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 oborchers/fractional-cto --skill source-evaluationgit clone --depth 1 https://github.com/oborchers/fractional-ctoWrote 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/oborchers/fractional-cto/source-evaluation)<a href="https://agentmods.dev/skills/oborchers/fractional-cto/source-evaluation"><img src="https://agentmods.dev/badge/skills/oborchers/fractional-cto/source-evaluation.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 5.1 | $0.00126 | $0.02394 |
| Opus 5 | $0.00063 | $0.01197 |
| Sonnet 5 | $0.00025 | $0.00479 |
| Haiku 4.5 | $0.00013 | $0.00239 |
Grade A, and why
source-evaluation scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| **npm** | `curl api.npmjs.org/downloads/point/last-week/{pkg}` | Exact weekly downloads | How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Source Evaluation
Source quality is the primary bottleneck in research agent pipelines. Research on deep research agent trajectories found that over 57% of source errors occur in early retrieval stages, where initial fabrication acts as the primary catalyst for cascading downstream errors (arXiv 2601.22984). A single bad source in the first retrieval round contaminates the entire research trajectory.
Source Credibility Tiers
Every source encountered during research falls into one of six tiers. Always prefer higher-tier sources and cite the tier when reporting findings.
| Tier | Source Type | Examples | Trust Level |
|---|---|---|---|
| T1 — Primary | Peer-reviewed journals, official specs, primary datasets | Nature, Science, IEEE, IETF RFCs, W3C specs | Highest |
| T2 — Institutional | Government agencies, established research institutions | NIH, WHO, NIST, ACM Digital Library | High |
| T3 — Expert | Named expert blogs, conference proceedings, major tech engineering blogs | Anthropic blog, Google Research, NeurIPS/ICML papers | Moderate-High |
| T4 — Quality Editorial | Major publications with editorial review | MIT Technology Review, Ars Technica, The Verge | Moderate |
| T5 — Community | Well-moderated forums, high-reputation answers | Stack Overflow (high-score), GitHub discussions | Low-Moderate |
| T6 — Unverified | Content farms, SEO-optimized articles, anonymous posts, AI-generated content | Medium listicles, affiliate blogs, uncredited tutorials | Do not cite |
Rule: Never cite T6 sources. Prefer T1-T3 for factual claims. Use T4-T5 for context and community consensus only.
The CRAAP Framework — Automated Signals
Adapted from the CRAAP framework (CSU Chico), five dimensions for evaluating sources:
| Dimension | What to Check | Red Flags |
|---|---|---|
| Currency | Publication date, last-modified headers | No date visible, information predates major changes in the field |
| Relevance | Does it address the specific research question? | Tangential coverage, keyword-stuffed but shallow |
| Authority | Who published it? Credentials? | Anonymous author, no institutional affiliation, no citations |
| Accuracy | Are claims sourced? Can they be verified? | No inline citations, contradicts known facts, round numbers without source |
| Purpose | Is it informing, selling, or persuading? | High ad density, affiliate links, promotional language |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 158 lines · 126 tokens per session scan A 69a09a01c4ae
source-evaluation is a skill published in the GitHub repository oborchers/fractional-cto (29 stars, last pushed 1mo ago), licensed MIT. It adds 126 tokens to every session and 2,394 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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