/ice-score

name: ice-score
description: "Use to prioritize solutions or opportunities using ICE scoring with evidence-backed confidence."
metadata:
  instruction_budget: "35"
  framework_dependency: "mycelium"
  framework_dependency_note: "This skill is designed to run within the Mycelium framework (https://github.com/haabe/mycelium). Standalone use will skip the canvas state, theory gates, and harness behavior the skill assumes. Install: /plugin install mycelium@haabe-mycelium."

ICE Score Skill

ICE scoring with integrated confidence meter.

Preflight: Read target canvas file(s) before any Write/Edit

**Hard rule.** Before issuing `Write` or `Edit` against any `.claude/canvas/*.yml`, use the **Read tool** on that file in this session. Claude Code's Read-before-Write check requires the `Read` tool specifically — `cat`/`head`/`grep` via Bash do NOT satisfy it.

**Edit vs Write — different cost profiles** (verified 2026-05-14):

• **`Edit`** (exact-string replacement): `Read` with `limit: 1` satisfies the check at ~50 tokens. State-tracking is per-file, not per-byte — subsequent `Edit` calls work anywhere in the file. Use this for partial updates against large canvas files (e.g., `purpose.yml` at 800+ lines).
• **`Write`** (full replacement): do a **full Read** first. Write obliterates the file; you should see what you're about to replace. The `limit:1` shortcut is *not* appropriate here.

**ID-bearing entries — scan the ID space before assigning** (added 2026-05-15, v0.23.19): When adding a new component, opportunity, solution, or any other ID-bearing entry to a canvas file, run a Bash grep first to confirm the next ID in your prefix sequence is actually free:

grep -o "<prefix>-[0-9][0-9]*" .claude/canvas/<file>.yml | sort -u -t- -k2 -n | tail -3

Replace `<prefix>` with the canvas's ID prefix (`comp` for landscape, `opp` for opportunities, `sol` for solutions, `ht` for human-tasks, etc.). Then pick the next free integer, **matching the zero-padding already used in that file**. The sort is NUMERIC (`-t- -k2 -n`) rather than lexical, and that is not pedantry: a plain `sort -u` orders `ht-1` after `ht-080`, so on a canvas with inconsistent padding it reports the wrong maximum and the next ID collides. Verified on the dogfood repo 2026-08-13, where lexical sort returned `ht-1` as the highest human-task ID against an actual `ht-080`. `grep -o` is also deliberate: it matches IDs wherever they appear, including cross-references and prose, so an ID that was promised somewhere but not yet defined is not handed out twice. `validate_canvas.py` has a duplicate-ID check (lines 230-239) that catches the failure on CI, but a duplicate can persist in the working tree for days if CI isn't run between edit and discovery — see roadmap-repo `corrections.md` 2026-05-15 "Duplicate canvas ID created in landscape.yml" for the worked example.

Original failure mode: anti-pattern #7 instance #5, 2026-05-09 — agent conflated Bash `head` with the Read tool, lost ~14k tokens to a Write-fail → remedial-full-Read → re-Write loop. The `limit:1` discipline (graduated 2026-05-14, v0.23.18) prevents the second-order cost where the agent *correctly* follows the rule but full-Reads every time. The ID-scan discipline (graduated 2026-05-15, v0.23.19) prevents the related class where the agent reads enough of the file to satisfy the Edit check but not enough to see existing ID assignments — kin to anti-pattern #8 (Stale State Read).

If this skill writes to multiple canvas files, register each one first (limit:1 for Edit-only paths; full Read for Write paths) AND ID-scan any prefix you intend to assign.

See `CLAUDE.md` *Canvas writes — Read before Write* for the canonical rule.

Workflow

ICE scoring is applied to **OST solution leaves**. Each leaf must have a Four Risks assessment (Torres Product Trio) before it can be scored — the risks are the inputs, ICE is the output.

Step 1: Verify Four Risks exist

For each solution leaf in `.claude/canvas/opportunities.yml`, check that `four_risks` has been assessed:

• **Value** (product lens): Do users want/need this?
• **Usability** (design lens): Can users figure it out?
• **Feasibility** (engineering lens): Can we build it within constraints?
• **Viability** (cross-cutting): Does it align with business/legal/ethical constraints?

If any risk dimension is missing, assess it first. Each dimension must have its own evidence — a single combined statement fails (Torres Product Trio rule).

Step 2: Derive ICE from the Four Risks

For each solution, score three dimensions (1-10):

**Impact** — derived from value + usability + viability risks:

• What does the value risk assessment say about user demand?
• What does the usability assessment say about adoption likelihood?
• What does the viability assessment say about business alignment?
• 1-3: Low value evidence, high usability risk, or poor viability fit
• 4-6: Moderate value evidence, some usability unknowns
• 7-10: Strong value evidence, low usability risk, clear viability

**Confidence** — how well-tested are the risk assessments?

> *Scale note: Mycelium uses 0.0-1.0. Gilad's original Confidence Meter uses 0-10 non-linear (0.01=opinion, 1=anecdotal, 5=market data, 8=A/B test, 10=launch data). The non-linear penalty is preserved through evidence-class weighting.*

• 1-3: Risk assessments based on gut feel or desk research
• 4-6: Some direct evidence (interviews, analogues, small tests)
• 7-10: Strong evidence (user tests, prototypes, data). Requires test-validated evidence.

**Ease** — derived from feasibility risk:

• What does the engineering assessment say about complexity?
• What dependencies, unknowns, or technical spikes were identified?
• 1-3: High feasibility risk. Major unknowns, new capabilities needed.
• 4-6: Moderate feasibility risk. Known patterns, some complexity.
• 7-10: Low feasibility risk. Quick win, existing patterns.

Step 3: Calculate and rank

ICE = Impact x Confidence x Ease. Rank soluti