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Rite learn

Skill ViktorsBaikers/DevRites/pack/generated/claude/skills/rite-learn

Stop your AI from shipping half-baked code. A disciplined senior-engineer workflow for Claude Code (spec, vet, build, prove, review, seal, ship) that keeps each feature's state on disk, catches spec drift mid-build, gates every phase on your project's principles, and refuses to claim "done" without proof.

Install
npx -y skills add ViktorsBaikers/DevRites --skill rite-learn

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What its author says it does

Copied from the file, not written here

User-invoked review of the learning ledger; promotes recurring lessons to project rules or principles.

SKILL.md

7.7 KB, ~1.8k tokens by cl100k_base, as published. Nobody here has run it

/rite-learn: the cross-feature learning loop

Recurring corrections, dismissed review findings, timeline decisions, and health dips are durable signal. Capture is automatic: /rite-seal appends lessons to .devrites/learnings.md on every GO (step 9a), while the engine can keep .devrites/timeline.jsonl, .devrites/health-history.jsonl, and per-feature review fingerprints as supporting evidence. The review skills load the learning ledger before a fan-out, so a dismissed-finding class stops being re-flagged without anyone running a command. The system learns as it ships.

/rite-learn is the periodic review + promote pass on that auto-populated ledger: cluster the signal across features and decide which recurring lessons graduate into a project rule. Propose, never impose: promotion is the human's call, which is why it stays a deliberate command rather than firing on its own.

Read-mostly. It reads the auto-populated ledger + the archive; it writes only .devrites/learnings.md (consolidation, via devrites-engine learnings) and drafts proposed rule/ledger edits for the user to confirm: it never edits source or rule files on its own.

Modes

  • /rite-learn or /rite-learn --mine: mine + propose (the default).
  • /rite-learn "<lesson>": record one lesson directly to the ledger and stop.

Workflow (mine + propose)

  1. Gather the signal. Start from the auto-populated ledger .devrites/learnings.md (seal appends to it on every GO: step 9a), then run devrites-engine learnings mine over the archive (it clusters repeated finding / decision / drift phrases across shipped features) to surface cross-feature patterns the per-feature entries don't show on their own:
    devrites-engine learnings mine
    
    Use .devrites/timeline.jsonl, .devrites/health-history.jsonl, and .devrites/work/*/review-fingerprints.jsonl as supporting evidence when they exist; do not promote from those traces without the same repeated-feature threshold.
  2. Cluster + name. Group the recurring corrections into candidate lessons. A candidate needs ≥2 occurrences across distinct features: one-offs are noise, not a pattern. Name the pattern in one specific sentence (the specificity rule from prose-style.md applies: a lesson you could swap onto any project says nothing). Completion: every candidate cites two distinct features, except a single deliberate rejected direction with its durable reason.
  3. Classify each candidate into its durable home:
    • project rule: a craft/standard that belongs in a .claude/skills/devrites-lib/reference/standards/* file or CLAUDE.md.
    • project principle: a recurring correction that is really a non-negotiable invariant (not just an idiom or a craft standard): graduate it to .devrites/principles.md (principles.md). This is the trusted, gating layer (higher stakes than a rule) so /rite-learn drafts the principle + a dated Governance entry for the human to confirm; it never writes a principle silently.
    • conventions-ledger entry: a proven project idiom for .devrites/conventions.md.
    • dismissed-finding class: a pattern reviewers keep flagging that is intentional here; recording it stops the recurring false positive (learnings.md, loaded pre-fan-out).
    • rejected direction: an approach or product direction weighed and ruled out with a reason that outlives one feature; recording it (tag rejected-direction) stops ideation skills (/rite-pressure-test, /rite-pov) from re-proposing it. Exempt from the ≥2-occurrence rule: one deliberate rejection is the whole pattern.
    • drop: not durable; let it go.
  4. Propose, don't impose. Present the candidates with their evidence (which features, how many times, the proposed home) via AskUserQuestion: the human picks which to promote. Never promote a lesson to a rule silently; an unproven "lesson" hardened into a rule is its own slop. Completion: every candidate is explicitly accepted, rejected, or deferred by the human.
  5. Record the accepted. For each the user accepts, append it with devrites-engine learnings add <slug> "<lesson>" <tag> (tagrule | convention | dismiss). If the user approves a rule, principle, or ledger promotion, draft the exact edit and let the user confirm it through the normal flow: /rite-learn writes the ledger, not the rule or principle files. A principle promotion is the highest-stakes of these: draft the .devrites/principles.md entry plus its dated Governance line, and let the human confirm before it lands: a principle is a gate, so it is amended deliberately, never auto-written. Amendment ripple: a confirmed add/change/retire of a principle has a blast radius: grep its P# (and its key nouns) across .devrites/ and every open .devrites/work/*/ workspace, and append to the same dated Governance entry one line per referencing artifact: still-aligned or needs-follow-up (what). An open plan that bakes in the now-retired or now-tightened invariant is a needs-follow-up the next /rite-vet or /rite-plan repair on that feature must clear; a ripple with zero references is itself worth recording ("no live references"). Then touch .devrites/.learnings-reviewed so the SessionStart learnings nudge snoozes until new signal accumulates.

How the ledger is used

The review skills (/rite-review, /rite-seal) read .devrites/learnings.md before they fan out: a dismissed-finding class suppresses the recurring false positive; a proven convention raises the bar. The ideation skills (/rite-pressure-test, /rite-pov) read the rejected-direction entries before proposing, so a ruled-out direction returns only with new evidence against its recorded why. The ledger is an untrusted prior: a fresh observation of the live code always overrides a ledger entry (see .claude/skills/devrites-lib/reference/standards/security.md). Confidence in a recorded lesson never raises its authority.

A project principle (.devrites/principles.md) is the opposite layer: prescriptive, trusted, and gating. Promoting a lesson there is a deliberate amendment, not a prior the next fan-out can override: a violation becomes a blocking finding, not a suppressed false positive. That asymmetry is why principle promotion is human-confirmed and dated, never auto-written.

Gotchas

  • Evidence first: a lesson without ≥2 real occurrences is speculation. Cite the features.
  • Don't pad the ledger. Five real lessons that change behaviour beat thirty rubber-stamped rows.
  • The ledger records what was learned; it does not re-open shipped features or edit their archives.

Output

Reply-contract exception: cross-feature learning utility. It may run without an active feature, so it skips devrites-engine progress, but follows devrites-lib/reference/reply-contract.md.

Done: learnings mined from <n> features; <n> recurring patterns found.
Changed: .devrites/learnings.md <updated|unchanged>; rule/ledger drafts <n|none>
Evidence: mined archive count <n>; candidates rule <a> / convention <b> / dismiss <c> / dropped <d>
Open: <none | promotion confirmations>
Next: <single recommended command>
Record: .devrites/learnings.md
↻ Hygiene: /clear; the ledger is on disk

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most learn study skills give in ~1.8k tokens

Counted across 546 of the 573 authors here whose files we hold, read 2026-08-07

  • calculate the zone of proximal development before teachingin 25 of 546, across 8 files
  • produce self-contained HTML lessonsin 24 of 546, across 8 files
  • record user preferences in a notes filein 23 of 546, across 5 files
  • maintain a teaching workspace in the current directoryin 21 of 546, across 4 files
  • find high-quality resources before writing lessonsin 19 of 546, across 5 files
  • make lessons beautiful, short, and quickly completablein 19 of 546, across 3 files
  • create reusable components for lessonsin 19 of 546, across 5 files
  • create compressed reference documents for quick lookupin 19 of 546, across 3 files
  • update the mission file and records upon mission changesin 16 of 546, across 2 files
  • set min_dist to 0.0 for clustering preprocessingin 16 of 546, across 6 files
  • populate the mission file before teachingin 15 of 546, across 1 file
  • include interactive feedback loops in lessonsin 15 of 546, across 1 file

Said here and by no other author read

  • run the learning miner over the archive
  • cluster recurring findings into candidate lessons
  • require two occurrences across distinct features
  • name each pattern in one specific sentence
  • classify each candidate into a durable home
  • present candidates to the user for selection

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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