citation-faithfulness
Skill1 starsChanged 4 months agoMITView on GitHub
Grade every [S#] claim in an answer against the exact evidence snippet it cites, and flag any claim the source does not actually support. Use whenever an agent produces medical or scientific claims tagged with numbered sources.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- AMinerOrg/aminer-research-skillstheir own version, first seen Nov 8, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- AMinerOrg/aminer-research-skills★ 67same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository8 other files it gives its agents.
- claim-extractionSkill
- conflict-detectionSkill
- evidence-coverageSkill
- meta-analysisSkill
- prisma-searchSkill
- query-planningSkill
- rag-injection-defenseSkill
- source-reliabilitySkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 28
Never upgrade certainty: If the snippet says "may reduce," the claim cannot say "reduces." If the snippet is a single small trial, the …
- Distinctive: line 34
Distinguish the source from your prior: If you believe the claim but the cited snippet does not say it, the grade is *not supported* — go …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Citation Faithfulness
A citation is a promise: "the source I tagged says this." A model that breaks that promise is worse than one with no citations at all, because the tag manufactures trust. This skill makes the agent keep the promise by grading each claim against its own evidence before the answer is shown.
When to use
Any time the output contains claims tagged to numbered sources ([S1], [S3], [12], etc.) and a reader might act on them. Especially: clinical claims, drug interactions, effect sizes, safety statements.
The rule
No [S#] claim ships until it has been graded against the snippet it cites.
For each sentence that carries a source tag:
- Pull the exact evidence the tag points to — the retrieved snippet, the abstract sentence, the table cell. Not the paper's title. Not your memory of the paper. The specific text.
- Grade the claim → evidence relationship into one of:
- Supported — the snippet states the claim (or entails it directly).
- Partially supported — the snippet supports a weaker or narrower version. Rewrite the claim to match what the snippet actually says.
- Not supported — the snippet does not contain the claim. Remove the tag, and remove or rewrite the claim.
- Contradicted — the snippet says the opposite. This is a hard stop: fix the claim, and surface the contradiction to the reader.
- Never upgrade certainty. If the snippet says "may reduce," the claim cannot say "reduces." If the snippet is a single small trial, the claim cannot speak for the field.
How to check well
- Match the specific quantity, direction, and population. "Lowers LDL by 40%" needs a snippet with that number, that direction, in that population. A snippet about a different dose or a different group is not support.
- Watch for the fabricated middle step. Two real sources, joined by a causal claim neither one makes, is the most common failure. Grade the joining claim on its own.
- Distinguish the source from your prior. If you believe the claim but the cited snippet does not say it, the grade is not supported — go find a snippet that does, or drop the claim. Your prior is not a citation.
Output
For a graded answer, keep a short audit the reader can expand:
[S3] "Metformin reduces all-cause mortality in T2DM"
→ snippet: "...associated with lower all-cause mortality (HR 0.83, 95% CI 0.76-0.90)..."
grade: SUPPORTED (observational; claim softened to "is associated with")
[S7] "Adding the second agent doubles efficacy"
→ snippet: "...no statistically significant difference between arms (p=0.21)..."
grade: CONTRADICTED — claim removed, tag removedThe point is not to produce the audit. The point is that producing it forces the check, and the check is what makes the answer safe to read.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

