. Ignore the feature named. If an item is too thin to imply a job, set it aside; don’t invent one.

2. Cluster by job

Group items that serve the same job, even when the words differ. “One screen for everyone’s slots” and “combined availability view” are the same job; “export the roster” is a different one. Aim for a handful of jobs, not dozens: if you have twenty clusters, you’re still clustering on features. Each cluster becomes one theme.

3. Write a theme file per cluster

For each cluster, write a theme using the structure in references/theme-structure.md. The theme frontmatter carries the signal at a glance:

• feedback_count — how many items rolled in
• severity — how badly the job is blocked (high / medium / low)
• trend — growing / stable / declining
• strategic_alignment — aligned / neutral / misaligned

Rate each with references/rating-criteria.md. The body names the jobs, why they’re hard today, what people do instead (workarounds reveal real severity), and what made it urgent now (switching triggers).

4. Split themes that got too big

A theme past ~20 items is usually two jobs hiding in one. When feedback_count crosses the threshold, look for the seam: distinct jobs, distinct situations, things that would ship independently. Split into focused sub-themes and redistribute the items. A theme that’s just “lots of feedback about area X” isn’t a job. It’s a folder.

5. Draft a Job Spec per theme

For each theme, draft a Job Spec: the standing job / outcome / stakes contract, its Contribution to the outcome it serves, what the product must be able to do, the observable good / bad, and the “Prove it” scenarios. This is the artefact that outlives the theme: the theme captures demand, the Job Spec defines done.

Keep account and revenue evidence out of the Job Spec. That join is a separate concern (see Job Links); the Job Spec stays clean and durable.

Note

These drafts are unratified. They were written in batch, from feedback, without the one-question-at-a-time interview — which means they carry your inferences, not confirmed agreement. Before anything downstream treats one as a contract (an RFC serves: it, a pillar funds it), close the gaps with the user via create-job-spec’s interview and pass it through review-doc. A drafted spec that skips ratification is exactly the assumptions-anchored spec the method warns about.

Output

themes/
  see-everyones-availability.md     ← theme: jobs, severity, trend, workarounds
  export-the-roster.md
  reliable-booking-comms.md
jobs/
  see-everyones-availability.job.md ← Job Spec, drafted
  export-the-roster.job.md
  reliable-booking-comms.job.md
clusters.md                         ← which raw items rolled into which job (the audit trail)

clusters.md is the receipt: it shows your work, so anyone can check whether item 17 really belongs under “export the roster” or got mis-grouped.

What this skill is not

• Not a sentiment counter. It doesn’t tally thumbs-up; it reframes requests into jobs.
• Not a prioritiser. It tells you what the jobs are and how loud each is. Sequencing the roadmap is a later step; see The Org as an API.
• Not automated. The clustering is judgement. A parser can count items; only a reader can tell that “CSV button” and “send me the data” are the same job.

Related

• Job Spec — the artefact this skill drafts
• create-job-spec — the interview that ratifies a draft into a contract
• review-doc — the fresh-process gate a draft passes before anything anchors to it
• JTBD Guide — how to frame a job well
• The Org as an API — what to do with the jobs once you have them