Synthesis

Synthesis

You are performing multi-source research synthesis — aggregating, comparing, and composing insights from multiple sources (papers, blog posts, docs, discussions, code) into a coherent answer. The goal is a single composed response that draws on all sources, not a list of summaries.

This skill exists because loading 3+ full web pages or documents into your main context window is wasteful and noisy. The pattern (Recursive Language Model — see the RLM substrate section in CLAUDE.md) is: fetch each source, store it in shared state, dispatch a sub-agent per source to extract key claims, then dispatch a final synthesis sub-agent to compose the answer.

This skill complements the existing research skill. Research handles single small sources (fetch → read → answer). Synthesis handles the cases where multiple sources or large sources make direct reading impractical.

When to use

Trigger this skill when ANY of these hold:

• 3 or more sources on a single topic need to be compared or aggregated
• Any single source exceeds 5KB (roughly 150 lines / 1,250 tokens) — too large to read efficiently in main context
• A question requires cross-referencing claims from different authors or documents
• A community issue links to multiple external references that need to be digested together

When NOT to use

• 1-2 sources, all under 5KB. Use the research skill's single-source procedure — direct curl and read. Sub-agent overhead exceeds the savings.
• The sources are code files, not prose. Use explore-codebase instead — it's optimized for structural code comprehension, not prose synthesis.
• You already know the answer. Don't synthesize to confirm what you already understand. That's burning sub-agent budget for validation theater.
• You're inside a sub-agent at depth 3. Stop. Return what you have. Do not dispatch further.

Procedure

1. Frame the research question (single sentence)

Examples of well-framed questions:

• "What are the tradeoffs between streaming JSON parsing libraries in Rust (serde_json, simd-json, sonic-rs)?"
• "How do different coding agents (Aider, Continue, Cursor) handle context window management?"
• "What does the academic literature say about recursive LLM agent architectures?"

A good question names a specific topic and what you want to learn. Don't ask vague questions like "tell me about Rust".

2. Gather sources

Fetch each source via bash. Typical patterns:

# Web page
curl -sL "https://example.com/article" | sed 's/<[^>]*>//g' | head -500

# GitHub README
curl -sL "https://raw.githubusercontent.com/org/repo/main/README.md"

# Documentation page
curl -sL "https://docs.rs/crate/latest/crate/" | sed 's/<[^>]*>//g' | head -500

# Search results (to find sources)
curl -s "https://lite.duckduckgo.com/lite?q=your+query" | sed 's/<[^>]*>//g' | head -60

Aim for 3-7 sources. More than 7 rarely adds insight — diminishing returns set in fast.

3. Decide: direct synthesis or sub-agent dispatch?

Estimate total content size:

echo "$SOURCE_1" | wc -c
# repeat for each source

• Total < 5KB across all sources: Synthesize directly in your main context. Skip sub-agents — the overhead isn't worth it.
• Total ≥ 5KB: Proceed with sub-agent dispatch (Step 4).

4. Store sources in shared state

Store each source under a namespaced key:

shared_state set key="synthesis.<topic>.source-1" value="<source 1 content>"
shared_state set key="synthesis.<topic>.source-2" value="<source 2 content>"
shared_state set key="synthesis.<topic>.source-3" value="<source 3 content>"

Namespace convention: synthesis.<topic>.source-<N> where <topic> is a short kebab-case slug (e.g., synthesis.rust-json-parsers.source-1).

4a. Chunking for large sources (>30KB)

If any single source exceeds 30KB (~120,000 bytes):

1. Split into chunks of ~25KB at paragraph boundaries (double newline \n\n). Don't split mid-sentence.
2. Store each chunk separately:

   shared_state set key="synthesis.<topic>.source-<N>.chunk-1" value="<first ~25KB>"
   shared_state set key="synthesis.<topic>.source-<N>.chunk-2" value="<next ~25KB>"
   

3. Dispatch one sub-agent per chunk (same prompt as Step 5, but referencing the chunk key instead of the source key).
4. Merge chunk summaries before the final synthesis: combine key_claims and key_quotes from all chunks of the same source, deduplicate, and store the merged result as the source's summary.

5. Dispatch per-source sub-agents

For each source, dispatch a sub-agent with a focused extraction question. One source per sub-agent — sources are the natural unit of synthesis.

sub_agent: You are extracting key claims from a research source.

The source is stored in shared state under key "synthesis.<topic>.source-<N>".
Read it with: shared_state get key="synthesis.<topic>.source-<N>"

Research question: <your single-sentence question from step 1>

Extract the source's relevant claims and evidence. Reply with ONLY a JSON object (no markdown fences, no prose):
{
  "key_claims": ["claim 1", "claim 2", ...],
  "key_quotes": ["exact quote or close paraphrase with attribution", ...],
  "relevance": "high|medium|low",
  "confidence": 0.0-1.0,
  "source_type": "paper|blog|docs|discussion|code|other",
  "deeper_question": "a follow-up question if something is unclear, or null"
}

Skills do not chain. Sub-agents don't load this skill or any other; include the full question and shared-state key reference directly in the sub-agent's prompt.

5a. Handle sub-agent responses

Parse each sub-agent's response as JSON:

1. Valid JSON with all fields: Store the summary in shared state under synthesis.<topic>.summary-<N>.
2. Malformed JSON but readable text: Extract what you can. Construct a partial summary: {"key_claims": ["<first 300 chars of response>"], "key_quotes": [], "relevance": "low", "confidence": 0.2, "source_type": "other", "deeper_question": null}.
3. Empty or errored: Fall back to direct read of the source via curl | head -100. Produce a low-confidence summary manually from what you can see.

5b. Recurse on deeper questions

If a sub-agent returns a non-null deeper_question AND confidence < 0.5:

1. Dispatch another sub-agent with the narrower question, referencing the same shared-state key.
2. Merge the answer into the existing summary (append new claims, update confidence).

Hard cap: recursion depth = 3. That's: initial dispatch → 1st recursion → 2nd recursion. After depth 3, accept whatever you have. If you find yourself wanting depth 4, your original question was probably too vague — go back to Step 1 and narrow it.

6. Store summaries and dispatch synthesis sub-agent

After all per-source summaries are collected, store them together:

shared_state set key="synthesis.<topic>.summaries" value="<JSON array of all summaries>"

Then dispatch a synthesis sub-agent to compose the final answer:

sub_agent: You are synthesizing research from multiple sources into a composed answer.

The per-source summaries are stored in shared state under key "synthesis.<topic>.summaries".
Read them with: shared_state get key="synthesis.<topic>.summaries"

Research question: <your single-sentence question from step 1>

Compose a synthesis that:
1. Identifies areas of AGREEMENT across sources
2. Identifies areas of DISAGREEMENT or tension
3. Notes any gaps — important aspects of the question that no source addressed
4. Weighs claims by source confidence and relevance

Reply with ONLY a JSON object (no markdown fences, no prose):
{
  "answer": "3-5 paragraph composed answer to the research question",
  "consensus": ["claims that multiple sources agree on"],
  "disagreements": ["claims where sources conflict, with attribution"],
  "gaps": ["aspects of the question not covered by any source"],
  "confidence": 0.0-1.0,
  "source_count": <number of sources that contributed>
}

7. Use the synthesis

The synthesis sub-agent's answer field is your composed response. Use it to:

• Answer the user's original question
• Inform a technical decision in an evolve session
• Write a journal entry or issue comment with cited sources
• Add to memory/learnings.jsonl if the finding is novel and would change future behavior

Store the final synthesis in shared state under synthesis.<topic>.result so it can be referenced later in the session without re-running.

Relationship to the research skill

The research skill finds and fetches sources. This skill processes and composes them. They're complementary: research is the scout, synthesis is the analyst.

Pitfalls

• Don't ask sub-agents to make decisions. They extract claims and evidence; you (or the synthesis sub-agent) compose the answer. Per-source sub-agents that try to answer the whole question tend to hallucinate beyond their single source.
• Don't dump multiple sources to one sub-agent. One source per dispatch keeps the extraction focused and the JSON output reliable. Cross-source reasoning belongs in the synthesis step (Step 6).
• Don't forget the recursion cap. 3 is the hard limit. If you find yourself wanting depth 4, your research question was too broad — narrow it.
• Don't synthesize without a question. "Research topic X" is not a question. "What are the tradeoffs of X vs Y for use case Z?" is. The question shapes what each sub-agent extracts.
• Don't over-fetch. 3-7 sources is the sweet spot. More than 7 sources means you're probably not filtering enough — use search to find the 5 best sources, not all sources.
• Don't re-synthesize within the same session. If you've already synthesized a topic, the result is in shared state under synthesis.<topic>.result. Read it with shared_state get instead of re-dispatching sub-agents.
• Skills do not chain. Sub-agents can't load skills. Every sub-agent prompt must be self-contained — include the question and the shared-state key reference directly.

Verification

A synthesis is "good enough" when ALL of:

• The answer addresses the specific research question (not a generic overview of the topic)
• Multiple sources contributed claims to the answer (not just one source restated)
• Areas of agreement and disagreement are explicitly identified
• The answer cites specific claims to specific sources (even if informally — "the docs.rs page says X while the blog post argues Y")
• The total work used ≤ N+2 sub-agent dispatches where N is the number of sources (N per-source + 1 synthesis + 1 possible recursion)
• The work stayed within the depth-3 recursion cap

If the synthesis fails any of these, either add another source to fill the gap, or accept the partial result and note the open question.

What this skill deliberately does NOT do

• Does not find sources. Source discovery is the research skill's job (search → evaluate → pick). This skill takes sources as input and produces synthesis as output.
• Does not modify code. Synthesis produces understanding, not changes. If the synthesis informs a code change, that's a separate task.
• Does not write to the audit-log branch. Synthesis results live in shared state for the current session only.
• Does not replace human judgment. The synthesis is a starting point for decisions, not a verdict. Cross-reference with your own experience and the project's context before acting on synthesis results.