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Get Started Free →Evaluate source credibility using primary/secondary classification, internal/external criticism, triangulation, and misinformation detection. Use this skill when the user needs to assess whether information is trustworthy, evaluate research sources, fact-check claims, or detect misinformation — even if they say 'can I trust this source', 'is this real', 'how reliable is this data', or 'fact-check this for me'.
.claude/skills/asgard-ai-platform-hum-source-criticism/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 50% | 0% |
Source criticism is a systematic method for evaluating whether information is trustworthy. Originally from historical methodology, it's now essential for navigating an information environment flooded with misinformation, opinion-as-fact, and AI-generated content.
IRON LAW: No Source Is Automatically Trustworthy
Every source — including academic journals, government data, and news from
reputable outlets — has potential biases, errors, and limitations. Credibility
is assessed, not assumed. "It's from the New York Times / 中央社" is not
sufficient — WHAT are they reporting, based on WHAT evidence, and do other
sources corroborate it?Primary sources: Direct evidence from the time/event (original documents, raw data, eyewitness accounts, original research, official records)
Secondary sources: Analysis or interpretation of primary sources (textbooks, review articles, news analysis, biographies)
Tertiary sources: Compilations of primary and secondary (encyclopedias, Wikipedia, databases) — starting points, not endpoints
1. External Criticism — Is the source authentic?
2. Internal Criticism — Is the content reliable?
3. Triangulation — Do multiple independent sources agree?
4. Currency — Is the information current enough?
| Red Flag | Description | |----------|-----------| | No author or organization identified | Who stands behind this claim? | | Emotional language without evidence | Designed to provoke, not inform | | No primary sources cited | Claims without traceable evidence | | "Studies show" without naming the study | Vague appeals to authority | | Single source amplified across many sites | Same claim copied, not independently verified | | Too good to be true / too outrageous | Extreme claims require extreme evidence | | URL/domain mimics reputable source | Fakecnn.com, bbc-news.co (not bbc.co.uk) |
markdown# Source Evaluation: {Source/Claim} ## Source Identity - Author/Organization: {who} - Publication: {where} - Date: {when} - Type: Primary / Secondary / Tertiary ## Credibility Assessment | Test | Assessment | Evidence | |------|-----------|---------| | External (authentic?) | ✓/⚠/✗ | {reasoning} | | Internal (reliable?) | ✓/⚠/✗ | {reasoning} | | Triangulation (corroborated?) | ✓/⚠/✗ | {other sources checked} | | Currency (current?) | ✓/⚠/✗ | {relevance of date} | ## Red Flags - {any detected red flags} ## Verdict - Credibility: High / Moderate / Low - Recommended action: {trust / verify further / discard}
Scenario: Evaluating a viral social media post claiming "Taiwan's GDP will surpass South Korea's by 2027"
| Test | Assessment | Evidence | |------|-----------|---------| | External | ⚠ | Anonymous account, no institutional affiliation, chart has no data source | | Internal | ✗ | Uses nominal GDP (not PPP), cherry-picks semiconductor sector projection, ignores exchange rate volatility | | Triangulation | ✗ | IMF and World Bank projections show no such convergence; no reputable analyst makes this claim | | Currency | ✓ | Posted this month |
Red flags: Emotional headline ("Taiwan DESTROYS Korea"), no primary data source cited, single unsourced chart Verdict: Low credibility — discard ✓
references/craap-test.mdreferences/fact-check-tools.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 18,966 | 40,214 | +112% | 1 | 1 | 0% | 2,872 | 3,293 | +15% | 0 | 0 | — |
case-02 | fail→pass | 12,587 | 10,571 | -16% | 1 | 1 | 0% | 2,165 | 2,943 | +36% | 0 | 0 | — |
case-03 | fail→fail | 13,645 | 8,821 | -35% | 1 | 1 | 0% | 2,185 | 2,715 | +24% | 0 | 0 | — |
case-04 | fail→pass | 12,204 | 11,849 | -3% | 1 | 1 | 0% | 1,638 | 2,865 | +75% | 0 | 0 | — |
case-05 | pass→pass | 14,382 | 40,497 | +182% | 1 | 1 | 0% | 2,218 | 2,910 | +31% | 0 | 0 | — |
case-06 | pass→pass | 10,767 | 11,784 | +9% | 1 | 1 | 0% | 1,849 | 3,112 | +68% | 0 | 0 | — |
case-07 | fail→pass | 6,149 | 3,414 | -44% | 1 | 1 | 0% | 966 | 1,889 | +96% | 0 | 0 | — |
case-08 | pass→pass | 10,132 | 4,183 | -59% | 1 | 1 | 0% | 1,677 | 1,965 | +17% | 0 | 0 | — |
case-09 | pass→pass | 8,562 | 6,799 | -21% | 1 | 1 | 0% | 1,254 | 2,347 | +87% | 0 | 0 | — |
case-10 | pass→pass | 11,868 | 9,912 | -16% | 1 | 1 | 0% | 1,935 | 2,719 | +41% | 0 | 0 | — |
case-11 | pass→pass | 8,920 | 5,011 | -44% | 1 | 1 | 0% | 1,280 | 2,081 | +63% | 0 | 0 | — |
case-12 | pass→pass | 10,132 | 7,678 | -24% | 1 | 1 | 0% | 1,518 | 2,463 | +62% | 0 | 0 | — |
case-13 | pass→pass | 10,844 | 7,714 | -29% | 1 | 1 | 0% | 1,422 | 2,443 | +72% | 0 | 0 | — |
case-14 | pass→pass | 13,921 | 9,591 | -31% | 1 | 1 | 0% | 1,951 | 2,754 | +41% | 0 | 0 | — |
case-15 | pass→pass | 14,564 | 14,661 | +1% | 1 | 1 | 0% | 2,209 | 3,185 | +44% | 0 | 0 | — |
case-16 | pass→pass | 13,539 | 13,216 | -2% | 1 | 1 | 0% | 1,914 | 3,042 | +59% | 0 | 0 | — |
case-17 | fail→fail | 23,951 | 8,786 | -63% | 1 | 1 | 0% | 3,385 | 2,473 | -27% | 0 | 0 | — |
case-18 | fail→pass | 10,609 | 8,970 | -15% | 1 | 1 | 0% | 1,644 | 2,535 | +54% | 0 | 0 | — |
case-19 | fail→pass | 16,746 | 12,382 | -26% | 1 | 1 | 0% | 2,257 | 3,396 | +50% | 0 | 0 | — |
case-20 | pass→pass | 11,918 | 14,573 | +22% | 1 | 1 | 0% | 2,606 | 4,343 | +67% | 0 | 0 | — |
case-21 | pass→pass | 6,938 | 7,463 | +8% | 1 | 1 | 0% | 1,251 | 2,368 | +89% | 0 | 0 | — |
case-22 | pass→pass | 14,397 | 18,114 | +26% | 1 | 1 | 0% | 3,119 | 5,037 | +61% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.