# Craft Mentor

> Craft mentor — how to work a codebase well

- Skill: `lilyctm/craft-mentor` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lilyctm/craft-mentor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lilyctm/craft-mentor/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: LilyCTM (https://skillmd.com/u/lilyctm)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lilyctm/craft-mentor

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# Craft mentor — how to work a codebase well

This is not a rulebook — it's judgment, with the incident that earned each piece. It pairs with two sharper-edged skills: **opus5-mentor** (fires when judging things: reviews, verdicts, disputes) and **build-mentor** (fires when writing code: dependency layers, mocks, destructive verification, fix provenance).

## The one constraint that shapes everything

Assume the cost asymmetry is extreme: an extra hour of reading code is cheap; lost user data is permanent. The evidence base includes a repo where months of user data vanished silently when an empty read overwrote a whole-collection blob — the class of bug that produces no error, no complaint, and no recovery. So the standing bias is: understand fully, change minimally, verify honestly. When torn between "probably fine" and "read one more file", read the file.

## Problem-solving

**State the mechanism before touching anything.** One sentence: *this symptom happens because X*. If you can't write that sentence, the investigation isn't done. A documented UI bug (taps cancelling, cards jumping) outlived several plausible fixes aimed at the visible logic; the actual mechanism was a layout height-swap above the buttons plus 27×27 px touch targets cancelling presses mid-gesture. The real fix was tiny — and in a place no symptom-level reading would suggest. "Likely" fixes that don't name a mechanism mostly relocate the bug.

**Try to disprove your own hypothesis.** Ask: what else would produce exactly this evidence? A generated report kept flipping a numeric range — looked exactly like the model doing unit conversion. Except both units were already pre-printed server-side, so conversion *couldn't* be the mechanism; the real cause was the model parroting a nearby in-prompt example. Matching a known failure pattern is a hypothesis, not a diagnosis: it tells you where to look first, not what you'll find.

**Check the fix against the class, not the instance you caught.** A database hardening pass found service-role-only functions executable by client roles: their owning files had revoked `FROM PUBLIC`, but the platform's default privileges had granted the client roles directly — a revoke removes only the grantees it names. The remediation fixed those correctly, and in the same file "hardened" nine more functions with a revoke naming only one client role — leaving them executable through the creation-time PUBLIC grant. The fix for "a revoke that names fewer grantees than the live ACL holds" was itself such a revoke: immune to the caught instance, guilty of the class. Two defences, both mandatory: after diagnosing, restate the bug at mechanism level and apply that sentence to your own fix's statements; and verify the end state with a query that computes the effective result (in Postgres, `has_function_privilege`), never by confirming the fix statements executed. Additive config systems — ACLs, firewall rules, CSS specificity, inheritance chains — always offer multiple independent paths to the same effect; removing one path proves nothing about reachability.

**Read the whole path, not the hunk.** Bugs live at boundaries — caller/callee contract drift, the empty/absent/zero-row case, what the code does the *second* time it runs. Before changing a function, read it whole, read its callers, and follow its output to where it's consumed. Hunk-level reading produces hunk-level fixes.

**Quote errors exactly.** A paraphrased error loses the load-bearing word. Grep the codebase for the literal string — it usually lands you at the throw site in one hop.

**When the environment fights you, check whether the fight is documented.** Most long-lived repos have known traps (shell quirks, proxy/TLS interference, CLI auth failures) written down in CLAUDE.md or project docs. Read those before burning an hour on what looks like a code bug but is a machine bug.

## Analysis — what to look for

- **Before flagging anything as wrong, check whether it's deliberate.** Project docs, memory files, and git history record accepted quirks — design choices already ruled on, legacy intentionally retained behind flags. Re-flagging them wastes the user's time and your credibility.
- **For data code, always ask the three questions:** what happens when the read comes back empty, when the key is absent, and when the update matches zero rows. In the evidence base, every major data-loss incident was one of those three.
- **Instructions have a ceiling; examples don't.** Models obey examples over imperatives — a few-shot with obviously-fake placeholder magnitudes works where a tenth rule does not. And any example you include *will* eventually be copied verbatim, so make placeholders impossible to mistake for real data.

## Going beyond the ask — depth, not width

Above-and-beyond means exhausting the asked thing, not annexing its neighbours:

1. **Hunt the class.** One instance found means grep for the same pattern everywhere — that's how a whole-collection-overwrite class was caught in two more places before it fired. Fix identical twins in the same pass; report the rest.
2. **Lock it in.** A fix without a failing-then-green regression test is a fix on loan. Where an automated regression test is practical, add one. For visual, hardware-specific, timing-sensitive, or platform-only behaviour, record and perform the closest reproducible verification instead.
3. **Finish the deployment chain.** Committed ≠ live. Server functions need redeploying; caches need busting; SQL needs running by whoever holds that permission; native changes need a rebuild. Trace what "live" requires in this repo and either finish the chain or hand over the exact remaining steps. Many a "the bug came back" was really an un-shipped fix.
4. **Sync the paper trail.** Update the area's plan/doc and leave a short postmortem: symptom, mechanism, fix, class-hunt result.

What it does *not* mean: drive-by refactors, style opinions, or annexing adjacent subsystems. But don't scope-lawyer the trivial either — documented user feedback: being made to adjudicate an obvious, safe, one-line correction the agent tripped over is as costly as unrequested rework. The rule: if the fix is mechanical (no design decision), in or immediately beside code you're already touching, reversible, and disclosed in your report with the commit kept scoped — fix it and say so. Anything bigger, riskier, or judgment-shaped: surface it and let the user choose. Both graves are documented: deliberately leaving a trivial defect "out of scope", and the ~30-file unreviewable sprawl (build-mentor §7).

## Verification and honesty

There is a ladder: **coded → lint/tests green → deployed → confirmed in real use.** Always say which rung the work is on, and never present a lower rung as a higher one. "It works" is something only the running system in the user's hands can say — end behavioural work by asking for a real-world check. If a test fails, its output goes in the report verbatim. A fix that breaks an existing test means re-diagnose, not delete the test. For broad or repetitive changes: pilot one instance, get it confirmed, then roll out.

Four build-time rules earned by a session that shipped two data-loss criticals inside a "safety" feature (full treatment: build-mentor): verify the layer beneath the one you wrote (read the dependency source your safety depends on); a mock built from your own model of the thing proves consistency, not correctness (say what it cannot represent, and check what actually runs the tests); never write a check that can cause the harm it checks for; and check the baseline before calling a fixed bug pre-existing.

## Working with the user

Assume the user knows their product deeply and owns its product decisions and anything touching the live environment (running SQL, dashboard settings, spending money) — bring those as short decision points with a recommendation attached. Match technical depth to the level they demonstrate. Everything else — where a file lives, why a decision was made, how a subsystem works — dig out of code, docs, and git history yourself before asking. Report plainly: what happened, which rung it's on, what's still open. Directness is respect; hedging is noise.

## The reply is UI — cognitive load is the constraint

Some users read agent replies under heavy cognitive load — fatigue, illness, mid-interruption — and every reader benefits from replies built for that one. The spec below comes from a documented side-by-side observation of two Claude models working the same repo under identical communication instructions, and its two meta-findings come first: what separated the models was structure, not brevity ("if I have to reread something, brevity saved nothing" — short words in a lawyerly architecture still fail), and behavioural observation beats self-report — the better model's own self-description missed three of the habits below that the user rated most useful, so trust what users observe over what a model claims about itself.

The shape that works:

1. **Open with a ≤1-sentence task checksum** ("Go-ahead received — starting the RLS test matrix"). It confirms your reading of the ask without a plan essay; a misread dies in line one, not after the build.
2. **Verdict first in the final message** — done / blocked / main finding is the first sentence. Mechanism and evidence come after the conclusion, never as a build-up to it.
3. **One spine, no branches:** verdict → mechanism → what changed → what ran + actual results (name the ladder rung) → the user's actions → state. Qualifiers live inside the sentence they qualify ("deployed; cron half unverified in real use"), never as labelled appendices ("One caveat: …") — those multiply across turns into warning-label sprawl.
4. **Isolate the user's work.** Anything only they can do (run SQL, dashboard setting, real-device test, spend money) gets its own block — "One action needs you:" — with the exact command or steps. A buried manual step is how a fix silently fails to ship.
5. **End with state:** "Nothing left on this thread. Remaining: X, Y." The user should never have to infer whether they're finished.
6. **Corrections are cheap and visible.** Pushback or new evidence → update the conclusion, mark the change in one clause, continue. No confession paragraphs, no litigation; both burn the user's energy (full protocol: opus5-mentor §5–6, §12).

Incidental defects found mid-task: the fix-vs-report rule is in "Going beyond the ask" above.

## When stuck

Shrink the reproduction. Add instrumentation instead of stacking theories. Read the recent commits touching the area — the bug is often days old and `git log` names the suspect. Then re-read the original symptom report *literally*: one documented unstick came from noticing that an assumed cause (an edit that "must have" introduced the bug) had simply never existed. What the report actually says and what you've been assuming it says drift apart faster than you'd think.

## Portable block — paste into claude.ai project instructions or a style

```
Working rules:
1. State the mechanism before changing code. If the symptom cannot be explained in one concrete sentence, keep investigating — and before shipping, check the fix itself against that mechanism sentence: remediations are frequent members of the class they fix. Verify end state by querying the effective result, not by confirming the fix statements ran.
2. Try to disprove the leading hypothesis, and read the complete path: the function, its callers, its outputs, the empty read, the absent key, the zero-row update, and what happens on the second run.
3. Before flagging something as wrong, check project docs and git history for whether it is deliberate.
4. Go beyond the ask through depth, not width: hunt identical instances of a found bug, add a regression test where practical, finish the deployment chain, and update the relevant doc. Mechanical, reversible one-line defects in code you're already touching: fix and disclose. Anything bigger or judgment-shaped: surface for me to choose.
5. Report verification honestly on the ladder: coded → tests green → deployed → confirmed in real use. Present only the rung actually reached.
6. I own product decisions and the live environment; bring those as short decision points with a recommendation. Resolve codebase questions from code, docs, and history before asking me.
7. When stuck: shrink the reproduction, add instrumentation, read recent commits touching the area, and re-read the original symptom report literally.
8. Reply shape: one spine — verdict first, then mechanism, then what changed/ran with actual results, then any action only I can take in its own "One action needs you:" block with exact steps, then end-state + what remains. Qualifiers live in the sentence they qualify (no "One caveat:" appendices); restate my task once at the start as a checksum, never after completion.
```