Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/ackeskin/contexture/human-viewgit clone --depth 1 https://github.com/AcKeskin/contextureWhat 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 | $0.00056 | $0.00318 |
| Opus 5 | $0.00028 | $0.00159 |
| Sonnet 5 | $0.00011 | $0.00064 |
| Haiku 4.5 | $0.00006 | $0.00032 |
Grade A, and why
human-view 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 2d 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.
What it actually says
Run the human-view skill.
Forms:
/human-view <slug>— render the human view of the slug's active artefact (prefers plan → blueprint → spec → vision; asks if ambiguous)./human-view <path>— render the human view of a specific artefact file./human-view <slug|path> --vault— also write a human copy to the Obsidian vault.
Every planning artefact is written token-optimized for the LLM. This renders the opposite: a plain-prose view of what's actually in the LLM's head — the goal in one breath, what was decided (and why it matters), an alignment check (where it narrowed/expanded/reinterpreted your ask), and the open questions — so you can approve it and catch a misalignment before it ships into /execute.
It is a faithful projection (never modifies the source, never invents or softens), library-callable by the /draft-plan and /blueprint review gates, and never auto-fires. The LLM artefact stays the source of truth.
See ~/.claude/skills/human-view/SKILL.md for the full procedure.
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.
- 2d ago First seen · 18 lines · 56 tokens per session scan A 666ca30bcaac
human-view is a command published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 318 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-31.
Other commands, from other repositories
translate-review-to-single-human
Translate a multi-reviewer code review into a single human-voice GitHub PR comment.
address
Address code review feedback — corroborate, validate, and implement changes from a review's final.md.
map
Generate a Code Review Map to help navigate large, complex changesets.
review
Run an AI-powered multi-agent code review on your changes.
sync-reviewers
Sync reviewer metadata from markdown files to reviewers-meta.json for the dashboard.
post
Post the current OCR review to a GitHub PR.