skills/smaht/synthesize/SKILL.md
Internal context synthesis engine. Triggered by UserPromptSubmit hook when complexity or risk is above threshold. Runs agentic exploration — facilitator + fan-out subagents — to produce a complete turn directive grounded in project knowledge. NOT user-invokeable. Called automatically by the hook via skill directive injection.
npx skillsauth add mikeparcewski/wicked-garden smaht-synthesizeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You received this skill because the hook determined this prompt needs deep context assembly.
Args are passed as a JSON string. Parse with json.loads(args):
prompt: the user's original promptcomplexity: float 0–1 (hook scoring)risk: bool — high-risk keywords detectedturns: recent session turns summary (condensed, may be absent on first turn)context_briefing: pre-assembled adapter context from the orchestrator (present on SLOW-path only, absent on FAST-path or when orchestrator failed). When present, skip cold exploration in Step 1 and use this as the starting context.| complexity | risk | rounds | parallel agents | |-----------|------|--------|----------------| | < 0.5 | no | 1 | 2 | | 0.5–0.7 | no | 1 | 3 | | > 0.7 | any | 2 | 4 | | any | yes | 2 | 3 |
Before running the full synthesis loop, validate that this prompt actually warrants agentic synthesis. Parse complexity, risk, and (if present) context_briefing from the skill args (all provided as JSON fields).
If context_briefing is present in args, its content can be used to inform all 3 checks below — treat it as verified adapter evidence (native task state, brain index hits, domain events) rather than re-deriving from scratch.
Run these 3 quick checks against the user's prompt:
risk true (destructive/production-affecting keywords detected by the hook)?Decision:
0 of 3 pass → Exit early. Output exactly:
[Scope Check: LOW — routing to fast path]
Then stop. Do NOT produce a context briefing or proceed to Step 1.
1 or more pass → Proceed to Step 1 below.
If context_briefing is present in args: Skip the cold exploration phase. Use the provided briefing as your starting context — it already contains adapter output (brain index hits, native task state, domain events). Proceed directly to Step 3 to assess whether the briefing is sufficient or if targeted follow-up is needed.
If context_briefing is absent: Read the user's prompt and recent turns. Identify 3–5 specific questions:
Do NOT search yet. Just list the questions.
Spawn parallel Agent calls (one per search question). Each agent should:
Brain wiki search (synthesized knowledge — try this first):
find ~/.wicked-brain/wiki -name "*.md" 2>/dev/null | head -20
# Read any wiki article whose name matches the topic
Brain FTS search (raw chunks — for specifics):
curl -s -X POST http://localhost:4242/api \
-H "Content-Type: application/json" \
-d '{"action":"search","params":{"query":"QUERY_TERMS","limit":5}}'
If curl fails (brain server not running), fall back to Grep/Glob on the project files directly.
Recent events (what changed recently):
curl -s -X POST http://localhost:4242/api \
-H "Content-Type: application/json" \
-d '{"action":"search","params":{"query":"QUERY_TERMS event created","limit":3}}'
Each agent returns: what it found (or "nothing relevant — brain unavailable, searched files directly").
Review all agent outputs. Ask:
If missing AND rounds remaining: identify the gap, spawn 1–2 targeted follow-up agents. If sufficient OR no rounds left: proceed to synthesis.
Output ONLY the following block (200–300 words). This replaces Claude's own context assembly.
CONTEXT BRIEFING [smaht-synthesized | complexity={X} | risk={Y}]
**The user is asking**: {one sentence — actual intent, not a restatement}
**What is true** (verified from project knowledge):
- {specific fact 1 with source reference}
- {specific fact 2 with source reference}
- {specific fact 3 with source reference}
[add up to 2 more only if directly relevant]
**Active constraints**: {from session turns — user-stated rules that apply}
**Recommended approach**: {1–2 sentences — what to do and how, based on what was found}
**What was NOT found**: {if any critical info was missing — be explicit}
After outputting the briefing, tell Claude: "Proceed with this context. Answer the original prompt: {prompt}"
development
Pattern-conformance agent-half: evaluates a produced artifact or diff against a set of architectural/design pattern rules from the conformance-rule store (wicked_governance schema). Returns structured findings with rule ID, severity, and rationale — the deterministic half (mechanical rule recall) is done by the guard pipeline; this is the semantic evaluation step. Triggered by: the guard_pipeline `outgov_pattern` check (session-close), or explicitly by an engineering review when WICKED_OUTGOV_RULES_DIR is populated. NOT a replacement for the full `engineering` review skill — focuses only on conformance to stored Pattern rules; architecture and code-quality checks live in the `engineering` skill. Semantic evaluation reuses `wicked-garden-qe-semantic-reviewer` as the designated agent-half evaluator (per garden#983 spec). This skill is the orchestrating wrapper that loads applicable Pattern rules and delegates the per-rule semantic judgment to qe-semantic-reviewer.
tools
The FOUNDATIONAL domain-model capability: extract a codebase's domain — testable business rules (with confidence + provenance), entities, requirements — as a schema-conformant model on the estate graph. The workers annotate the store; wicked-core reads it and builds the requirements graph, coverage-gating fail-closed. Steers three fork workers. A shared substrate, not a modernization tool. The `modernize` archetype DERIVES from it; build / migrate / review / specify / explore consume the SAME domain model — none OWN it. Understanding a codebase's domain is upstream of almost everything else garden does. Use when: "extract the business rules / domain model from this codebase", "build a requirements graph from the code", "what does this system actually require", "reverse-engineer the domain before we build/port/migrate". Works on ANY codebase (modern or legacy) — the value is the domain model, not the porting. NOT the code transform itself (that is the archetype consuming this model). This skill produces the DOMAIN MODEL, not new code.
development
Domain-graph fork worker for the modernize archetype. Groups the estate's Louvain communities into business domains, attaches each requirement to its cluster (advisory cluster_id provenance), and invokes wicked-core's domain-graph build (which reads the annotated estate store, recomputes coverage fail-closed, and builds the requirements graph) — then validates core's output against the vendored schema. Use when: dispatched by wicked-garden-domain after rule extraction to turn a flat rule set into cluster-keyed domains; "group these into domains", "build the requirements graph", "translate clusters into a domain model". NOT for mining the rules themselves (that is domain-extractor) or threat-modeling (that is domain-coverage).
tools
Rule-extraction fork worker for the FOUNDATIONAL domain-model capability. Mines testable business rules from a codebase — each with a numeric confidence and a provenance{source, ref, source_kinds} — and annotates them into the estate store so wicked-core can build the domain-model requirements graph (coverage-gated). This is a substrate, not a modernization tool: the `modernize` archetype DERIVES from it, and build / migrate / review / specify / explore can consume the same domain model — none OWN it. Use when: dispatched by wicked-garden-domain to mine the business_rules of a codebase (or a module); "extract the domain rules", "what does this system require", building the requirements half of a domain model. NOT for grouping into domains (that is domain-modeler) or judging coverage (that is domain-coverage — a seat-distinct evaluator).