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 skills add jellydn/my-ai-tools --skill blindspot-passgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/skills/jellydn/my-ai-tools/blindspot-pass)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/blindspot-pass"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/blindspot-pass/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/skills/jellydn/my-ai-tools/blindspot-pass"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/blindspot-pass.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00024 | $0.01392 |
| Opus 5 | $0.00012 | $0.00696 |
| Sonnet 5 | $0.00005 | $0.00278 |
| Haiku 4.5 | $0.00002 | $0.00139 |
Grade A, and why
blindspot-pass 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 12d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blind Spot Pass
When to Use
Use this skill before starting implementation when:
- Working on an unfamiliar part of the codebase
- Integrating with systems you don't fully understand
- The task has high stakes or complexity
- You sense there might be hidden gotchas
What It Does
A blind spot pass helps identify unknown unknowns that could derail implementation or lead to poor architectural decisions.
How to Execute
Step 1: Define Scope
Clearly state what you're about to work on:
- Feature or change description
- Affected modules/systems
- Current understanding level
Step 2: Search for Context
Look in multiple places for relevant history:
# Recent changes to related files
git log --oneline --all -20 -- path/to/module/
# Search commit messages for keywords
git log --all --grep="auth\|oauth\|provider" --oneline
# Find related discussions in code comments
rg -i "TODO|FIXME|HACK|XXX" path/to/module/
# Search for error handling patterns
rg "catch|throw|error" path/to/module/ -A 3
Step 3: Identify Gotchas
Document findings in categories:
Technical Gotchas:
- Edge cases in existing code
- Performance considerations
- Error handling patterns
- Testing requirements
Architectural Gotchas:
- Existing abstractions to follow
- Integration points
- Data flow patterns
- State management approaches
Business Gotchas:
- Implicit requirements from past discussions
- Feature flags or phased rollouts
- Backward compatibility needs
- Security or compliance requirements
Step 4: Surface Questions
Generate questions that would change the approach:
- "I see error handling uses X pattern; should I follow it?"
- "There's a feature flag system; does this need one?"
- "Previous auth work had Y consideration; does that apply here?"
Step 5: Present Findings
Format as a structured report:
## Blind Spot Analysis: [Task Name]
### Context Sources Checked
- [x] Git history (last 3 months, 45 commits in auth/)
- [x] Code comments and TODOs (found 3 relevant)
- [x] Error handling patterns (uses Result type)
- [x] Test coverage (integration tests in /tests/auth/)
### Key Findings
**Architectural Patterns**:
- All auth providers extend BaseOAuthProvider
- Must implement 3 lifecycle hooks: init, authenticate, refresh
- Config validation happens at provider registration
**Known Gotchas**:
- GitHub OAuth requires app installation per org (not per user)
- Refresh tokens expire after 6 months of inactivity
- Rate limiting uses Redis; need to handle connection failures
**Testing Requirements**:
- Mock responses in tests/fixtures/oauth/
- Integration tests use test credentials from .env.test
- Must test token refresh flow explicitly
### Questions Before Proceeding
1. Should GitHub provider support org-level installation?
2. How should we handle rate limit failures? (retry? user error?)
3. Is there a preferred library for GitHub OAuth, or implement from scratch?
### Recommended Approach
[Brief recommendation based on findings]
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.
- 12d ago First seen · 200 lines · 24 tokens per session scan A fdd84c4c9ccc
blindspot-pass is a skill published in the GitHub repository jellydn/my-ai-tools (120 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 1,392 once invoked, about $0.0001 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 2026-08-30.
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