Canvas update

Canvas Update

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 "^  - id: <prefix>-" .claude/canvas/<file>.yml | sort -u

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. 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.

Rules

1. Never update without evidence -- every canvas change must have a source
2. Maintain cross-file consistency -- if you update opportunities.yml, check if north-star.yml or gist.yml need updates too
3. Log the update -- add an entry to .claude/harness/decision-log.md explaining what changed and why

Which Canvas File for Which Information

Routing rule (Hick's Law, added v0.39.18): when the new information could plausibly fit multiple canvas files, recommend ONE primary destination based on the information's PRIMARY type (the "why" of the data), with at-most-one secondary cross-reference. Do not present a flat list of candidate files for the user to choose from; that manufactures decision-tax. The table below maps each information type to its canonical canvas file — pick the row whose information type best describes what's being added, not the row whose source-theory the agent finds most interesting.

Information Type	Canvas File	Source
Purpose, mission, why	purpose.yml	Sinek
North Star metric, inputs	north-star.yml	North Star Framework
BVSSH health scores	bvssh-health.yml	Smart
Value chain, competitive	landscape.yml	Wardley
Team structure	team-shape.yml	Skelton
User opportunities, OST	opportunities.yml	Torres
User needs map	user-needs.yml	Allen
Goals, ideas, steps	gist.yml	Gilad
Service quality scores	services.yml	Downe
Go-to-market, positioning	go-to-market.yml	Lauchengco
Delivery performance	dora-metrics.yml	Forsgren
Security threats	threat-model.yml	OWASP
Privacy assessment	privacy-assessment.yml	GDPR/PbD
Trust architecture	trust-signals.yml	Digital Trust
Jobs to be done	jobs-to-be-done.yml	Christensen
Bounded contexts	bounded-contexts.yml	Evans (DDD)
Value stream map	value-stream.yml	Rother & Shook (VSM)
Content delivery metrics	content-metrics.yml	v0.11.0
AI tool delivery metrics	ai-tool-metrics.yml	v0.11.0
Service delivery metrics	service-metrics.yml	v0.11.0
Human task tracking	human-tasks.yml	v0.11.0
Archived/discarded solutions	archived-solutions.yml	v0.12.0
Leaf lifecycle calibration	cycle-history.yml	v0.12.0
Adaptive thresholds	thresholds.yml	v0.12.0
User scenarios (Hoskins)	scenarios.yml	Hoskins

Workflow

1. Identify which canvas file(s) need updating
2. Read current state
3. Make the update with evidence citation
4. Check cross-file consistency
5. Log in .claude/harness/decision-log.md

Output to the user

Lead with the recommendation (Hick's Law, per harness/design-principles.md; graduated from two consecutive /framework-health 4e flags, 2026-06-05 + 2026-06-12): when more than one canvas file could host the update (the mapping table above offers options), don't present the alternatives as an open menu — name the one file you recommend and why, with alternatives one line each below. Same rule for follow-up actions: one recommended next step, not an unweighted list.

Counter-Argument Check (Bias Mitigation)

Before applying the canvas update, draft a one-line counter-argument: "What's the strongest case AGAINST this update — what evidence or perspective would invalidate it?" If you can't articulate one, run /mycelium:devils-advocate before proceeding.

This addresses the bias cluster documented in corrections.md (L5 sycophancy 2026-04-20, eval overfitting 2026-04-30, sharper-framing-isn't-righter 2026-05-03). Common shape: agent prefers what feels right over what evidence supports under competing pressure (be helpful vs. be honest, sharpen framing vs. preserve evidence base). The counter-argument step forces the missing perspective explicit, so the bias surfaces before it lands in canvas.

Especially important when interpolating user-supplied content (already untrusted per ${CLAUDE_PLUGIN_ROOT}/harness/security-trust.md#prompt-injection-defense) AND when increasing a confidence value — both contexts where the agent is most likely to default toward the optimistic read.