| name | work-decomposition |
| tier | meta-meta |
| description | Decide how to decompose the rule set into TaskBoard tasks during rule_extraction → skill_authoring transition. Covers ordering methodologies (difficulty-first / Shannon–Huffman, breadth-first, depth-first, binary partition), grouping rules (when to bundle multiple rules into one task vs. keep separate), three-axis difficulty estimation, and how to write PATTERNS.md project memory that stays useful across the run. Use when entering rule_extraction, when entering skill_authoring, or whenever the TaskBoard feels wrong and you want to re-decompose. |
Work Decomposition
KC's main agent is the conductor. The conductor decides what work to do next — and that decision is upstream of every other choice that follows. Wrong decomposition makes the rest of the run expensive: if rules are processed in the wrong order, the agent re-designs the same shape three times. If unrelated rules are bundled into one skill, the resulting check.py drifts into the unified-runner anti-pattern. If related rules are split across separate skills, the agent re-derives the shared chunker logic many times over.
This skill is the conductor's playbook for that decision. It's tagged tier: meta-meta because work decomposition is a system-level discipline, not a per-rule technique. The complementary task-decomposition skill (also tier: meta-meta) covers the internal structure of one rule's check — locate, extract, normalize, judge, comment. This skill covers how the rule set should be split into TaskBoard items.
When to use this skill
- Entering rule_extraction. Read the regulation, decompose into rules, then decide how those rules will be ordered and grouped before declaring the phase done. Coverage audit + chunk refs are downstream of these decisions.
- Entering skill_authoring. TaskBoard is empty (engine no longer auto-populates per-rule tasks). Read the rule list from
describeState, decide grouping + order, then call TaskCreate for each unit of work.
- Mid-run re-decomposition. If the TaskBoard feels wrong (rules accumulating in the wrong order, an obviously-bundled pair across two tasks), stop adding work and re-decompose. The cost of pausing 5 minutes to re-plan is recovered within 2 rules of better-shaped work.
- Any phase with 3+ parallel sub-goals. If you find yourself juggling multiple parallel sub-goals in working memory (3+ rules × docs, multiple deliverable-prep items in finalization, several QC batches in production_qc), drop them into the TaskBoard and work serially. Every phase from rule_extraction through finalization benefits from explicit tasks once parallel sub-goals appear — distillation and production_qc included.
Quick rule: when does the TaskBoard belong?
- Touching N+ rules or docs in parallel? →
TaskCreate one task per rule/doc before starting.
- Single 1-step request? → Skip; just do it.
- Subagent internal coordination? → Skip; subagents don't get the TaskBoard.
- Anything you'd otherwise need to remember "I'll come back to that"? → TaskCreate it now. Working memory loses partial completions over long turns.
Locked principles
- Hard tracking, soft executing. The engine derives milestones from disk facts (
rule_skills/<id>/SKILL.md, check_*.py, workflows/<id>/...) regardless of how you grouped your tasks. Coverage is engine-verified; grouping is your call. You cannot bypass the floor by clever task naming, but the floor doesn't dictate task shape.
- The hardest rule contains the most information. Hard rules force the chunker, classifier, verdict shape, and worker LLM tier you'll need. Easy rules can compile down to a subset of that machinery cheaply. Encode the hard cases first; let the easy cases inherit.
- PATTERNS.md is the load-bearing memory. Without an accumulating reference, every rule starts from a blank slate and you re-design the same shape repeatedly. With it, work compounds.
Ordering methodologies
Pick one explicitly and write it into your first PATTERNS.md entry. "I'm going Shannon–Huffman because R028's multi-party verdict shape will dictate the chunker for everyone else" is a valid decision; "I started at the top of catalog.json and kept going" is not — it's just absence of decision.
Shannon–Huffman (difficulty-first) — recommended default
Process the hardest rule first. Use the chunker, verdict shape, and worker tier that hard rule demands as the design floor. Process subsequent rules in descending difficulty, each one a degenerate case of the machinery already built.
When to pick: the rule set has uneven complexity and you suspect a few hard rules will dictate the shape (almost always true for compliance / regulatory work). When this method is followed correctly it tends to produce sub-1% ERROR rates on real LLM-driven workflows; the bottom-up alternative typically over-produces NOT_APPLICABLE verdicts because the easy rules' machinery can't handle the hard cases at the end.
Why "Huffman" not "Shannon" for the analogy: Huffman builds optimal prefix codes by processing low-frequency symbols first. KC's analogue is the high-cost-per-rule, low-frequency rules — the R028s that dominate the design space even though there are few of them. Touch them first. The easy rules inherit the framework cheaply.
The ordering compiler-design parallel: don't optimize the common case until you've handled the worst case correctly. The common case being fast doesn't matter if the worst case requires a redesign.
Breadth-first (round-robin)
Process every rule to a shallow depth (skill skeleton + first regex pass), then go back and deepen each one. Useful when:
- The full set's quality matters more than per-rule depth (e.g., you need a coverage report fast)
- You don't yet know which rules are hard
- You're piloting a new methodology and want to validate the pipeline shape across many rules cheaply
Trap: you may declare rule_extraction done with shallow skills that never deepen. Worse than depth-first because the gate appears satisfied from coverage alone.
Depth-first (one rule at a time, fully done)
Process rule 1 to completion (SKILL.md + check.py + tests passing) before touching rule 2. Useful when:
- Rules are largely independent (rare in compliance work)
- The conductor model has small context and re-loading shape between rules is cheap
- You're proving the end-to-end pipeline before scaling
Trap: the first rule's shape gets locked in; refactoring after rule 5 means rewriting 1-4. Combine with PATTERNS.md to mitigate.
Binary partition
Split the rule set into two halves on a meaningful axis (public/private products, document type, regulation chapter), then recurse. Useful when:
- The split axis is structural (e.g., banking rules vs trust rules) — you can build separate tools per partition
- Some partitions can be skipped entirely (D6 applicability filter says "not applicable for this corpus")
Trap: premature partitioning when the axis isn't real. The agent commits to two tools that turn out to need a shared base. Validate the split with 2-3 rules per side before committing.
"Easiest first" — what NOT to default to
Tempting because it builds confidence and ships something visible quickly. Do not default to it for regulatory rule sets — the easy rules teach you nothing transferable about the hard ones, and the framework crystallizes around the wrong shape. Use it only when you're piloting tooling on a brand-new project and need to prove the pipeline can produce ANY output before sizing the real work.
Grouping rules
The default is one rule per task → one rule per skill directory. This keeps coverage measurable and the TaskBoard clear. Group only when grouping reduces total work without coupling unrelated concerns.
When to bundle
Bundle multiple rules into a single task (and a single check_r###_r###.py file) when ALL of:
- The rules share the same source chunk(s) — looking at the same paragraph of the same regulation
- They share the same input format (e.g., a required-fields table)
- The judgment logic for one rule is a substring or close variant of the next
- A single failure typically implies multiple failures (you can't pass R013 if R015 fails)
Example: R013 / R015 / R017 all check that a specific table on page 3 of the report contains certain mandatory fields. Same chunk, same parse, same verdict shape. Bundle as check_r013_r015_r017.py and create a single task: TaskCreate({id: "R013-R015-R017-skill_authoring", title: "R013/R015/R017 — required-fields table", phase: "skill_authoring"}). The engine's filesystem-derived milestones recognize the grouped check.py and credit all three rule_ids.
When to keep separate
Keep separate when ANY of:
- Rules cite different regulation chapters — even if conceptually related (e.g., R013 disclosure-content and R028 custodian-responsibility — both about reports, but different chapters / different evidence chains)
- Rules need different worker LLM tiers (R005 needs a flagship for nuanced judgment, R001 is regex)
- Rules apply to different document types (one applies only to public-fund reports, another only to private-fund reports)
- One rule's failure mode is a specific failure mode of another (don't bundle parent + child rules — the child's check redundantly re-runs the parent's)
The anti-pattern wording captures the failure case clearly:
If you find yourself writing a unified_qc.py-style monolith that bypasses individual skills, your per-rule skills are wrong. Fix them, don't replace them.
A failure mode worth flagging: a conductor writes a 2,000+ line unified_qc.py that runs all rules at once. The result is cascading errors — every rule's failure corrupts every other rule's verdict, easily producing 15%+ ERROR rates on production checks. Per-rule skills are KC's unit of granularity for a reason.
Anti-pattern: stub check.py + real workflow.py
Do NOT make rule_skills/<id>/check.py a stub that defers to
workflows/<id>/workflow.py. KC's intent: SKILL.md + check.py is the
canonical verification. workflow.py is the distilled, cheaper
form (regex baseline + LLM fallback). The relationship is
skill → workflow, not workflow → skill.
❌ DON'T:
def check(text):
rule_ids = re.findall(r"R\d{3}", load_skill())
return {rid: {"pass": None, "method": "stub",
"note": "to be implemented later"} for rid in rule_ids}
✅ DO:
def check(text):
matches = re.findall(r"...", text)
return {"rule_id": "R001", "passed": bool(matches),
"evidence": matches[:3], "method": "regex"}
def run(text, llm_fn=None):
result = check(text)
if not result["passed"] and llm_fn:
result = llm_verify(text, llm_fn)
return result
Why it matters: distillation phase consumers (release tool, run.py
harness) load workflow.py. If check.py is a stub, the skill's
methodology (SKILL.md) becomes documentation-only and the
verification logic is scattered across N workflow files. Future
iterations of the skill (changes to regulation interpretation, edge
cases discovered in production) need a single canonical place to
update — the skill — not N workflows that have drifted independently.
Two failure modes worth flagging:
The pure-stub failure: bundled-skill check.py files all return {"pass": null, "method": "stub"} deferring to workflows/. Methodology is described in SKILL.md but never executable from the skill folder.
The inverted-canonical failure: the agent avoids stubs (good) but inverts the canonical-vs-distilled relationship — thematic skill folders have only SKILL.md (no check.py), with the real verification code living inside workflows/<skill>/check.py. The absence of stubs is good; the inversion is not — editing a rule then requires touching both SKILL.md (the doc) and the workflow check.py (the code). Single source of truth is lost.
The canonical landing looks like this: every skill has BOTH a substantive SKILL.md AND a real check.py (regex + applicability logic), and workflows/<id>/workflow_v1.py is a thin wrapper (~50 LOC) that imports and calls it:
import importlib.util, json
from pathlib import Path
def run(doc_text: str, meta: dict = None) -> dict:
check_path = Path(__file__).parent.parent.parent / "rule_skills" / "D01-01" / "check.py"
spec = importlib.util.spec_from_file_location("check", check_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
result = mod.check(doc_text, meta)
result["_workflow"] = "D01-01_v1"
return result
This is the canonical pattern: workflow is a shim that points at the skill's check.py. To iterate on a rule's verification, edit rule_skills/<id>/check.py. The workflow doesn't change. The teaching has two parts: avoid stubs AND keep the canonical relationship (skill is canonical, workflow is distilled wrapper).
Naming convention for grouped checks
When you do bundle, name the file with the explicit range:
check_r013_r015_r017.py — three specific rulescheck_r002_r007.py — contiguous range (R002 through R007)check_r013-r017.py — alternative spelling, also accepted
The engine's filesystem-derived milestones parse these names and credit each constituent rule_id. The grouping is documentation as much as code organization — downstream consumers (workflow-run, dashboards, release tool) read the filename to know coverage.
Difficulty estimation — three-axis triage
Before you commit to an order, score each extracted rule on three axes. One quick worker LLM call per rule (tier3 is sufficient — not a deep judgment) writes a rules/difficulty.json that the conductor then reads when deciding TaskBoard order.
Axis 1 — Chain-of-thought depth
How many sequential judgments does the rule require? Count operations the agent has to chain together:
- 1:
text contains "industry-unified channel" (regex)
- 2: classify product type, then check channel (two-step)
- 3+: classify product type, locate disclosure section, parse table, compare against another section's table (multi-step)
Score: 1 / 2 / 3+ on this axis.
Axis 2 — Module count
How many distinct sub-checks does the rule encompass? A "module" is a logically separable predicate.
- 1: single predicate ("must mention channel A")
- 2-3: a small required-fields list ("must mention A, B, C, D")
- 4+: a large checklist or conditional branch ("if public fund, then channels X+Y; if private, then channel Z; in all cases also include the manager identity")
Score: 1 / 2-3 / 4+ on this axis.
Axis 3 — Cross-rule interaction
Does the rule reference another rule, depend on its output, or have to resolve consistency with it?
- 0: standalone (most rules)
- 1: cross-references one other rule (e.g., R007 references R013's table existence)
- 2+: tightly coupled with multiple rules, requires consistency reasoning across them
Score: 0 / 1 / 2+ on this axis.
Total difficulty
Sum the three axes (1+1+0 = 2 minimum, 3+3+2 = 8 maximum). Sort descending. The 2-3 highest are your design-floor cases — work them first.
For a 70-rule corpus, expect difficulty distribution roughly:
- 10-15 hard (sum 5-8)
- 30-40 medium (sum 3-4)
- 20-30 easy (sum 2)
Don't over-engineer the triage. It's a planning aid, not a contract. If during work you discover a rule scored 2 was actually a 6, update PATTERNS.md and re-sort the remaining queue.
PATTERNS.md — project memory discipline
KC's main agent does not have continuous memory across phases. Every time the agent re-reads describeState, it sees the same rule list and the same milestones. Without an external accumulating reference, every rule's design starts from scratch.
rules/PATTERNS.md is that reference. The agent owns it (writes via workspace_file, not via any tool wrapper). The engine surfaces it in every system prompt of skill_authoring + skill_testing. Capped at ~5 KB so token cost stays trivial.
What to write — patterns that transfer
A good PATTERNS.md entry captures something that will SAVE work on the next rule. Three legitimate categories:
✅ Transferable shape — a verdict shape, chunker granularity, or interface decision that subsequent rules will reuse.
R028 (custodian responsibility) needed multi-party verdict shape:
{ primary_party: PASS|FAIL, secondary_parties: [...], reasons: [...] }
Adopting as default for any rule with multiple liable entities.
Confirmed reusable on R029, R031.
✅ Project-level constraint — a fact about the corpus or environment that affects multiple rules.
Sample corpus has bilingual table headings (EN+ZH).
Chunker MUST split on the ZH heading boundary, not the EN one —
verified on 5 sample docs. Without this, R013 / R015 / R017 all
under-extract.
✅ Anti-pattern with rationale — a thing you tried, why it failed, what to do instead.
Tried tier4 for JSON-output verdicts → empty responses 80% of the time.
tier3 (Qwen3.5) hallucinates field names. Settled on tier2 (DeepSeek-V3.2)
for any structured-output rule. Tier1 reserved for verdict reasoning under
ambiguous evidence (R005, R024).
What NOT to write — log-dump anti-patterns
These add token cost without adding decision value. Future-you reading PATTERNS.md is trying to figure out what to do, not reconstruct what already happened.
❌ Completion log — already in tasks.json + filesystem.
R001 done. R002 done. R003 partial pass. R004 done.
❌ Tool history echo — already in events.jsonl.
Called workspace_file to write check_R013.py. Then called sandbox_exec.
Then ran the result through worker_llm_call.
❌ Filesystem-authoritative facts — the engine derives these from disk.
Workflows live under workflows/R001_workflow.py. There are 28 of them.
❌ Conversation summary — neither agent nor user reads PATTERNS.md as narrative.
After discussing with the user, we decided to focus on banking rules first.
The user mentioned that trust products are out of scope.
If a project-level decision came out of conversation, write it as a constraint:
Trust products excluded from this run (D6 applicability NO).
Skip R078, R092, R104 — their skills exist as stubs only.
When to update vs append
- Append when you discover something new and transferable.
- Update an existing entry when work on a later rule reveals a better abstraction. Don't lock yourself into the first hard rule's shape — JIT compilers recompile when profile data invalidates the original assumption; PATTERNS.md should evolve the same way.
- Delete an entry when you discover it was wrong. Mark the deletion with a brief rationale at the bottom of the file:
[DELETED 2026-04-29] "Always use tier1 for FAIL verdicts"
Why: R005 + R007 work fine on tier2; tier1 reserved for genuinely
ambiguous evidence cases only (3 rules across the set).
Sizing
Keep PATTERNS.md under ~5 KB total. If it exceeds, prune the least-actionable entries (the ones that haven't influenced any decision in the last 5 rules). Memory is for what you're using, not what you've seen.
Putting it together — opening sequence
When entering skill_authoring with an empty TaskBoard:
- Read
describeState. Look at the rule list, the milestones (rules with chunk refs / coverage audited), and any existing PATTERNS.md.
- If PATTERNS.md is empty: spend ~2 turns deciding ordering methodology + first 3-5 patterns. Write PATTERNS.md as your first artifact, before any skill code.
- If
rules/difficulty.json exists: sort rules by difficulty descending. Group where appropriate per the rules above. Call TaskCreate for each unit.
- If
rules/difficulty.json doesn't exist: decide whether to spend the worker LLM calls to triage (almost always yes for a corpus of >20 rules). Run the triage step (one tier3 call per rule, batched in groups of 10 if you want), write rules/difficulty.json, then proceed to step 3.
- Pick the first task. Work it to completion (skill + check + at least one local test). Update PATTERNS.md with whatever you learned. Move to the next task.
- At task ~5 and task ~10: stop and re-read PATTERNS.md. If patterns suggest a refactor of earlier work, do it now (cheap) rather than later (expensive).
Calling TaskCreate / TaskUpdate / TaskComplete
The engine registers three task-board tools:
TaskCreate({id, title, phase, ruleId?}) — adds a task to tasks.json. id must be unique within the session; pick a stable shape like <rule_id>-<phase> for per-rule tasks or <group-name>-<phase> for grouped / non-rule tasks. phase is the current phase the task belongs to. ruleId is optional — set it for per-rule tasks so the engine can credit the rule_id in milestone derivation.TaskUpdate({id, status?, summary?}) — change a task's status (pending / in_progress / completed / failed), optionally with a short summary.TaskComplete({id, summary?}) — sugar for TaskUpdate({id, status:"completed", summary}). Use this after finishing a unit of work.
Ralph loop scope — within a phase only
Important contract:
- Loop scope = current phase only. TaskCreate populates tasks for the CURRENT phase. The Ralph loop processes them one by one within the phase.
- Loop exits at phase boundaries. When all current-phase tasks complete OR the phase advances (you call
phase_advance, or anything else changes currentPhase), the loop exits cleanly. Control returns to the user.
- No engine auto-advance. The engine does NOT auto-advance phases when tasks complete + exit criteria are met. Phase advance is YOUR explicit call (
phase_advance tool) or the user's re-prompt.
- Don't pre-create tasks for future phases. They'll be ignored — the loop exits at the phase boundary before processing them. Create tasks only for the phase you're currently in.
- Phase boundaries = user checkpoints. This is intentional. The team needs visibility into progress at natural breakpoints. After your task batch +
phase_advance, the loop exits, you summarize progress in your final message, the user prompts you to begin the next phase.
End-to-end autonomous "run from bootstrap to finalization without stopping" is NOT the engine's job — when that capability ships, it'll be an external driver (/loop-style command) that calls the agent repeatedly across phases. Inside one invocation, work the current phase fully, advance, and return to the user.
Examples:
TaskCreate({ id: "R001-skill_authoring", title: "Author skill for R001",
phase: "skill_authoring", ruleId: "R001" })
TaskCreate({ id: "trust-bundle-skill_authoring",
title: "R013/R015/R017 — required-fields table",
phase: "skill_authoring" })
TaskComplete({ id: "R001-skill_authoring",
summary: "regex check passes 89/90; R001 done" })
Persisted methodology — PATTERNS.md OR phase logs OR AGENT.md decisions
The principle: capture framework-level decisions to disk before each phase advance. The conversation will compact, agents will restart, the next phase will lose grounding. Whichever format you pick, write to disk — don't rely on conversation context that disappears.
Three formats, each defensible. Pick one and stick with it:
-
rules/PATTERNS.md — concise, framework-only, updated as the project progresses. Best for greenfield projects with clear hypothesis-up-front structure. Capped at ~5 KB; entries are transferable shapes / project constraints / anti-patterns with rationale (see "What to write" above).
-
logs/phase_<name>_complete.md per phase — incremental, captures what each phase produced + decisions made + what the next phase inherits. Best for iterative discovery work where the framework crystallizes mid-run. A real example pattern: six phase docs plus an evolution_summary_vN.md capture the methodology even when PATTERNS.md is never written.
-
AGENT.md decisions section + domain notes — narrative-style, living document of "what we know" and "why". Best for projects with rich domain context to capture (regulations, edge cases, thresholds, sample format distributions). An AGENT.md that records regulation enforcement dates, product type taxonomies, threshold values, and sample format counts works perfectly — it's a different idiom for the same goal.
What you should NOT do: skip persistence and rely only on the live conversation context. By the time you have N skills authored without any persisted methodology, you've made N implicit decisions about verdict shape, chunker boundaries, and worker tier. Each rule re-derives from scratch. Refactoring requires touching N files instead of one.
❌ "I'll capture insights when I have time."
✅ "Before each phase advance, write what I learned to whichever persistence file matches this project's idiom — even if it's tentative."
Failure modes worth flagging:
- An agent writes PATTERNS.md only after a rollback. All the per-skill decisions made before that point have to be re-touched. "PATTERNS.md FIRST" exists because of this cost.
- An agent skips PATTERNS.md entirely but writes rich phase-completion logs and a comprehensive AGENT.md. The methodology IS captured, just in different files — which is fine. The broader principle: persist before you advance, format flexible.
The engine's filesystem-derived milestones verify coverage on disk regardless of how you split the work. The TaskBoard is your scratchpad; the disk is the contract; the persistence file is your project's memory.
Subagent batch work: rolling-window writes
When you dispatch N subagents to do batch work (regression tests, batch verification, parallel rule processing), DO NOT have them write to a shared coordination file. A failure mode worth flagging: subagents race on tasks.json / rules/catalog.json / output/results/summary.json — one takes the workspace lock for several minutes while the others wait silently.
The right pattern: each subagent writes to its OWN file under a known prefix. The parent aggregates after all subagents finish.
sub_agents/
batch-001-regression/
output/results/v2_regression.json # ❌ shared — races other subagents
batch-002-regression/
output/results/v2_regression.json # ❌ same path, race
# vs:
output/
batch_regression_001.json # ✓ each subagent owns one file
batch_regression_002.json # ✓
batch_regression_003.json # ✓
# Parent agent reads all batch_regression_*.json and writes the aggregate.
Engine signal: if you see lock_blocked events in events.jsonl during subagent work, that's the symptom — the engine emits this event so the parent has visibility into contention before the subagent times out. If the pattern shows up, refactor to rolling-window writes.
Don't write a "coordinate via file locking" subagent batch. The locking primitives exist for safety against accidental concurrent writes, not as a queue. Use the filesystem layout as the coordination mechanism.
