Fr-e-d Bootstrap LLM Synthesis
Purpose / When to Activate
Activate:
- As Stage 3 of the
/gaai:bootstrappipeline (afterproject-surface-scanand optional tree-sitter parse) - When the bootstrap orchestrator is ready to distill raw project signals into structured memory entries
- Re-run if Q&A answers (Stage 4) are incorporated after an initial synthesis pass
Produces synthesis_result — structured memory entries ready for consent gate (Stage 5 pre-write gate) and memory ingest.
Clarity Tag Semantics
Each entry carries a clarity field. Use these definitions consistently:
| Value | Meaning |
|---|---|
confirmed |
Evidence found directly in source files (imports, config, explicit declarations). Source[] must include file:line reference. |
inferred |
Reasonable conclusion from structural patterns (directory layout, naming conventions, dep graph). Source[] should list supporting files. |
open-question |
Significant ambiguity remains. Formulate as a question in content. Source[] may be empty. |
declared-by-user |
User stated this explicitly in Q&A answers. Source[] should reference the Q&A input. |
Process
Step 1 — Assemble synthesis context
Build the synthesis_context object that will be injected into the prompt:
synthesis_context:
project_summary:
total_files: surface_scan_result.total_file_count
size_class: surface_scan_result.size_class
estimated_loc: surface_scan_result.estimated_loc
top_languages: surface_scan_result.languages[0..4] # top 5 only
top_directories: surface_scan_result.dir_counts # depth-1 map
ast_context:
available: (tree_sitter_context != null)
symbol_table_summary: tree_sitter_context.symbol_table_summary ?? null
import_graph_summary: tree_sitter_context.import_graph_summary ?? null
hot_entry_points: tree_sitter_context.entry_points[0..9] ?? []
qa_context:
available: (qa_answers != null)
answers: qa_answers ?? []
Log: [bootstrap-llm-synthesis] context assembled — AST: {available}, Q&A: {available}
Step 2 — Build synthesis prompt
Construct the prompt using the template below. Apply cache_control: {type: "ephemeral"} on the system prompt block (prompt caching at static prefixes — a standard cost-optimization for repeated LLM calls).
System prompt (static — cache this block):
You are a precision technical analyst. Your task is to distill raw project signals into
structured memory entries that will serve as the permanent shared brain for an AI
delivery team working on this project.
Requirements:
- Produce between 6 and 8 entries. Never fewer than 4, never more than 10.
- Each entry must be independently useful — no cross-entry dependencies.
- Clarity is non-negotiable: every claim must be traceable to evidence.
- Prefer confirmed over inferred. Flag gaps as open-question.
- Write for a senior developer who has never seen this codebase.
Output format: a JSON array. Each element is an object with exactly these fields:
{
"title": "<short descriptive title, ≤ 80 chars>",
"content": "<body text, ≤ 2000 chars, markdown allowed>",
"category": "<one of: project | architecture | patterns | domains | map | lexicon | decisions>",
"tags": ["<tag1>", "<tag2>"],
"clarity": "<confirmed | inferred | open-question | declared-by-user>",
"source": ["<file:path:line-range or 'qa:question-N' or evidence descriptor>"]
}
Return ONLY the JSON array. No prose before or after. No code fences.
User message (dynamic — include synthesis_context):
Analyze the following project signals and produce distilled memory entries.
## Project surface scan
- Total files: {synthesis_context.project_summary.total_files}
- Size class: {synthesis_context.project_summary.size_class}
- Estimated LOC: {synthesis_context.project_summary.estimated_loc}
- Top languages: {synthesis_context.project_summary.top_languages formatted as "Language (N files)"}
- Directory structure (depth-1):
{synthesis_context.project_summary.top_directories formatted as YAML block}
{if synthesis_context.ast_context.available}
## AST analysis (tree-sitter)
### Symbol summary
{synthesis_context.ast_context.symbol_table_summary}
### Import graph (top entry points)
{synthesis_context.ast_context.hot_entry_points formatted as list}
{endif}
{if synthesis_context.qa_context.available}
## User Q&A answers
{for answer in synthesis_context.qa_context.answers}
Q: {answer.question}
A: {answer.answer}
{endfor}
{endif}
Produce 6-8 memory entries now.
Step 3 — LLM call with retry
Call the Anthropic Messages API with the constructed prompt.
Model: use the primary model available in the current session (Claude Sonnet or equivalent). Do NOT hardcode a model string — use the session default.
Parameters:
max_tokens: 4096
temperature: 0 # deterministic output for caching coherence
Retry policy (AC4):
max_attempts = 3
backoff_seconds = [2, 5, 12] # exponential backoff
for attempt in 1..max_attempts:
try:
response = anthropic_messages_create(...)
break # success
except (RateLimitError, ServiceUnavailableError, TimeoutError) as e:
if attempt == max_attempts: raise RetryExhaustedError(e)
sleep(backoff_seconds[attempt - 1])
continue
except (AuthenticationError, InvalidRequestError) as e:
raise immediately # non-retryable
Log each attempt: [bootstrap-llm-synthesis] LLM call attempt {N} — {status}
Step 4 — Parse LLM response
Tolerant parsing (AC2):
The LLM may wrap the JSON in markdown code fences (json ... ) or include leading/trailing whitespace. Apply this extraction sequence before JSON.parse:
1. Strip leading/trailing whitespace
2. If starts with "```": extract content between first "```" and last "```"
(handle both ```json and ``` variants)
3. Strip any trailing comma before closing `]` (common LLM formatting artifact)
4. Attempt JSON.parse
5. If parse fails: try extracting the largest [...] block via regex /\[[\s\S]*\]/
6. If still fails: raise ParseError with raw response excerpt (first 500 chars) for logging
Schema validation (AC2):
For each parsed element, validate:
required_fields = ["title", "content", "category", "tags", "clarity", "source"]
valid_categories = ["project", "architecture", "patterns", "domains", "map", "lexicon", "decisions"]
valid_clarity = ["confirmed", "inferred", "open-question", "declared-by-user"]
for entry in parsed_array:
errors = []
if missing required_field: errors += ["missing: {field}"]
if category not in valid_categories: errors += ["invalid category: {value}"]
if clarity not in valid_clarity: errors += ["invalid clarity: {value}"]
if typeof tags != "array": errors += ["tags must be array"]
if typeof source != "array": errors += ["source must be array"]
if errors is empty:
valid_entries.append(entry)
else:
parse_errors.append({ entry_index: i, raw: entry, errors: errors })
log: "[bootstrap-llm-synthesis] entry {i} invalid: {errors}"
Partial recovery (AC4): if some entries are invalid, continue with valid_entries. Do NOT abort unless valid_entries is empty.
Step 5 — Apply bounds
Hard cap (AC3):
if len(valid_entries) > 10:
valid_entries = valid_entries[0:10]
log: "[bootstrap-llm-synthesis] WARNING: LLM returned {original_count} entries — capped at 10"
Per-entry length cap (AC3):
for entry in valid_entries:
if len(entry.content) > 2000:
entry.content = entry.content[0:1997] + "…"
log: "[bootstrap-llm-synthesis] entry '{entry.title}' truncated to 2000 chars"
Step 6 — Observability summary
Record token usage from API response (AC5):
token_usage:
prompt_tokens: response.usage.input_tokens
completion_tokens: response.usage.output_tokens
total_tokens: response.usage.input_tokens + response.usage.output_tokens
cached_tokens: response.usage.cache_read_input_tokens ?? 0 # prompt cache hit
Build entry count distribution (AC5):
entry_count_distribution:
total_produced: len(parsed_array) # before bounds/validation
total_valid: len(valid_entries) # after validation
total_invalid: len(parse_errors)
by_category: { "project": N, "architecture": N, ... }
by_clarity: { "confirmed": N, "inferred": N, "open-question": N, "declared-by-user": N }
Log (stdout):
[bootstrap-llm-synthesis] synthesis complete
entries produced: {total_produced} → {total_valid} valid, {total_invalid} invalid
token usage: {prompt_tokens} in / {completion_tokens} out (cache hit: {cached_tokens})
by category: {by_category}
by clarity: {by_clarity}
Output Schema (synthesis_result)
synthesis_result:
synthesized_at: "2026-04-29T12:00:00Z" # ISO timestamp
entries:
- title: "TypeScript monorepo with pnpm workspaces"
content: "Project uses pnpm workspaces with 4 packages: ..."
category: project
tags: [typescript, pnpm, monorepo]
clarity: confirmed
source: ["package.json:1-5", "pnpm-workspace.yaml:1-10"]
# ... 5-9 more entries
token_usage:
prompt_tokens: 1840
completion_tokens: 612
total_tokens: 2452
cached_tokens: 1240
parse_errors:
- entry_index: 3
errors: ["invalid category: infrastructure"]
entry_count_distribution:
total_produced: 8
total_valid: 7
total_invalid: 1
by_category:
project: 2
architecture: 2
patterns: 1
map: 1
decisions: 1
by_clarity:
confirmed: 4
inferred: 3
open-question: 0
declared-by-user: 0
Quality Checks
valid_entriescount ≥ 4 (synthesis failed if fewer than 4 valid entries)- Each valid entry has all 6 required fields
token_usage.total_tokens> 0 (LLM was actually called)synthesized_atis presentparse_errorsis a list (may be empty — that is fine)- No entry
contentexceeds 2000 chars after bounds step
Non-Goals
This skill must NOT:
- Write to memory directly (use
memory-ingestafter Stage 5 consent gate) - Make decisions about which entries to persist (that is the consent gate's job)
- Run tree-sitter parsing (use
tree-sitter-parsefor Stage 0c) - Ask the user questions (Q&A is Stage 4, inputs arrive pre-answered)
- Modify the surface scan result (read-only input)
Synthesizes signals into structured entries. Does not persist them.