evaluation-anchor-checker✓ تایید شده
Audit and rewrite evaluation/numeric claims to ensure they carry minimal protocol context (task + metric + constraint) and avoid underspecified model naming. **Trigger**: evaluation anchor checker, numeric claim hygiene, underspecified numbers, protocol context, 评测锚点检查, 数字断言, 指标上下文. **Use when**: before final merge/polish, or when reviewers would likely flag claims as underspecified (numbers without task/metric/budget), or `pipeline-auditor` warns about suspicious model naming. **Skip if**: evidence is too thin to justify numeric claims (route upstream to C3/C4), or you are pre-C2 (NO PROSE). **Network**: none. **Guardrail**: do not invent numbers; do not add/remove/move citation keys; if protocol context is missing, weaken/remove the numeric claim rather than guessing.
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نصب سراسری (سطح کاربر):
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checkerنصب در پروژه فعلی:
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checker --projectskill.install.customTargetHelp
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checker --target-dir /path/to/skillsمسیر پیشنهادی: ~/.claude/skills/evaluation-anchor-checker/
محتوای SKILL.md
---
name: evaluation-anchor-checker
description: |
Audit and rewrite evaluation/numeric claims to ensure they carry minimal protocol context (task + metric + constraint) and avoid underspecified model naming.
**Trigger**: evaluation anchor checker, numeric claim hygiene, underspecified numbers, protocol context, 评测锚点检查, 数字断言, 指标上下文.
**Use when**: before final merge/polish, or when reviewers would likely flag claims as underspecified (numbers without task/metric/budget), or `pipeline-auditor` warns about suspicious model naming.
**Skip if**: evidence is too thin to justify numeric claims (route upstream to C3/C4), or you are pre-C2 (NO PROSE).
**Network**: none.
**Guardrail**: do not invent numbers; do not add/remove/move citation keys; if protocol context is missing, weaken/remove the numeric claim rather than guessing.
---
# Evaluation Anchor Checker (make numbers reviewer-safe)
Purpose: fix a reviewer-magnet failure mode in agent surveys:
- strong numeric/performance statements appear
- but the minimal evaluation context is missing
This skill treats numeric claims as *contracts*:
- if a number stays, the same sentence must contain enough protocol context to interpret it
- if that context is not in evidence, the claim must be downgraded (no guessing)
## Inputs
Preferred (pre-merge, keeps anchoring intact):
- the affected `sections/*.md` files
Optional context (read-only; helps you avoid guessing):
- `outline/writer_context_packs.jsonl` (look for `evaluation_anchor_minimal`, `evaluation_protocol`, `anchor_facts`)
- `outline/evidence_drafts.jsonl` / `outline/anchor_sheet.jsonl`
- `citations/ref.bib`
## Outputs
- Updated `sections/*.md` (or `output/DRAFT.md` if you are post-merge), with safer evaluation anchoring
- `output/EVAL_ANCHOR_REPORT.md` (always; short report with files checked / changed / weakened sentences)
- Optional completion marker: `output/eval_anchors_checked.refined.ok`
## Recommended slot in the survey pipeline
Use this as the **last section-level numeric hygiene sweep before merge**:
- after `paragraph-curator` + `style-harmonizer` + `opener-variator`
- before `transition-weaver` / `section-merger`
Reason:
- earlier section-level rewrite passes can legitimately rephrase or fuse numeric sentences
- if you only wait for `pipeline-auditor`, numeric-context issues are discovered too late in the merged draft
- section-scoped fixes are cheaper and preserve citation anchoring better than post-merge patching
## Read Order
Always read:
- `references/numeric_hygiene.md`
Machine-readable asset:
- `assets/numeric_hygiene.json`
The asset defines the keyword families and qualitative fallback templates.
Keep the script deterministic and let the policy live in the asset/reference pair.
## Role prompt: Reviewer-minded Editor (evaluation hygiene)
```text
You are a reviewer-minded editor for evaluation claims in a technical survey.
Goal:
- make every numeric/performance claim interpretable and reviewer-safe
Hard constraints:
- do not invent numbers
- do not add/remove/move citation keys
- if protocol context is missing, weaken or remove the numeric claim
Minimum context to include when keeping a number:
- task / setting (what kind of task)
- metric (what is being measured)
- constraint (budget/cost/tool access/horizon/seed/logging) when relevant
Avoid:
- ambiguous model naming that looks hallucinated (e.g., “GPT-5”) unless the cited paper uses it verbatim
```
## Workflow (explicit inputs)
- Use `outline/writer_context_packs.jsonl` to locate the subsection's allowed citations and any extracted `evaluation_protocol`/`anchor_facts`.
- Cross-check `outline/evidence_drafts.jsonl` and `outline/anchor_sheet.jsonl` for task/metric/constraint context before touching numbers.
- Validate every cited key against `citations/ref.bib` (do not introduce new keys).
- Write `output/EVAL_ANCHOR_REPORT.md` so the pipeline has an auditable completion artifact for this sweep.
## What to enforce (the “minimum protocol trio”)
When a sentence contains digits (`%`, `x`, or numbers):
- Keep the number only if you can attach at least 2 of the following *in the same sentence* without guessing:
- task family / benchmark name
- metric definition
- constraint (budget, tool access, cost model, retries, horizon)
If you cannot, downgrade:
- remove the number and rewrite as qualitative (“often”, “can”, “may”) with the same citation
- or move the specificity into a verification target (“evaluations need to report …”) without adding new facts
## Mini examples (paraphrase; do not copy)
Bad (underspecified):
- `Model X achieves 75% exact performance [@SomeBench].`
Better (minimal context):
- `On <task/benchmark>, Model X reaches ~75% <metric>, under <constraint/budget/tool access> [@SomeBench].`
Better (downgrade when context is missing):
- `Reported gains vary, but comparisons remain fragile when budgets and retry policies are not reported [@SomeBench].`
## Done checklist
- [ ] `output/EVAL_ANCHOR_REPORT.md` exists and reports a non-zero file count.
- [ ] No numeric claim remains without minimal protocol context.
- [ ] No ambiguous model naming remains unless explicitly supported by citations.
- [ ] Citation keys are unchanged.
- [ ] If you removed/downgraded numbers, the paragraph still makes a defensible, evidence-bounded point.
## Script
### Quick Start
- `python .codex/skills/evaluation-anchor-checker/scripts/run.py --workspace workspaces/<ws>`
### All Options
- `--workspace <dir>`: workspace containing `sections/*.md` or merged draft artifacts
- `--unit-id <id>`: optional harness metadata
- `--inputs <semicolon-separated>`: optional override from `UNITS.csv`
- `--outputs <semicolon-separated>`: optional output override; default includes `output/EVAL_ANCHOR_REPORT.md`
- `--checkpoint <C*>`: optional harness metadata
### Examples
- Run the numeric hygiene sweep before merge:
- `python .codex/skills/evaluation-anchor-checker/scripts/run.py --workspace workspaces/<ws> --inputs 'sections/*.md;outline/writer_context_packs.jsonl;citations/ref.bib' --outputs 'sections/*.md;output/EVAL_ANCHOR_REPORT.md;output/eval_anchors_checked.refined.ok'`
مجوز
در منبع این مهارت هیچ مجوزی اعلام نشده است.
متن کامل مجوز در مخزن منبع در دسترس است.
مشاهدهٔ مجوز در مخزن منبع — نسخهٔ منتشرشده در آنجا مرجع است.
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مهارتها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناختهشده را اسکن میکند اما نمیتواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.
نصب سراسری (سطح کاربر):
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checkerنصب در پروژه فعلی:
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checker --projectskill.install.customTargetHelp
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checker --target-dir /path/to/skillsمسیر پیشنهادی: ~/.claude/skills/evaluation-anchor-checker/