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 paruff/uFawkesAI --skill finding-qualitygit clone --depth 1 https://github.com/paruff/uFawkesAIWrote 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/paruff/ufawkesai/finding-quality)<a href="https://agentmods.dev/skills/paruff/ufawkesai/finding-quality"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/finding-quality/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/paruff/ufawkesai/finding-quality"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/finding-quality.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 | $0.00030 | $0.00466 |
| Opus 5 | $0.00015 | $0.00233 |
| Sonnet 5 | $0.00006 | $0.00093 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
agent-finding-quality 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 8d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Agent Finding Quality
Load trigger:
"load agent-finding-quality skill"> DORA: Cap 6 (Reliability) Token cost: Low
Purpose
Measure whether findings produced by agents are actually useful. A finding that is never acted on is noise. A finding that prevents a bug is gold.
Span Specification
agent.finding.produced
Emit for each finding the agent identifies.
Attributes:
| Attribute | Type | Description |
|---|---|---|
severity |
string | CRITICAL, HIGH, MEDIUM, LOW, INFO |
category |
string | Skill or check category that produced this finding |
actionable |
boolean | Whether this finding requires a specific action |
manual_review_needed |
boolean | Whether human judgment is required |
agent.name |
string | Agent that produced this finding |
session_id |
string | Unique invocation identifier |
Quality Metrics
| Metric | Formula | Target |
|---|---|---|
| Actionability rate | actionable findings / total findings | > 60% |
| Manual review burden | manual_review_needed findings / total findings | < 30% |
| Blocker density | CRITICAL+HIGH findings / total invocations | < 50% |
| False positive rate | findings rejected by human / total findings | < 20% (requires human feedback) |
Usage
- Low actionability → agent protocol is too loose. Tighten required fields and forbidden patterns.
- High manual review burden → agent lacks specific rules. Add more concrete checks.
- High blocker density → either quality is dropping or agents are too strict. Investigate.
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.
- 8d ago First seen · 51 lines · 30 tokens per session scan A a4a9d288dc29
agent-finding-quality is a skill published in the GitHub repository paruff/uFawkesAI (2 stars, last pushed 16d ago), licensed MIT. It adds 30 tokens to every session and 466 once invoked, about $0.0002 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 skills, from other repositories
planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps taskplan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and…
infrastructure-overview
Top-level skill for the research template infrastructure layer. Use in Cursor, Claude Code, or similar agents when editing or importing anything under infrastructure/, understanding the two-layer architecture, or wiring build/validation/rendering/publishing. Covers module discovery, import patterns, thin…
infrastructure-validation
Skill for the validation infrastructure module providing PDF validation, markdown validation, output integrity checks, link verification, documentation audits, issue categorization, and repository scanning. Use when validating research outputs, checking document quality, running audits, or verifying cross-references.
infrastructure-llm
Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage. Use when querying LLMs, generating manuscript reviews, validating LLM outputs, or…
research-workflow
Seven-stage research workflow (SCOPE→LITERATURE→REASON→DESIGN→COMPUTE→SYNTHESIZE→WRITE). Use for: structuring an AI agent's research process, generating literature review prompts, scoping methodology. Usage: from infrastructure.research import ResearchWorkflow; ResearchWorkflow.describe() Config: set stage overrides…
infrastructure-search-literature
Paperclip-style multi-source literature search across arXiv, Crossref, local JSON corpora, and (opt-in) the Paperclip API. Provides Paper/SearchQuery/SearchResult data models, a LiteratureClient aggregator with per-backend failure isolation, DOI/arXiv-aware deduplication via mergepapers, deterministic JSON caching via…