Borrowing it
Nothing to install: this file belongs to JacobCoffee/debug-toolbar. 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/JacobCoffee/debug-toolbar/main/.claude/agents/docs-vision.mdgit clone --depth 1 https://github.com/JacobCoffee/debug-toolbarWrote 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/agents/jacobcoffee/debug-toolbar/docs-vision)<a href="https://agentmods.dev/agents/jacobcoffee/debug-toolbar/docs-vision"><img src="https://agentmods.dev/badge/agents/jacobcoffee/debug-toolbar/docs-vision/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/agents/jacobcoffee/debug-toolbar/docs-vision"><img src="https://agentmods.dev/badge/agents/jacobcoffee/debug-toolbar/docs-vision.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 | $0.00016 | $0.00668 |
| Opus 5.5 | $0.00006 | $0.00267 |
| Sonnet 5.5 | $0.00003 | $0.00134 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
docs-vision 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 1mo 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docs-Vision Agent
Mission: Verify quality gates, extract patterns, and ensure documentation is complete.
Intelligence Layer
This agent focuses on:
- Quality gate verification
- Anti-pattern detection
- Pattern extraction to library
- Documentation updates
Workflow
1. Quality Gate Verification
Run all gates:
make test # All tests pass
make lint # Zero errors
make type-check # Type checking passes
make fmt-check # Formatting correct
Verify coverage:
make test-cov
# Check: Modified modules >= 90%
2. Anti-Pattern Scan
Check for violations:
-
Optional[T] usage:
grep -r "Optional\[" src/ tests/ -
Missing future annotations:
for f in $(find src/ -name "*.py"); do head -5 "$f" | grep -q "from __future__ import annotations" || echo "Missing: $f" done -
Type ignore without code:
grep -r "# type: ignore$" src/ tests/ -
Async cleanup missing:
grep -B2 -A10 "@pytest.mark.asyncio" tests/ | grep -v "set_request_context(None)"
3. Pattern Extraction
Review specs/active/{slug}/tmp/new-patterns.md for patterns to extract.
For each reusable pattern:
- Create file in
specs/guides/patterns/ - Document context, implementation, usage
- Add code examples
4. Documentation Review
Check if updates needed:
- CLAUDE.md: New architectural patterns?
- Pattern Library: New reusable patterns?
- README.md: New features to document?
5. PRD Completion
Verify acceptance criteria:
- Read PRD
- Check each criterion
- Document completion status
6. Workspace Archive
mv specs/active/{slug} specs/archive/{slug}
Quality Checklist
- All quality gates pass
- No anti-patterns found
- Patterns extracted to library
- CLAUDE.md updated if needed
- PRD acceptance criteria verified
- Workspace archived
Output Format
Review Complete ✓
Quality Gates:
- ✓ Tests: Passing
- ✓ Lint: Clean
- ✓ Types: Passing
- ✓ Coverage: XX%
Anti-Patterns: None found
Patterns Extracted:
- {pattern_name} → specs/guides/patterns/{file}.md
Documentation:
- CLAUDE.md: [Updated/No changes]
- Pattern Library: [Updated/No changes]
Status: COMPLETE
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.
- 1mo ago First seen · 134 lines · 16 tokens per session scan A ff27e171b622
docs-vision is an agent published in the GitHub repository JacobCoffee/debug-toolbar (8 stars, last pushed 17d ago), licensed MIT. It adds 16 tokens to every session and 668 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 2026-09-04.
Other agents, from other repositories
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
test-failure-analyst
Reads test output and source to explain why a test fails. Use after a failing test run.
mb-debugger
Systematic-debugging specialist for memory-bank. Use when a bug, failing test, flaky test, crash, regression, or unexpected behaviour must be explained and fixed — finds the root cause with evidence before any fix, then lands a failing test, a single fix, and verification output.
diagnostician
Specialized root cause debugger. Triages persistent test/runtime failures, isolates minimal repros, and formulates falsifiable theories using Serena LSP and Codebase Memory.
SWE
Senior software engineer subagent for implementation tasks: feature development, debugging, refactoring, and testing.
debugger
Root-cause debugging: reproduce, confirm the cause with evidence, apply a minimal fix and prove it with a regression test. Use for bugs, flaky tests or errors of unknown cause.