wiki-audit

name	wiki_audit
description	Audit the compiled wiki pages to cross-check statements, highlight scientific contradictions, and output theses using a Map-Reduce architecture.
commands	{"audit":"Audit the wiki contents using Map-Reduce to identify and flag contradictions or inconsistencies."}

LLM Wiki — Audit Skill (wiki_audit)

Resolving script paths (read first): Commands below invoke scripts as <BIN>/X.py (and a few as <SKILLS>/...). Resolve these to absolute paths once before running anything:

• <SKILL_DIR> = the directory this SKILL.md lives in.
• <SKILLS> = the skills/ folder containing this skill = <SKILL_DIR>/..
• <BIN> = the bin/ folder beside it = <SKILL_DIR>/../../bin

Do not hardcode a fixed prefix like .agents/bin or ../bin: shell relative paths resolve against the current working directory (usually the topic root), not this skill's location. Once resolved, <BIN> is typically .agents/bin when invoked from the hub root, or .claude/bin from inside a topic directory.

This skill handles factual auditing, truth-seeking evaluations, and thesis-driven investigations across the compiled knowledge base. To prevent context window limits on large vaults, it strictly uses a Map-Reduce architecture.

Tooling (framework-agnostic): This skill is written tool-agnostic. Map each capability to your own agent's tool — read-file (Read in Claude Code, view_file in Antigravity), sub-agent / parallel task (Task/Agent in Claude Code, invoke_subagent in Antigravity), shell (Bash/PowerShell). Use the closest equivalent your framework provides; if a parallel sub-agent tool is unavailable, audit each file subset sequentially yourself.

When the user asks to perform an audit or truth check on their vault:

1. Map (Deterministic Inventory — SCRIPT FIRST):

   • Run the wiki inventory script to get a deterministic file listing — do NOT manually browse or rely on grep keywords alone: python <BIN>/llm-wiki.py stats <TOPIC_DIR> wiki-summary
   • Parse the JSON output to understand the vault structure: total files, per-directory counts, file titles, and which files have sources.
   • Graph Analysis (MANDATORY): Run python <BIN>/llm-wiki.py graph to ensure the local graph database is strictly up to date. Do NOT skip this, otherwise you will read stale data!
   • Then query the knowledge graph using python <BIN>/query-graph.py "<SQL>". Do not use direct sqlite3 command line execution. Graph DB Schema:
     • nodes(id TEXT PRIMARY KEY, path TEXT, title TEXT, type TEXT, category TEXT, summary TEXT, created TEXT, updated TEXT)
     • edges(source_id TEXT, target_id TEXT, type TEXT)
     • tags(node_id TEXT, tag TEXT)
     • aliases(node_id TEXT, alias TEXT) Example Queries:
     • SELECT path FROM nodes WHERE category='reference' AND id IN (SELECT node_id FROM tags WHERE tag='quantum-mechanics')
     • SELECT n.path, e.type FROM nodes n JOIN edges e ON n.id = e.target_id WHERE e.source_id = 'some-concept-id'
   • Use the inventory and graph results to select the files most relevant to the user's audit query. Then use python <BIN>/search-wiki.py "<regex>" <files...> for targeted keyword searches within those specific files.
   • Do NOT attempt to read all compiled cards manually.

2. Reduce (Subagent Phase):

   • Use your agent's sub-agent / parallel-task tool to spawn one or more "Audit Subagents". Assign each subagent a specific subset of the relevant files. (If no sub-agent tool exists, audit each subset sequentially yourself.)
   • Subagent Output Contract (MANDATORY): Each subagent MUST structure findings as:

     CLAIM: "<exact quote from conflicting file>"
     EVIDENCE: "<exact quote from file>"
     SOURCE_TYPE: local_wiki
     SOURCE: <wiki file path>
     CONTRADICTS_SOURCE: <conflicting wiki file path>
     SEVERITY: high|medium|low
     EXPLANATION: <why these claims conflict>
     

   • If a subagent fails or times out, log the failure and proceed with available results.

3. Verify Citations (MANDATORY):

   • Save all subagent outputs to scratch/temp_claims.txt.
   • Run python <BIN>/verify_claims.py scratch/temp_claims.txt --topic-dir "<TOPIC_DIR>"
   • Discard any finding that is reported as [UNVERIFIED]. Log discarded findings separately.

4. Synthesize: Merge the verified findings into a structured investigation report (Thesis).

5. Produce Theses:

   • Save the compiled Thesis report under wiki/theses/YYYY-MM-DD-<slug>.md with proper YAML frontmatter:

     ---
     title: "Thesis: <descriptive title>"
     type: thesis
     category: reference
     created: YYYY-MM-DD
     sources:
       - <list of wiki files examined>
     tags: [audit, thesis]
     confidence: <high|medium|low>
     summary: "<1-2 sentence summary of findings>"
     ---
     

   • Post-Write Validation (MANDATORY): Run: python <BIN>/validate-output.py "<thesis_file>" --schema thesis --wiki-root "<TOPIC_DIR>" If validation fails, fix the reported issues before proceeding.
   • Run: python <BIN>/llm-wiki.py stats <TOPIC_DIR> verify-refs "<thesis_file>" to ensure all [[references]] in the thesis point to existing files.

6. Log: Update the activity log log.md with: audit query, files examined count, findings count, findings discarded count.