tylerburleigh

Task preparation skill for spec-driven workflows. Reads specifications, identifies next actionable tasks, and creates detailed execution plans. Use when ready to implement a task from an existing spec - bridges the gap between planning and coding.

Validate SDD JSON specs, auto-fix common issues, generate detailed reports, and analyze dependencies.

Targeted query capabilities for machine-readable codebase documentation with cross-reference tracking, call graph analysis, and workflow automation. Enables fast lookups of classes, functions, dependencies, and function relationships without parsing source code.

LLM-powered documentation generation for narrative architecture docs, tutorials, and developer guides. Uses AI consultation to create contextual, human-readable documentation from code analysis and spec data.

Plan-first development methodology that creates detailed specifications before coding. Use when building features, refactoring code, or implementing complex changes. Creates structured plans with phases, file-level details, and verification steps to prevent drift and ensure production-ready code.

AI-powered PR creation after spec completion. Analyzes spec metadata, git diffs, commit history, and journal entries to generate comprehensive PR descriptions with user approval before creation.

Comprehensive pytest testing and debugging framework. Use when running tests, debugging failures, fixing broken tests, or investigating test errors. Includes systematic investigation workflow with external AI tool consultation and verification strategies.

Review implementation fidelity against specifications by comparing actual code to spec requirements. Identifies deviations, assesses impact, and generates compliance reports for tasks, phases, or entire specs.

Quick preliminary research on a supplement or question. Searches for promising evidence that might warrant full deep research. Outputs a structured scouting brief to wiki/scouting/ — kept separate from the main wiki until promoted through deep research and ingest. Use when: "is X worth researching deeper?", "what's the preliminary evidence for X?", "$scout taurine cognitive", "should I research quercetin?". Do NOT use for comprehensive research (use foundry-research deep-research instead) or for wiki ingestion (use wiki-ingest).

Ingest a source into the supplements wiki, or re-audit a past ingest. Use for foundry-research reports such as "ingest the sulforaphane research" or "$wiki-ingest sulforaphane"; arbitrary sources in raw/ such as "ingest the source at raw/foo.pdf" or "$wiki-ingest raw/foo.md"; and audit-only mode such as "$wiki-ingest --audit-only sulforaphane" or "what did we miss in the sulforaphane ingest?" Orchestrates staging, pre-check, repo-local extractor and auditor role instructions, and gap-report append. Does not commit to git; the human reviews first.

Health-check the supplements wiki structurally and conceptually. Use when the user says "lint the wiki", asks whether the vault is clean, asks for maintenance findings, or wants low-risk structural fixes. Runs deterministic lint first, then performs targeted judgment-dependent review. Does not commit to git.

Answer substantive questions from the supplements wiki and optionally file reusable answers into wiki/queries. Use for practical supplement questions, comparisons, stack decisions, evidence questions, mechanism questions, safety checks, and "what does the wiki say about..." prompts. Does not commit to git.

Make a scoped corrective edit to existing supplement wiki pages when the problem is already known. Use for audit findings, attribution mismatches, broken source links, stale status fields, scope qualifiers, metadata drift, or other narrow page issues that do not require a full re-ingest. Does not commit to git.

Run a periodic supplements wiki review. Use when the user says "run a wiki review", "monthly review", "quarterly review", "review the supplements vault", or asks what needs attention beyond a single ingest/query.

Ingest a source into the supplements wiki, or re-audit a past ingest. Use for two source types: (1) foundry-research reports — "ingest the sulforaphane research", "/wiki-ingest sulforaphane"; (2) arbitrary sources in raw/ — "ingest the source at raw/foo.pdf", "/wiki-ingest raw/foo.md". Also handles audit-only mode — "/wiki-ingest --audit-only sulforaphane", "what did we miss in the sulforaphane ingest?". Orchestrates the pipeline — stage source, present the pre-check, run wiki-extractor, run wiki-auditor, append the gap report. Does not commit to git; the human reviews first.

Health-check the supplements wiki structurally and conceptually. Use when the user says "lint the wiki", asks whether the vault is clean, asks for maintenance findings, or wants low-risk structural fixes. Runs deterministic lint first, then performs targeted judgment-dependent review. Does not commit to git.

Use when revising the public current LEV story, homepage State of LEV story, what to watch next, where better evidence is needed, or story revision triggers. Compares the current story against public outlooks, state-of-the-field editions, public updates, and review due dates without introducing new unreviewed evidence.

Use when coordinating agent-led LEV reconciliation across public updates, State of the Field editions, current LEV story, hallmark insights, track pages, and editorial workflow manifests. Produces structured agent recommendations, asks the curator for approval or revision only when meaningful, then executes approved work through the specialist LEV skills and verification commands.

Use when creating or revising LEV State of the Field editions, especially monthly public updates that need to explain what changed, what did not change, why it matters, and whether the evidence makes LEV look closer without introducing unreviewed evidence.

Use when checking what changed since the last LEV review for one scope. Compares public records, evidence audit trails, PubMed, registries, and other current external sources, then stages JSON plus a surveillance bundle without publishing.

Use when initializing LEV coverage for one hallmark, track, or intervention family. Stages source/study/finding/outlook evidence-map JSON plus a candidate bundle without publishing.

Use when reviewing or publishing approved LEV candidate bundles. Compares staged JSON to live records and evidence-audit mappings, then uses the file-backed publish workflow.

Use when verifying a LEV candidate bundle. Checks source fidelity, interpretation, and evidence-audit mappings; writes evidence-review records without publishing.

Use when producing a cross-record synthesis report for one domain or bounded set of taxonomy scope units after baseline review or review update records have been published.

Use when deciding whether an appraised candidate bundle should receive comments, changes requested, rejection, approval, or publication.

Use when verifying one appraisal lane for one candidate bundle revision before editorial approval or publication.

Use when checking an already scoped research unit for material changes since the latest baseline-review pass, publication, or review-update pass.

Use when drafting or revising paper-facing literature-review prose from already-collected research records, especially background sections that need inline source citations for substantive claims.

Use when initializing baseline coverage for one bounded research scope unit by staging source, artifact, finding, and claim records into a candidate bundle.

Use when deciding what needs attention now in the active lit-review-studio domain, including coverage gaps, bundle readiness, source-access issues, source-summary gaps, and thin claim support.

Extract source-backed evidence into the longevity hallmarks knowledge base. Use when Codex is asked to turn papers, PubMed records, trial registries, preprints, reviews, or primary documents into structured source, study, finding, outcome, result, eligibility_decision, risk_of_bias, candidate_change, or evidence_review records.

Run bounded research passes for the longevity hallmarks evidence knowledge base. Use when Codex is asked to start, continue, plan, or record a track-level research pass; choose the next research work scope; create or update research_session, coverage_assessment, or candidate_change records; or decide whether a pass is bootstrap, surveillance, coverage repair, extraction refresh, or synthesis refresh.

Validate, maintain, and audit the longevity hallmarks evidence knowledge base. Use when Codex is asked to check data health, find broken references, inspect schema drift, verify candidate changes or evidence reviews, audit taxonomy coverage, prepare release readiness, or maintain repository quality.

Run a structured periodic review (weekly, monthly, or quarterly). Orchestrates the full review workflow — pull data, validate structure, work through checklist, include bear-case/disconfirming evidence, and write the review note. Use when the user says "run a weekly review", "time for the monthly review", "let's do the quarterly", or when briefing.py flags an overdue review.

Run a weekly thesis-relevant news scan. Reads all active thesis/theme docs, runs targeted Tavily searches per thesis, checks for hypothesis triggers and disconfirming evidence, and writes a dated scan artifact to scans/. Use when the user says "run the weekly scan", "scan for news", "check thesis triggers", "what happened this week", or when the research methodology weekly cadence is due.

Research a specific item from docs/research-backlog.md using trusted sources defined in docs/research-methodology.md. Fetches evidence, summarizes findings with proper claim typing, updates the relevant thesis/theme doc, and marks the item resolved. Use when the user says "research B001", "work through the backlog", "resolve item B005", "look into [claim]", or similar.

Use when extracting facts from saved real-estate listing HTML in raw/, cleaning source-specific listing data, filling missing fields in data/heritage_sales.csv through reviewable candidates, proposing supplemental model features, or preserving evidence/conflict flags for property sale-price analysis.