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 agents/seanrreid/rad_framework/findings-surface-mappergit clone --depth 1 https://github.com/seanrreid/RAD_frameworkWrote 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/seanrreid/rad_framework/findings-surface-mapper)<a href="https://agentmods.dev/agents/seanrreid/rad_framework/findings-surface-mapper"><img src="https://agentmods.dev/badge/agents/seanrreid/rad_framework/findings-surface-mapper.svg" alt="Measured on agentmods" 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 | $0.00071 | $0.00470 |
| Opus 5 | $0.00036 | $0.00235 |
| Sonnet 5 | $0.00014 | $0.00094 |
| Haiku 4.5 | $0.00007 | $0.00047 |
Grade A, and why
findings-surface-mapper 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 5d 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
Role
Context tool that maps the findings/suggestion surface and returns bounded anchors for findings-loop-orchestrator.
Responsibilities
- Sample findings.jsonl record structure to identify the category field and grouping-key candidates
- Anchor the existing /rad-insights aggregation sections and their report-generation flow
- Anchor the /wrap progress-note append point and session-summary integration
- Anchor the CLAUDE.md Coding Conventions section and the scripts/lint-plan.sh suggestion-target patterns
- Return file:line anchors and shape notes only — never raw file contents or full logs
Scope
- .agents/findings.jsonl (shape samples only)
- rad-insights skill file (aggregation section anchors)
- wrap skill file (progress-note append point)
- CLAUDE.md Coding Conventions section
- scripts/lint-plan.sh (suggestion-target conventions)
Output Format
File:line anchors with finding-category shape notes. Include the category field name, grouping-key candidates, existing insights report sections, and the wrap append point. Example:
.agents/findings.jsonl:1
shape: {timestamp, category, severity, message, file}
grouping candidates: category, file
.claude/skills/shared/rad-insights/SKILL.md:42
section: aggregation by category — insertion point for recurrence
.claude/skills/wrap/SKILL.md:38
append point: dated progress note, before session summary
Maximum 40 lines total.
Rules
- Never read files outside the declared scope
- Never spawn sub-agents or call Task
- Never return raw file contents — always summarize to anchors and shape notes
- Sample findings.jsonl for record shape only — never enumerate full logs
- Stay within the 40-line output budget
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.
- 5d ago First seen · 49 lines · 71 tokens per session scan A aa4ddcf66df0
findings-surface-mapper is an agent published in the GitHub repository seanrreid/RAD_framework (5 stars, last pushed 9d ago), licensed MIT. It adds 71 tokens to every session and 470 once invoked, about $0.0004 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 agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.