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Get Started Free →Apply a `citation-diversifier` budget report by injecting *in-scope* citations into an existing draft (NO NEW FACTS), so the run passes the global unique-citation gate without citation dumps. **Trigger**: citation injector, apply citation budget, inject citations, add citations safely, 引用注入, 按预算加引用, 引用增密.
.claude/skills/willoscar-citation-injector/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 111% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 161% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 193% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -7% | 0% |
Purpose: make the pipeline converge when the draft is:
This skill is intentionally conservative and scriptable:
output/DRAFT.md directly using the budget report as constraintsoutput/DRAFT.mdoutput/CITATION_BUDGET_REPORT.md (from citation-diversifier)outline/outline.yml (H3 id/title mapping)citations/ref.bib (must contain every injected key)output/DRAFT.md (updated in place)output/CITATION_INJECTION_REPORT.md (PASS/FAIL + what you changed)This subsection discusses or Next, we examine.[@a; @b; @c] as the only citations in a paragraph.Use these as sentence intentions (paraphrase; do not copy verbatim).
1) Axis-anchored exemplars (preferred)
Systems such as X [@a] and Y [@b] instantiate <axis/design point>, whereas Z [@c] explores a contrasting point under a different protocol.2) Parenthetical grounding (short, low-risk)
The same design pressure appears in nearby systems (e.g., X [@a], Y [@b], Z [@c]).3) Cluster pointer + contrast hint
Representative implementations span both <cluster A> (X [@a], Y [@b]) and <cluster B> (Z [@c]), suggesting that the trade-off hinges on <lens>.4) Decision-lens pointer
For builders choosing between <A> and <B>, prior systems provide concrete instantiations on both sides (X [@a]; Y [@b]; Z [@c]).5) Evaluation-lens pointer (still evidence-neutral)
Across commonly used agent evaluations, systems such as X [@a] and Y [@b] illustrate how <lens> is operationalized, while Z [@c] highlights a different constraint.6) Contrast without list voice
While many works operationalize <topic> via <mechanism> (X [@a]; Y [@b]), others treat it as <alternative> (Z [@c]), which changes the failure modes discussed later.Avoid these stems (they read like automated injection):
A few representative references includeNotable lines of work includeConcrete examples includeIf your draft contains these, rewrite them immediately using the patterns above (keep citation keys unchanged).
contrast_hook) so the same sentence cannot be copy-pasted into every H3.1) Read the budget report (output/CITATION_BUDGET_REPORT.md)
Global target (policy; blocking) as the PASS line for the pipeline gate (derived from queries.md:citation_target; A150++ default: recommended).Gap: 0, do nothing: write a short PASS report and move on.Prefer keys that are unused globally and avoid repeating the same new keys across many H3s.
2) Inject in the right subsection
outline/outline.yml to confirm H3 ordering and ensure the injected sentence lands inside the correct ### subsection.3) Inject with paper voice
citations/ref.bib.4) Write output/CITATION_INJECTION_REPORT.md
- Status: PASS only when the global target is met.5) Verify
draft-polisher to smooth any residual injection voice (citation keys must remain unchanged).output/CITATION_INJECTION_REPORT.md exists and is - Status: PASS.pipeline-auditor no longer FAILs on “unique citations too low”.You usually do not run this manually; it exists so a pipeline runner can deterministically apply a baseline injection and validate the target.
uv run python .codex/skills/citation-injector/scripts/run.py --workspace <workspace>--workspace <dir>--unit-id <U###> (optional; for logs)--inputs <semicolon-separated> (rare override; prefer defaults)--outputs <semicolon-separated> (rare override; default validates output/CITATION_INJECTION_REPORT.md)--checkpoint <C#> (optional)uv run python .codex/skills/citation-injector/scripts/run.py --workspace <workspace>| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,105 | 5,675 | +11% | 1 | 1 | 0% | 786 | 1,645 | +109% | 0 | 0 | — |
case-02 | fail→fail | 3,543 | 6,127 | +73% | 1 | 1 | 0% | 251 | 1,654 | +559% | 0 | 0 | — |
case-03 | fail→fail | 4,347 | 5,791 | +33% | 1 | 1 | 0% | 656 | 1,659 | +153% | 0 | 0 | — |
case-04 | pass→pass | 6,352 | 6,833 | +8% | 1 | 1 | 0% | 1,077 | 2,541 | +136% | 0 | 0 | — |
case-05 | pass→pass | 8,970 | 7,107 | -21% | 1 | 1 | 0% | 1,494 | 2,561 | +71% | 0 | 0 | — |
case-06 | fail→fail | 3,595 | 5,579 | +55% | 1 | 1 | 0% | 496 | 2,195 | +343% | 0 | 0 | — |
case-07 | fail→pass | 7,442 | 5,465 | -27% | 1 | 1 | 0% | 1,083 | 2,281 | +111% | 0 | 0 | — |
case-08 | fail→pass | 6,436 | 6,233 | -3% | 1 | 1 | 0% | 895 | 2,333 | +161% | 0 | 0 | — |
case-09 | fail→fail | 11,518 | 4,489 | -61% | 1 | 1 | 0% | 1,633 | 1,985 | +22% | 0 | 0 | — |
case-10 | fail→pass | 11,796 | 19,085 | +62% | 1 | 1 | 0% | 1,717 | 1,936 | +13% | 0 | 0 | — |
case-11 | fail→fail | 8,062 | 5,573 | -31% | 1 | 1 | 0% | 1,276 | 2,294 | +80% | 0 | 0 | — |
case-12 | pass→pass | 14,326 | 9,822 | -31% | 1 | 1 | 0% | 2,170 | 2,908 | +34% | 0 | 0 | — |
case-13 | fail→pass | 5,182 | 4,855 | -6% | 1 | 1 | 0% | 696 | 2,040 | +193% | 0 | 0 | — |
case-14 | fail→pass | 12,129 | 3,217 | -73% | 1 | 1 | 0% | 1,938 | 1,808 | -7% | 0 | 0 | — |
case-15 | fail→pass | 13,320 | 2,967 | -78% | 1 | 1 | 0% | 1,463 | 1,873 | +28% | 0 | 0 | — |
case-16 | fail→pass | 9,576 | 6,335 | -34% | 1 | 1 | 0% | 1,374 | 2,269 | +65% | 0 | 0 | — |
case-17 | fail→pass | 10,818 | 5,036 | -53% | 1 | 1 | 0% | 1,599 | 2,192 | +37% | 0 | 0 | — |
case-18 | fail→pass | 11,954 | 4,624 | -61% | 1 | 1 | 0% | 1,714 | 2,092 | +22% | 0 | 0 | — |
case-19 | fail→fail | 6,185 | 4,059 | -34% | 1 | 1 | 0% | 1,082 | 1,604 | +48% | 0 | 0 | — |
case-20 | fail→fail | 16,644 | 5,714 | -66% | 1 | 1 | 0% | 2,938 | 1,595 | -46% | 0 | 0 | — |
case-21 | fail→fail | 7,551 | 7,512 | -1% | 1 | 1 | 0% | 365 | 1,896 | +419% | 0 | 0 | — |
case-22 | pass→pass | 7,476 | 6,914 | -8% | 1 | 1 | 0% | 1,050 | 2,610 | +149% | 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, and 16 counted toward the lift figure. The other 6 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +41 percentage points is the difference between those two pass rates over the 16 comparable cases.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
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.