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Gist plan

Skill haabe/mycelium/plugins/mycelium/skills/gist-plan

AI made building cheap. It didn't make deciding cheap. Mycelium is a Claude Code harness that makes your agent run discovery and weigh evidence before it writes code. It earns the right to start. Built for software, courses, AI tools, and services.

Install
npx -y skills add haabe/mycelium --skill gist-plan

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GIST planning workflow. Structure goals into ideas, steps, and tasks using Gilad's evidence-guided framework.

SKILL.md

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GIST Planning

Replace opinion-based roadmaps with evidence-guided planning. Source: Gilad (Evidence Guided).

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.

Workflow

1. Set Goals (Quarterly)

  • Derive from North Star input metrics or OKRs
  • Format: "Improve [metric] from [current] to [target] by [date]"
  • Maximum 3 goals per quarter
  • Update .claude/canvas/gist.yml goals section

2. Generate Ideas (Ongoing)

  • Ideas are hypothetical ways to achieve goals
  • Most ideas fail (>80%) -- this is expected and planned for
  • Generate many, hold loosely
  • Store in .claude/canvas/gist.yml idea bank with ICE scores
  • Never commit to an idea until evidence supports it

3. Score with ICE + Confidence Meter

Use /mycelium:ice-score to prioritize. ICE scoring (Ellis; confidence dimension added by Gilad):

  • Confidence is NOT gut feel -- it maps to evidence levels
  • 0.1 = opinion only | 0.5 = data supports | 0.7 = tested | 0.9 = launched
  • Rescore after every experiment

Mycelium uses 0.0-1.0 (adapted from Gilad's 0-10 non-linear Confidence Meter). See /mycelium:ice-score for details.

4. Design Steps (per top idea)

  • Steps are small, time-boxed activities that build evidence
  • Each step has: hypothesis, method, success criteria, MoSCoW priority
  • Tag each step as Must / Should / Could / Won't (DSDM):
    • Must: Non-negotiable. Delivery fails without this. All REVIEW checks apply.
    • Should: Important. Ship if time allows. All REVIEW checks apply.
    • Could: Nice-to-have. Cut first when timebox runs out. NUDGE checks only.
    • Won't: Explicitly out of scope for this cycle. Documented for future reference.
  • Steps follow a confidence ladder: assessment -> exploratory experiment -> feature experiment -> launch
  • Each step produces evidence that increases or decreases confidence
  • If evidence is negative: pivot or kill the idea (sunk cost is irrelevant)
  • For user-facing ideas, frame hypotheses in Lean UX format: "We believe [outcome] for [users] if [change]." (Gothelf)
  • When a delivery timebox is exceeded: Flex scope using MoSCoW — cut Could/Won't before compromising Must/Should

5. Execute Tasks (Sprint-level)

  • Tasks belong to the CURRENT step only
  • Don't plan tasks for future steps
  • Standard agile execution

6. Reprioritize (Continuous)

After each step completes:

  • Update ICE scores based on new evidence
  • Re-rank ideas
  • Kill ideas below threshold
  • Surface new ideas from discovery work

Shape Up: Appetite Over Estimates

Instead of asking "how long will this take?", ask "how much is this worth?" (Shape Up by Basecamp). Set an appetite — the maximum time you're willing to invest — then design the solution to fit within it. If the solution can't fit, narrow the scope, don't extend the timebox. This connects naturally to MoSCoW: appetite defines the timebox, MoSCoW decides what fits within it.

Anti-Pattern: The Feature Roadmap

If your GIST board looks like a feature list with dates, you're doing it wrong. Goals are outcomes, ideas are hypotheses, steps are experiments.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.