| 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:
-
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.
-
Reduce (Subagent Phase):
-
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.
-
Synthesize: Merge the verified findings into a structured investigation report (Thesis).
-
Produce Theses:
-
Log: Update the activity log log.md with: audit query, files examined count, findings count, findings discarded count.
