Define done

Define Done

Pin an explicit, outcome-based Definition of Done for a diamond. "Done" is a change in human behaviour that creates value (Seiden), not "the feature shipped" — you're done when it ships and has the intended impact (Amplitude). The diamond's implicit done defaults to the harshest, least-controllable bar ("a user shipped a product"), which is both wrong for validating purpose and a demotivation engine. This skill makes the bar explicit, measurable, and pre-committed.

This is distinct from /mycelium:definition-of-done (the per-feature agile quality checklist run at Deliver→Complete). This skill sets the outcome bar a whole diamond is judged against; that one verifies a feature meets quality criteria. Both can apply.

When to use

• At diamond birth — /mycelium:interview (L0) and child-diamond spawn (/mycelium:diamond-progress) call this before a diamond is "live."
• Retrofit — when /mycelium:canvas-health, /mycelium:diamond-assess, or the SessionStart nudge flags a diamond with no definition_of_done.
• Never silent-fill. The question is what produces a real bar ("fits, not ships"); a back-filled field is theatre.

Preflight: Read target file before any Write/Edit

Hard rule. The definition_of_done field lives on the diamond in .claude/diamonds/active.yml. Before Write/Edit on it, use the Read tool on that file this session (cat/head/grep via Bash do NOT satisfy Claude Code's check). Edit with limit:1 suffices for a partial update; full Read before a Write. See CLAUDE.md Canvas writes — Read before Write.

The sequence (Socratic, problem-first)

Ask these in order. The teaching is in the sequencing (problem → signal → maybe-number → kill) and the good/bad contrast — not a help doc. Reject build-lists at step 1; that is the whole point.

1. "When this is done, what's different — and for WHOM?" → outcome + whose behaviour. Lead with the problem and the person, never the deliverable (Cagan: "most measurement problems are clarity problems").

• ✗ "the onboarding flow ships." (a build-list — reject it)
• ✓ "non-fluent users get through the brief without hitting the vocabulary wall."

2. "What's the ONE thing you'd SEE them DO that proves it?" → signal (One Metric That Matters — one, not many).

• ✗ "it feels better."
• ✓ "they came back for a second session."

3. (optional) "Is there a point where it's ENOUGH?" → threshold, only if one genuinely fits. A number is optional, not mandatory — directional outcomes are legitimate. When you do set one, pair it with a qualitative guard ("3 warm bodies who bounce ≠ done") so it can't be gamed (Goodhart).

4. Pre-mortem for the kill-criterion. Ask in the past tense: "It's [review date]. This diamond failed. What happened?" (premortem finds ~30% more real failure reasons than "might it fail?" — Klein). From the answer derive a concrete state + date that means kill, not finish:

• state — an objective benchmark that means this goal is wrong.
• date — the review date by which the state must hold. Pre-commit BOTH, before the data (anti-HARKing). Invalidation-with-evidence at that date is a legitimate "done" — routed through /mycelium:diamond-progress kill + dogfood-mode, not silently declared.

Per-scale "done" + lead/lag defaults

Default kind by altitude: lower/activity scales take leading measures (predictive, team-influenceable); higher value-validation scales take lagging (already realized). Direct most attention to lead measures (4DX).

Scale	"Done" means	kind default
L0 Purpose	people keep choosing it ("fits, not ships")	lagging
L1 Strategy	a where-to-play bet validated	lagging
L2 Opportunity	a real user need confirmed worth solving	mixed
L3 Solution	the solution actually solves it for users (not just built)	leading
L4 Delivery	shipped AND has the intended impact	leading
L5 Market	adoption / business outcome at scale	lagging

Build-mode gate — refuse build-to-earn during build-to-learn

The per-scale table isn't only a kind default — it encodes the build mode (Patton/Cagan). This skill enforces it at diamond birth (agent-adjudicated), with a /canvas-health lint backstop that fires unconditionally (step 8c there). This is the mechanism behind philosophy.md's "the framework refuses to let the agent build-to-earn during a build-to-learn phase — that refusal is the gate."

• Build-to-LEARN (a diamond whose terminal purpose is to learn — scales L0 Purpose → L3 Solution): its DoD outcome must be a learning / validation result ("the bet is validated", "the need is confirmed worth solving", "the solution actually solves it for users").
• Build-to-EARN (terminal purpose is to ship value — L4 Delivery, L5 Market): its DoD outcome is a shipped-value / adoption result ("shipped AND the intended impact landed").

The discriminator is NOT the word "ship" — it is what the done-bar IS. Discovery legitimately ships to learn: a fake-door test, wizard-of-oz, a disposable prototype shipped to a few opted-in users. Those are build-to-LEARN — the ship is the method, the learning it yields is the done-bar ("40% of the 5 opted-in users returned = demand validated"). Do NOT reject those.

The gate — reject a build-to-LEARN diamond's DoD only when it is genuinely earn-shaped, i.e. either:

• the ship itself is the done-bar (no learning behind it — "the flow is deployed" as the terminal criterion), OR
• scope is all-users / permanent / production-hardened rather than select opt-in (reuse G-M2's opted-in-vs-all-users line).

Then say: "That's build-to-earn on a build-to-learn diamond — you haven't earned delivery yet (Patton/Cagan). What will this ship let you LEARN? Make the learning the done-bar, keep the ship disposable / opt-in. Production rollout is the L4 outcome, earned after discovery validates." This is distinct from the step-1 build-list rejection: step 1 catches "the flow ships" as a non-outcome; this catches a well-formed but premature earn-bar. Conversely, a build-to-EARN (L4) diamond whose outcome is pure learning with no value/impact → prompt to reframe to earn ("shipped AND moved [signal]").

Boundary note (mode is per-phase; the DoD bar is per-diamond). The DoD keys to the diamond's terminal purpose (its scale), but build mode shifts across a diamond's phases: even an L4 Delivery diamond has an early Discover/Develop phase (a spike, "how should we build this?") that is build-to-learn — don't force its early spike into an earn-bar. And a build-to-EARN L4 child under a build-to-LEARN parent ships (earn) yet rolls up to the parent's learning (rolls_up_to). philosophy.md frames the line by phase (Discover/Develop divergent = learn; Define/Deliver convergent = earn); this skill keys the DoD outcome to the diamond's terminal altitude — the same distinction, applied to the artifact each governs.

Scenarios — happy: L4 "shipped X and retention rose" ✓; L1 "the where-to-play bet is validated" ✓; L2 "fake-door: 40% of opted-in users clicked through = demand validated" ✓ (ships, but to learn). sad (redirect): L2 "ship the onboarding flow" (ship = the bar, no learning) → reframe to the need it must confirm first. bad (blocked): L0/L1 "deploy production auth to all users" → earn-during-learn, the "earn the right to start" gate firing.

Ladder rule (contribution-not-summation): a child diamond is done only when its outcome rolls up to move the parent outcome / validate the parent assumption — not just because the child shipped. A child that doesn't roll up is discarded, not summed. Set rolls_up_to on every child.

Write the field

Write definition_of_done onto the target diamond in .claude/diamonds/active.yml:

definition_of_done:
  outcome:   "<what changes, for WHOM — a behaviour, not a feature>"   # required; problem-first
  signal:    "<the ONE observable thing you'd see them DO>"            # required; OMTM
  kind:      leading | lagging                                        # defaulted by scale above
  threshold: "<a target IF one genuinely fits>"                       # OPTIONAL (numbers not mandatory)
  measure:                                                            # OPTIONAL — makes `signal` checkable post-ship; closes the outcome→discovery loop (/metrics-pull reads this)
    source:      "<metric adapter key (github, plausible, …) OR 'manual' for interview/observation outcomes>"
    field:       "<snapshot field path for automated sources; omit for source: manual>"
    target:      "<value or direction that means the outcome landed — reuse `threshold` if already set>"
    check_after: "<lag before checking, as Nd or Nw after ship (e.g. 14d, 8w) — matched to `kind`. Per-DoD; consumed by /metrics-pull's lag gate + the overdue nudge. Default 14d if unset>"
    guardrail:   "<counter-metric that must NOT worsen while `signal` improves — Goodhart guard (advisory: downgrades a 'met' to 'met-with-regression')>"
    last_checked: null                                                # stamped by /metrics-pull when the outcome-check runs
  rolls_up_to: "<parent diamond id + which parent outcome this serves>"  # child diamonds only
  kill_criterion:
    state: "<concrete benchmark that means this goal is WRONG>"
    date:  "<YYYY-MM-DD review date by which state must hold>"
    premortem: "<the failure it was generated from>"
  provenance: { source_class: internal_stakeholder, validated: false, captured_at: "<YYYY-MM-DD>" }

outcome and signal are required; everything else is optional or scale-defaulted. At L0/L1 birth from the brief, a one-line outcome + signal stub is enough — depth comes from re-running this skill.

measure — closing the outcome→discovery loop (optional)

Setting a DoD target and never checking whether it landed leaves the back half of the loop open (Kim's Second Way: outcomes must flow back). The optional measure block makes signal checkable after ship, so /metrics-pull can compare target-vs-actual and route the result back to discovery (met → confidence up / scale; missed → reopen the opportunity). Design notes, all evidence-grounded:

• source: manual is first-class, not a fallback. Many real outcomes — especially early, and any that depend on real users experiencing the thing — are interview/observation-based, not dashboard numbers. measure must NOT push you to pick the number you can automate over the outcome you care about (the metric-availability trap). Manual outcomes get prompted, not faked.
• check_after is per-DoD, never a global window. Outcome lag varies by what you measure (engagement lands in days; retention in weeks or months). Match it to kind — leading measures check sooner, lagging later — long enough to capture real signal, short enough to still act on it. Written as Nd/Nw; /metrics-pull skips the check (no false "missed") and the nudge stays quiet until it elapses.
• guardrail is the Goodhart guard (advisory, with teeth). When signal becomes a target it degrades; pair it with a counter-metric that must not worsen. It doesn't block, but /metrics-pull downgrades a "met" to "met-with-regression" when the guardrail worsened as the signal improved. Five metrics are harder to game than one.
• Omit measure entirely for a directional outcome with no meaningful check — that stays valid.

Failure-mode guards (baked in)

• No checklist theatre — step 1 rejects "what you built"; the field is an outcome.
• No Goodhart — number optional; when set, pair with a qualitative guard.
• Kill-path honesty — done-by-invalidation requires the pre-committed state+date to have actually fired with evidence at the scheduled gate, logged via dogfood-mode + decision-log. It is not a way to declare failed work done.
• Problem-first sequencing prevents the metric-availability trap (picking the number you can measure over the outcome you care about).

Provisional wording

The exact prompt phrasing above is provisional — only the why → who → how-should-behaviour-change → what scaffold (Impact Mapping) survived research verification; the specific wording did not. Validate it with /mycelium:prompt-optimizer A/B rather than treating the phrasing as settled. Design + evidence grades: docs/design/definition-of-done.md.

Theory Citations

• Seiden (Outcomes over Output): done = a behaviour-change that creates value, not a feature shipped.
• Cagan (Inspired): problem-and-whom first; measurement problems are clarity problems.
• Amplitude (North Star Playbook): shipped AND has impact.
• Adzic (Impact Mapping) / Microsoft Research: contribution-not-summation laddering; why→who→how→what.
• 4DX (McChesney) / Lean Analytics (Croll & Yoskovitz): lead-low / lag-high; One Metric That Matters.
• Duke (Thinking in Bets) / Klein (premortem) / pre-registration: state+date kill-criteria, pre-committed, generated by pre-mortem.
• SAFe (Epic Hypothesis Statement): done = hypothesis confirmed OR invalidated-and-cancelled.