evaluation-anchor-checker✓ Pass
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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SKILL.md Content
---
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'`
License
No license was declared in this skill's source.
The full license text is available in the source repository.
View the license in the source repository — the version published there is authoritative.
// Install Skill
Install Skill
Skills are third-party code from public GitHub repositories. SkillHub scans for known malicious patterns but cannot guarantee safety. Review the source code before installing.
Install globally (user-level):
npx skillhub install WILLOSCAR/research-units-pipeline-skills/evaluation-anchor-checkerInstall in current project:
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/skillsSuggested path: ~/.claude/skills/evaluation-anchor-checker/