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 | $0.00018 | $0.01424 |
| Opus 5.5 | $0.00007 | $0.00570 |
| Sonnet 5.5 | $0.00004 | $0.00285 |
| Haiku 4.5 | $0.00002 | $0.00142 |
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 21d 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 — 201 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.
- 21d ago Changed · +1 lines f1e810da5f3c
- 1mo ago Changed · -6 tokens per session 3f0026607305
- 1mo 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 (124 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,424 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
ask-first
A decision guide for handling unclear requests before taking action. It uses one focused question or probe when the requested goal, context, or boundaries are not clear.
insight-miner
A manually triggered writing guide that turns notes, debugging records, design summaries, code snippets, or rough drafts into Chinese technical blog posts. It first identifies and evaluates possible article topics before writing.
book-recommender
A Chinese-language book recommendation and analysis skill that evaluates what books are about, whether they are worth reading, and how to read them.
notes-to-blog
A manually triggered guide for turning technical notes and rough material into a Chinese programming blog post. It first extracts and evaluates the main transferable idea, then writes the article after the user confirms its direction.
wisdom-decoder
A guided explanation skill for ideas from Buddhism, philosophy, and psychology. It presents a chosen topic in everyday language and connects it to practical actions, sources, and perspectives from other fields.
teach-me
A five-step teaching workflow for introducing an unfamiliar topic and guiding the learner toward applying it. It moves from an example and explanation to supported practice and an independent task.