Adopt (brownfield entry)

/mycelium:start assumes a blank page. Most projects are not a blank page. This skill is the entry point when the code came first.

When to Use

• A repo with existing source and no populated canvas (the SessionStart brownfield check points here).
• A maintainer who wants discovery on a product that already ships.
• NOT for a new idea — that is /mycelium:start.

The shape of the run: two phases, one session

Phase 1 — populate from the codebase. Read the repo and fill in what the code can actually establish, at the right evidence class.

Phase 2 — patch the holes with the user, exactly as a greenfield project would. The gaps left by phase 1 are not a backlog to hand over. They are the agenda for the rest of the session, worked with the same discovery discipline /mycelium:start would apply — the only difference is that the canvas is not empty when you begin.

Do not stop between the phases and present a list. Ending at a fork is the failure this skill exists to remove: a maintainer offered "run the greenfield brief or skip" has no good option, and "here is your discovery backlog" is the same fork with extra steps.

Why phase 1 cannot be the whole thing. A codebase answers what was built and how it ships. It is nearly silent on why it exists and who decided that. Extraction is asymmetric, and inverted from where discovery value lives:

scale	what code yields
L4 Delivery	STRONG — stack, release cadence, distribution, CI, test posture
L3 Solution	STRONG — feature surface, module structure, what shipped when
L2 Opportunity	INFERENCE ONLY — you see what was built, not the problem it solved. Real demand signal lives in the ISSUE TRACKER, which a clone does not contain.
L5 Market	THIN — category and channels, rarely positioning
L1 Strategy	USUALLY EMPTY
L0 Purpose	USUALLY NEAR-EMPTY — READMEs state a what and a stack, seldom a why

So phase 1 reliably fills the layers that never needed discovery and leaves empty the ones that did. That is not a defect in the extraction — it is the map of what phase 2 has to work on. Never present a phase-1 canvas as though discovery happened; it is a starting position, and say so.

Watch for drift. The sharpest thing this skill can surface is a mismatch between what the project says it is and what it has become. A README that describes the original scope while five years of changelog show the product moved somewhere else is a real finding, visible in minutes, and a maintainer recognises it immediately. Look for it explicitly: compare the stated purpose against the feature surface and the most recent changelog entries.

Workflow

Step 1: Confirm before anything

Do not scan and present as a fait accompli. Say what you propose to read and ask.

"This project has code but no Mycelium discovery state. I can read the repo and draft what it can establish — roughly the delivery and solution picture — and then show you what it cannot: purpose, strategy, and any real user evidence. That second list is the useful half. About N minutes. Go ahead?"

If declined, stop. Do not leave partial state. Offer /mycelium:start (blank page) or nothing.

Step 2: Read, in this order

Cheapest signal first; stop when you have enough rather than reading everything.

1. README, docs index — stated purpose, category, audience
2. CHANGELOG, release history — what the product actually became, and how fast
3. Manifest (package.json, pyproject.toml, …) — stack, distribution, scripts
4. Top-level source tree — feature surface and module boundaries
5. CONTRIBUTING, CI config — delivery posture
6. Tests — what the team considers worth protecting

Do not read the whole codebase. You are establishing shape, not comprehension. If the repo is large, breadth beats depth.

Issue tracker: if the host is reachable and the user consents, open issues are the only real L2 signal available. Without them, say L2 is un-evidenced rather than inferring it from code.

Step 3: Draft, with the evidence class the material actually has

Write to canvas ONLY what the code supports, and tag every entry:

provenance:
  evidence_type: anecdotal
  source_classes:
    - internal_desk        # derived from artifacts, NOT from anyone
  evidence_sources:
    - "codebase read <date> @ <commit>: <file> — <what it showed>"

HARD GUARDRAIL. Extracted material is never external_human. Nobody said it. If code-derived inference gets recorded as user evidence, the framework has automated the consistency-as-evidence failure it exists to prevent, and a canvas full of code-derived "evidence" is worse than an empty one. Confidence on anything extracted starts low and says why.

THREE TIERS, NOT TWO — and phase 2 moves material between them. This is the distinction that keeps the guardrail honest once the user starts talking:

tier	what it is	when
internal_desk	inferred from artifacts. Nobody said it.	phase 1
internal_stakeholder	the maintainer's own testimony about their own product	phase 2
external_human	someone who is NOT the maintainer — a user, a customer	neither

When the user confirms or corrects a phase-1 inference, that entry is upgraded from internal_desk to internal_stakeholder — it is now testimony rather than inference. It does NOT become external_human. The maintainer describing their own product is the person closest to the assumption, not evidence against it, and a canvas that blurs the two will read as validated when nothing outside the project has been consulted.

Say so explicitly in the entry when you upgrade, and note what is still missing: if no real user has been asked, the canvas should record that plainly rather than let a well-populated file imply otherwise.

Step 4: Show the starting position, briefly

State both halves in a few lines, then keep going. This is a checkpoint, not a handover.

Established from the code — delivery and solution, plainly, flagged as artifact-derived.

Open — and these are the next questions, not a report:

• Who is this for, specifically?
• What evidence exists that the problem is real?
• What would prove the idea wrong?
• Why this rather than the alternatives?
• Any drift between stated purpose and shipped behaviour.

Then say what happens next and start: "Those are the gaps. Same questions a new project would get, except we already know what you built. Shall we work through them?"

Step 5: Patch the holes — greenfield discipline, non-empty canvas

Run the normal discovery loop against the gaps. /mycelium:interview's questioning shape is the right instrument for the L0/L1 holes; /mycelium:jtbd-map or /mycelium:user-needs-map for L2. Invoke them, or apply their sequence directly — what matters is that the questions get asked in this session rather than deferred to one the user may never start.

Two things change versus greenfield, and both help:

• Ask better questions. You have read the code. "You describe this as X but the last year of changelog is mostly Y — which is it now?" beats "what are you trying to change, and for whom?" Use the drift finding as the opening.
• Anchor answers against reality. When the user states a purpose, check it against the feature surface. A mismatch is a finding, surfaced not corrected.

Same exit condition as greenfield. The canvas is done when it would pass the same gates a new project's must — real evidence at L0/L2, human sources, honest confidence. Artifact-derived entries do not satisfy those gates; they are scaffolding the human answers replace or confirm. A diamond opens when the user has chosen a scale, exactly as it would otherwise.

What NOT to Do

• Never write a canvas the user has not seen and approved.
• Never present extracted L3/L4 as though discovery happened.
• Never mark code-derived inference external_human, or set confidence above anecdotal on it.
• Never modify user-owned source. Brownfield-additive: the framework adds capabilities, it does not touch the project's own files.
• Never claim the drift finding is a problem. Surface it; the maintainer decides whether it is intentional.

Theory Citations

• Torres (CDH): opportunities come from evidence, not from reading code. Anything extracted at L2 is a hypothesis awaiting a human source.
• Gilad (Evidence-Guided): artifact-derived material sits at the bottom of the confidence ladder; label it there and let it earn its way up.
• Christensen (JTBD): a codebase shows the solution, never the job. The gap between them is what this skill exists to make visible.