This is the specification the analyses were produced by — the flf-epistack Claude Code skill, published here so the method is auditable without cloning anything. It is the same text the agents actually execute, not a write-up of it.
The pipeline runs as one orchestrator plus per-step child agents. Each step below states what it consumes, what it must write, and the checks that gate it before the next step starts.
Read it
- The skill definition — the entry point: node ontology, folder layout, the rules every step obeys, and the invocation contract.
The ten steps
- Step 0 — Orchestrate the run
- Step 1 — Find primary sources
- Step 2 — Curate sources
- Step 3 — Extract observations, hypotheses, and arguments from a paper
- Step 4 — Structure hypotheses
- Step 5 — Link observations to hypothesis-clusters
- Step 6 — Assess argument validity
- Step 7 — Estimate priors
- Step 8 — Estimate likelihoods per (cluster, observation)
- Step 9 — Per-cluster review of the analysis
- Step 10 — Final report answering the main question
The code
Rendered on GitHub, which does code better than this site does:
runner/run.pyrunner/test_run.pyscripts/attach_argument_backlinks.pyscripts/batch_arguments.pyscripts/batch_likelihoods.pyscripts/build_correlation_groups.pyscripts/check_missing_evidence_links.pyscripts/create_node.pyscripts/curation_select.pyscripts/no_observation_arguments.py
The runner (runner/run.py) is the piece that makes the answer recompute: it extracts the ## Prior and ## Likelihood python blocks from the notes, enforces determinism (arithmetic over named variables only — no imports, randomness, clock or I/O), and composes each cluster’s posterior. runner/test_run.py checks it against the numbers the step 7 and 8 specs publish.
Generated copy
These pages are generated from
pipeline/byscripts/sync-pipeline-to-content.mjs. Edit the source, not these. Wikilinks appearing in the step specs are syntax examples, shown verbatim rather than linked.