Borrowing it
Nothing to install: this file belongs to alfadur7/llm-wiki-newsroom. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alfadur7/llm-wiki-newsroom/main/.claude/commands/wiki-lint-theme-mapping.mdgit clone --depth 1 https://github.com/alfadur7/llm-wiki-newsroomWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/alfadur7/llm-wiki-newsroom/wiki-lint-theme-mapping)<a href="https://agentmods.dev/commands/alfadur7/llm-wiki-newsroom/wiki-lint-theme-mapping"><img src="https://agentmods.dev/badge/commands/alfadur7/llm-wiki-newsroom/wiki-lint-theme-mapping/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/alfadur7/llm-wiki-newsroom/wiki-lint-theme-mapping"><img src="https://agentmods.dev/badge/commands/alfadur7/llm-wiki-newsroom/wiki-lint-theme-mapping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.06522 |
| Opus 5 | $0.00000 | $0.03261 |
| Sonnet 5 | $0.00000 | $0.01304 |
| Haiku 4.5 | $0.00000 | $0.00652 |
Grade A, and why
wiki-lint-theme-mapping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/wiki-lint sub-procedure — Contradiction Theme Mapping (full re-derivation)
Not a slash command — a sub-procedure of /wiki-lint, reached from contradiction theme --fix. Invoking it directly runs nothing.
This guide prescribes the procedure for reading all claims in _contradictions.json and generating _contradictions_themes.json (the theme ↔ claim mapping SoT). Since the output is structured JSON, not markdown prose, journalism · consulting form does not apply (unlike overview authoring). Schema conformance · contradiction-axis accuracy · self-validation are the core.
A Claude with no prior knowledge must be able to read this guide alone and reproduce the same quality · structure.
Read before working:
wiki-lint.md→## Sub-procedure: Conflict Axis Sync Rule(SoT hierarchy · cross-assignment principle)wiki/contradictions/_contradictions.json(main input — all claims; for structure see "Input Data Structure" below)- For Phase 2 only,
wiki/contradictions/_contradictions_themes.json(carrying over the Phase 1 output) - As needed,
wiki/sources/<slug>.md(source originals — Phase 2 Priority Read targets; for structure see "Source File Structure" below)
Input Data Structure
_contradictions.json — Main Input
The top level is a JSON array, and each element is a record with the fields below. python tools/build.py contradictions auto-generates it by extracting - contradicts: [[Hub]] — desc lines from the ## Connections section of source pages.
{
"id": "a709b575",
"source": "sources/agentic-ai-kill-saas-debate.md",
"claim": "[[osi-open-source-ai-definition]] requires training-data disclosure, whereas this document argues that releasing open weights alone is sufficient to count as open.",
"status": "open",
"type": "real",
"type_score": 0.6,
"evidence_strength": 0.42
}
| Field | Meaning | Use in theme derivation |
|---|---|---|
id |
First 8 chars of the SHA1 hash of the claim text. The key referenced by claim_ids in _contradictions_themes.json |
Must use this value verbatim |
source |
Path of the Layer 2-1 source file this claim was extracted from (sources/<slug>.md) |
The actual Read target during Priority Source Read |
claim |
The desc part of the ## Connections - contradicts: [[Hub]] — desc line (after the prefix is removed). The contradiction is summarized in 1–2 sentences |
The primary basis for judging the contradiction axis |
status |
Currently fixed at "open" |
Unused |
type |
Auto-classification result — one of 4 values real · superseded · related · soft (explained below) |
Priority-Read selection · auxiliary grouping judgment |
type_score |
Type-category keyword-match score 0.0–1.0 (sum of regex weights capped at 1.0) | Lower = weaker classification signal (re-classification candidate) |
evidence_strength |
Phase 2 meta-based evidence strength 0.0–1.0 — anchor presence 0.30 + source recency 0.15 + ratio of ## Key Claims primary sources 0.40 + target hub type 0.15 |
Higher = strong contradiction (priority ## Representative Evidence candidate). Lower = weak signal (consider drop) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 372 lines · 0 tokens per session scan A c14e583d489c
wiki-lint-theme-mapping is a command published in the GitHub repository alfadur7/llm-wiki-newsroom (85 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 6,522 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-07.
Other commands, from other repositories
slm-loop
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review
When reviewing the code changes, the agent should apply general best practices to ensure high code quality. Key areas of evaluation include.
mt-onboard
Shortcut to the canonical Memtrace codebase exploration skill.
add-source
Guided add of a new source store to sources/supported-stores.json — evaluate, register, refresh, verify catalog impact.
choose-model
Compare current models across configured AI providers and produce an executable plan without running paid work. Use when the user asks which model or provider should perform a task.
checklist
Generate a custom checklist for the current feature based on user requirements.