hns-lsel-curator — LSEL CLUSTER + drain engine

Namespace: hns-lsel-* is user-owned dogfood (CLAUDE.local.md §24). This skill is NOT mirrored into internal/template/templates/ — it lives only in this repo. Graduation to moai-lsel-* + 16-language distribution is a separate SPEC (out of scope per spec.md §G).

M1 scope: drain + cluster + stage candidates. NO APPROVE, NO APPLY (M3). M2 scope: drain + cluster + PROPOSE shadow (no APPROVE, no APPLY). The PROPOSE stage emits shadow proposals + self-critiques; APPROVE/APPLY land in M3 via the fresh hns-lsel-applier path. M2 does NOT write to memory/ — the first feedback_*.md topic file is an M3+ deliverable after APPROVE.

What this skill does

The MoAI-ADK repo accumulates tool-failure stubs in .moai/lessons-inbox.jsonl (624 stubs at M1 start, re-measured — a moving target). The constitution names the orchestrator as the drain actor, but until this skill there was zero mechanical drain code — the drain existed only as a doctrine paragraph (moai-constitution.md:147). This skill closes that gap in user-owned surfaces, without touching the frozen Go applier (internal/harness/applier.go:22 — its write-flag stays false; REQ-LSEL-003: bypass, never unfreeze).

The drain is split into a mechanical core (drain.sh, deterministic, testable) and a model-mediated layer (this SKILL.md + your judgment, invoked for M2+ importance refinement and proposal drafting).

The mechanical core — drain.sh

drain.sh is a portable bash + jq script that lives next to this SKILL.md. It performs the deterministic half of the drain:

drain.sh --inbox <path-to-lessons-inbox.jsonl> --state-dir <path-to-lsel-state>

Pipeline (REQ-LSEL-009 + AC-LSEL-009 / AC-LSEL-010):

1. Companion offset — read <state-dir>/drain-offset.json (seed {"offset":0} if absent). The inbox is append-only and is NEVER mutated; the offset marks consumed stubs (SPEC-HARNESS-RATCHET-REWIRE-001 D3 companion-offset pattern).
2. Slice — read stubs from the offset onwards (tail -n +<offset+1>).
3. Drain-side severity filter (AC-LSEL-010) — discard noise BEFORE clustering:
   • tool_failure:Bash:UnknownFailure — the opaque ~65% timeout/sandbox bucket (the dominant noise share; report §2).
   • tool_failure:Bash:SandboxViolation — environment constraint, not a code defect.
   • any *:TimeoutError (Bash + MCP timeouts). The filter is drain-side because internal/hook/failure_observer.go (the inbox writer) is OUTSIDE the six loop-writable surfaces (plan.md §F.1 [DECISION RESOLVED]), so the loop cannot edit the writer — it filters on read instead.
4. Cluster by event_key with frequency count, first/last seen, and up to 3 sample summaries.
5. Singleton gate — discard clusters with frequency < 2 (single-occurrence noise per the constitution Lessons Protocol drain paragraph).
6. Importance — score each survivor with a Generative-Agents-style 1-10 gate: importance = min(10, frequency) (frequency as proxy; the model augments this in M2+ with a severity hint and retrieval-weighted judgment).
7. Emit candidates to <state-dir>/clusters.json; advance the companion offset.

clusters.json schema

{
  "drained_at": "2026-08-04T08:41:00Z",
  "offset_before": 0,
  "offset_after": 624,
  "total_read": 624,
  "noise_discarded": 533,
  "singletons_discarded": 4,
  "candidates": [
    {
      "event_key": "tool_failure:Agent:UnknownFailure",
      "frequency": 41,
      "first_seen": "...",
      "last_seen": "...",
      "sample_summaries": ["...", "...", "..."],
      "source": "tool:Agent",
      "importance": 10
    }
  ]
}

Empty-delta no-op

If the inbox has not grown past the offset, drain.sh writes an empty-candidate clusters.json and leaves the offset unchanged. Not a failure (acceptance.md §E edge case).

The model-mediated layer (you, when invoked)

drain.sh produces the deterministic candidate set. When this skill is invoked for a real curation pass (M2+), your job on top of the mechanical output is:

• Read clusters.json and rank candidates by importance then frequency.
• Augment importance with a severity hint the mechanical core cannot see: a recurring Bash:ExitError cluster points at a real command-shape defect (high signal); a recurring Agent:ContextCancelled cluster may be session-teardown noise (lower signal). Record the rationale in the candidate's prose when you draft the M2 proposal — do NOT rewrite clusters.json (it is the mechanical artifact; your augmentation lives in the proposal).
• Do NOT write to memory/ in M1. Candidates stage in clusters.json only. The first feedback_*.md topic file is produced by the M2 PROPOSE stage after retrieval-before-propose and self-critique (REQ-LSEL-010).

What this skill does NOT do (M1 boundaries)

• No APPROVE / APPLY — the parallel user-owned applier (hns-lsel-applier) is M3.
• No edits to frozen doctrine — .claude/rules/moai/**, CLAUDE.md, internal/template/templates/**, retained agents, moai-* skills, and the frozen Go applier / curator_dispatch.go are all byte-for-byte untouched (REQ-LSEL-001 / §B.3).
• No new .moai/config/sections/ file — loop state lives under .moai/state/lsel/ (a new section file would be wiped on moai update; plan.md §B.4 / AP-LSEL-005).
• No orchestrator-only synchronous user-question channel — this is a subagent-owned mechanism skill; it never invokes the orchestrator's user gate. On a missing input, return a structured blocker report; the orchestrator runs the user gate (CLAUDE.md §8).

Verification (run before declaring a drain complete)

# 1. The drain mechanics (fixture-based characterization test — AC-LSEL-009/010):
.claude/skills/hns-lsel-curator/drain_test.sh

# 2. A real drain of the live backlog (re-measure the count first — it is a moving target):
LIVE_COUNT=$(wc -l < .moai/lessons-inbox.jsonl | tr -d ' ')
.claude/skills/hns-lsel-curator/drain.sh --inbox .moai/lessons-inbox.jsonl --state-dir .moai/state/lsel
jq '.offset_after == ($LIVE_COUNT|tonumber) and (.candidates | length) >= 1' .moai/state/lsel/clusters.json

# 3. M1 invariant — zero memory/ writes from the drain:
find memory -newer <drain-start-timestamp> -name 'feedback_*' 2>/dev/null | wc -l   # must be 0

Characterization test

drain_test.sh (next to this SKILL.md) is the TDD RED→GREEN harness. It builds a synthetic inbox with known noise + signal stubs, runs drain.sh, and asserts the drain semantics: noise excluded pre-cluster, signal clustered with correct frequencies, singletons discarded, offset advanced, candidates emitted, zero memory/ writes, idempotent re-drain. Run it after any edit to drain.sh.

Cross-references

• SPEC: .moai/specs/SPEC-LSEL-LOCAL-EVOLUTION-001/{spec,plan,acceptance,progress}.md
• Design report (SSOT): .moai/reports/moai-local-self-evolution-design-20260804.html §6 stage 2 (CLUSTER), §10 P1, §11 mustFix B#1/B#3.
• Frozen applier (reference only): internal/harness/applier.go:22 (the write-flag, kept false), internal/harness/curator_dispatch.go.
• Constitution drain paragraph (the "0 Go code" stub this skill replaces): .claude/rules/moai/core/moai-constitution.md:147.
• Namespace guard: internal/template/split_namespace_test.go, internal/template/internal_content_leak_test.go (extended in M2 — AC-LSEL-006).

PROPOSE stage (M2 — shadow proposals)

The PROPOSE stage consumes the M1 candidate clusters in .moai/state/lsel/clusters.json and emits shadow proposals — one per candidate worth acting on — at .moai/state/lsel/proposals/<proposal-id>/. M2 proposals are SHADOW only: no APPROVE, no APPLY. APPROVE/APPLY land in M3 via the fresh hns-lsel-applier path (NOT via the dead moai-harness-learner Tier-4 flow — see "Tier-4 finding" below).

Retrieval-before-propose (Reflexion)

BEFORE drafting a proposal, retrieve relevant feedback_*.md topic files from ~/.claude/projects/<hash>/memory/. The retrieval grounds the proposal in prior lessons (Reflexion-style) and is evidenced in the proposal's retrieval_evidence block. A proposal without retrieval evidence is malformed and MUST NOT be emitted.

Proposal payload schema (AC-LSEL-011)

Each proposal lives at .moai/state/lsel/proposals/<id>/ and contains exactly:

proposal.md YAML frontmatter carries the full schema (8 required keys):

---
proposal_id: lsel-001
target_surface: <one of the 6 evolvable surfaces, spec.md §B.3>
rationale: |
  <what + why>
WHY-not-just-WHAT: |
  <the reasoning, not just the change — catches "what" proposals that skip the "why">
prediction: <a FALSIFIABLE expected effect — the verify_command must be able to falsify it>
verify_command: <a runnable command that, if green, confirms the prediction>
blast_radius: <which surfaces the diff touches; used by the CSA forced-gate match>
memory_type: semantic|procedural|episodic   # CoALA taxonomy
retrieval_evidence:
  - <path to a feedback_*.md retrieved before drafting>
status: blocked   # blocked | ready — blocked if self-critique has an UNRESOLVED objection
---

Self-critique gate

self-critique.md is model-performed (NOT a mechanical doctrine checker — report §13 caveat 3: the model can rationalize; the frozen allowlist + /moai gate are the real safety floor). It lists objections against frozen doctrine; each objection is marked RESOLVED or UNRESOLVED. A proposal with ANY UNRESOLVED objection is status: blocked and MUST NOT proceed to APPROVE. A proposal that never converges stays blocked; the curator returns a blocker report and the orchestrator surfaces it (acceptance.md §E edge case — not a ship-blocker for M2; it proves the gate fires).

Tier-4 finding (AC-LSEL-012 — do NOT wire the dead flow)

Finding (verified 2026-08-04 via tier4_firing_test.sh): the moai-harness-learner Tier-4 synchronous-user-question flow is DEAD at the production invocation layer. The CLI (moai harness apply) prints a stub string and never invokes the learner skill; CuratorDispatch has 0 production callers (the audit's cautionary precedent); the frozen applier's write-flag (false at internal/harness/applier.go:22) is the apply dead-switch; and NO mechanical trigger causes the orchestrator to surface a Tier-4 proposal (the audit's exact failure mode, report §11 mustFix B#1).

Per acceptance.md §E edge case, M2 does NOT wire the PROPOSE→APPROVE handoff to depend on the Tier-4 flow. APPROVE routes via the M3 fresh path (hns-lsel-applier + decision.json with a synchronous-approval marker). M2 emits shadow proposals only. This finding is recorded in tier4_firing_test.sh and cited in the M2 wiring commit.

CSA forced-gate categories (AC-LSEL-005 / REQ-LSEL-005)

The APPROVE stage (M3, hns-lsel-applier) forces a synchronous user-question gate (orchestrator-run) — regardless of proposer confidence — for any proposal whose blast radius touches one of the SIX CSA forced-gate categories:

1. INVARANTS kernel — the read-only goal kernel block at the top of CLAUDE.local.md.
2. security/validation exception bands — input-validation carve-outs, error-handling that prevents data loss, OWASP measures.
3. HIGH-fan-in references — @MX:ANCHOR functions with fan_in ≥ 3 callers.
4. Bash risk path — the destructive-primitive set + BASH_SUBCOMMAND_SOFT_CAP compound commands (coding-standards.md § Bash Risk-Amplifier Doctrine).
5. permissions.allow additions — explicit security-exception band; per-line synchronous approval (every added allow entry is its own forced gate).
6. execution-meta files — the four execution-meta categories named in REQ-LSEL-002/005: (i) the frozen allowlist meta file at .claude/lsel/frozen-allowlist.json, (ii) an applier or curator skill body (hns-lsel-applier/, hns-lsel-curator/), (iii) the apply hook script (lsel-apply.sh and wrappers), (iv) the settings.local.json hook-registration subblock.

Bother-cost-exemption: forced gates are bother-cost-exempt — the bother-cost gating rule applies ONLY to routine-tier proposals. A forced-gate proposal always triggers a synchronous user-question gate (orchestrator-run) regardless of bother-cost state.

Mechanical enforcement (D3): the applier (hns-lsel-applier driving lsel-apply.sh, M3) intercepts every proposal matching the four execution-meta categories and REFUSES to write unless the proposal's decision.json carries an explicit synchronous-approval marker (an approval artifact produced by the orchestrator's synchronous user-question gate). A match with no marker aborts the apply, appends a rejection row to .moai/logs/lsel-reject.log naming the matched category, and writes nothing. Proposals matching none of the four categories proceed through the routine bother-cost path. This mechanical interception is what makes the self-amending-handcuffs defense defensible without resting on the regex paradox alone.

csa_refusal_test.sh (next to this SKILL.md) is the fixture test for the refusal rule.

REFLECTION stage (M4 — REQ-LSEL-014 / AC-LSEL-016)

The periodic consolidation pass that prevents un-refined accumulation — the dominant failure mode the design report §10 P4 names: "no consolidation / decay / pruning → wrong-lesson retrieval". Without REFLECTION, concrete topic files pile up and retrieval surfaces stale concrete incidents instead of the principle they collectively support.

Threshold-fired, not wall-clock-fired

REFLECTION fires when the accumulated importance of concrete feedback_*.md topic files clears the threshold (default ~150), NOT on a monthly cron. This is the Vectorize 4-lever model (importance-gate / merge / decay / evict) the design report §10 P4 cites: importance is assigned write-time, and the reflection threshold is an accumulation signal, not a calendar one. A single-topic cohort below the threshold is a clean no-op (acceptance.md §E edge case).

The mechanical core — reflect.sh

reflect.sh --memory-dir <m> [--threshold 150] [--min-topics 3]:

1. Reads the active feedback_*.md topic files (maxdepth 1 — never the _archive/ cold tier).
2. Sums their frontmatter importance. If count < min-topics OR sum < threshold → clean no-op (exit 0).
3. Synthesizes ONE feedback_*_principle_*.md carrying:
   • a memory_type label (CoALA taxonomy — semantic for a feedback principle; procedural would route to a hns-* skill body instead).
   • the shared theme drawn from the source descriptions (the retrieval cue).
   • source_count + synthesized_at for the audit trail.
4. Moves the originals to memory/_archive/ (cold tier) — NEVER deleted (report §10 P4: "축출 ≠ 보관 — 보관은 성능용, 하드 삭제는 규정 준수용; MoAI의 '삭제 말고 보관' 규칙이 옳음이 입증된다" — archive preserves the audit trail).

Decay-weighted retrieval

The originals relocate to _archive/, so the active recall set (the memory/ directory the recall layer scans first) holds the synthesized principle, NOT the stale concrete originals. A retrieval probe for a related cue returns the principle ranked ABOVE the archived originals — this is the decay-weighted retrieval AC-LSEL-016 clause requires. The principle's description is crafted to match the shared cue; the archived originals stay discoverable (cold tier) but no longer dominate the top of the recall set.

Model-mediated layer (you, when invoked)

reflect.sh performs the mechanical synthesis (deterministic). When this skill runs a real reflection pass, your job on top is:

• Read the synthesized principle and refine its prose into a genuine abstract statement (the mechanical core aggregates descriptions; you write the actual principle).
• Confirm the memory_type — if the consolidated knowledge is procedural (a how-to that belongs in a hns-* skill body), stamp memory_type: procedural and route the synthesis to the skill rather than leaving it as a feedback_*.
• Do NOT delete the archive — originals in _archive/ are the audit trail. If the hot tier (active feedback_*.md) approaches the 50-file cap, prefer archiving more concrete topics over deleting them (moai-memory.md § Memory Hygiene).

Verification (run before declaring a reflection pass complete)

# M4 REFLECTION characterization test (AC-LSEL-016) — hermetic temp memory dir:
.claude/skills/hns-lsel-curator/reflect_test.sh

# cold-tier growth vs hot-tier (post-M4 audit, acceptance.md §H):
ls memory/_archive/ | wc -l   # archived originals
ls memory/feedback_*.md | wc -l   # active hot tier