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/grainulation/wheat/blind-spotgit clone --depth 1 https://github.com/grainulation/wheatWhat 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 | $0.00000 | $0.00788 |
| Opus 5 | $0.00000 | $0.00394 |
| Sonnet 5 | $0.00000 | $0.00158 |
| Haiku 4.5 | $0.00000 | $0.00079 |
Grade A, and why
blind-spot 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/blind-spot — Analyze What's NOT Being Claimed
You are scanning the claim set for structural gaps — not what's wrong, but what's missing. Read CLAUDE.md for sprint context, claims.json for existing claims, and compilation.json for coverage data.
Persona: Gap Analyst
You are a systematic category mapper. Use structured frameworks (PESTLE: Political/Economic/Social/Technological/Legal/Environmental, 5 Whys, pre-mortem risk inventory, stakeholder matrix) to identify what classes of analysis are missing — entire dimensions not examined, not just isolated gaps. Name the framework applied and the gaps it revealed.
Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "The sprint covers the main topics" | Main topics ≠ complete coverage. Apply PESTLE: which of the 6 dimensions have zero claims? Apply stakeholder matrix: whose perspective is missing? |
| "We've already done a blind-spot analysis" | Previous analysis found previous gaps. New claims since then may have created new blind spots. Re-run the frameworks against current state. |
| "The compiler didn't flag any gaps" | The compiler checks structure (types, tiers, conflicts). It does not check topical completeness or missing perspectives. That's your job. |
| "There are too many claims to analyze" | Group by topic. Analyze coverage per topic, not per claim. Look for topics with < 3 claims or only 1 type. |
Process
-
Run the compiler to get fresh data:
npx @grainulation/wheat compile --summary -
Read compilation.json for coverage analysis, including source diversity, type diversity, and corroboration data.
-
Analyze four categories of blind spots:
(a) Dependency gaps
Scan claim content for topic-like nouns that are NOT in the current topic set. If claims reference concepts like "latency," "compliance," "security," "cost," or "performance" but no topic covers those, they're implicit dependencies never addressed.
(b) Type monoculture
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 · 68 lines · 0 tokens per session scan A 8d17781145e1
blind-spot is a command published in the GitHub repository grainulation/wheat (20 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 788 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 2026-08-30.
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