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Evidence Binder

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WILLOSCAR
evidence-binder

Bind addressable evidence IDs from `papers/evidence_bank.jsonl` to each subsection (H3), producing `outline/evidence_bindings.jsonl`.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameevidence-binder
Stars
513
Forks
39
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Evidence Binder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/evidence-binder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/evidence-binder .claude/skills/evidence-binder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evidence Binder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evidence Binder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evidence Binder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Evidence Binder (NO PROSE)

Triggers & routing

  • Trigger: evidence binder, evidence plan, section->evidence mapping, 证据绑定, evidence_id.
  • Use when: papers/evidence_bank.jsonl exists and you want writer/auditor to use section-scoped evidence items (WebWeaver-style memory bank).

Goal: convert a paper-level pool into a subsection-addressable evidence plan.

This skill is the bridge from “Evidence Bank” → “Writer”: the writer should only use evidence IDs bound to the current subsection.

Why this matters for writing quality:

  • Weak/undifferentiated bindings force the writer to either pad prose or cite out-of-scope.
  • Treat binding_gaps as a routing signal: fix upstream evidence/mapping instead of "writing around" missing evidence.

Inputs

  • outline/subsection_briefs.jsonl
  • outline/mapping.tsv
  • papers/evidence_bank.jsonl
  • Optional:
    • citations/ref.bib (to validate cite keys when evidence items carry citations)

Outputs

  • outline/evidence_bindings.jsonl (1 JSONL record per subsection)
  • outline/evidence_binding_report.md (summary; bullets + small tables)
    • Includes gaps (missing required evidence fields) and tag mix (selected evidence tags) so subsection-specific evidence needs are visible.

Output format (outline/evidence_bindings.jsonl)

JSONL (one object per H3 subsection). Best-effort fields (keep deterministic):

  • sub_id, title
  • paper_ids (papers in-scope for this subsection, from mapping.tsv)
  • mapped_bibkeys (bibkeys mapped to this subsection)
  • bibkeys (a selected subset to encourage subsection-first citations)
  • evidence_ids (selected evidence items from papers/evidence_bank.jsonl)
  • evidence_counts (small summary by claim_type / tag / evidence_level)
  • binding_rationale (short bullets; why the selected evidence covers this subsection’s axes / desired tags)
  • binding_gaps (list[str]; required evidence fields not covered by selected evidence; drives the evidence self-loop upstream)

A150++ density contract (default)

  • Use queries.md:per_subsection as the width contract (A150++ default: 28).
  • Bind enough evidence to make writing concretely executable without out-of-scope pressure:
    • mapped_bibkeys: >= per_subsection
    • evidence_ids: >= per_subsection - 4 (A150++: >=24)
    • bibkeys (selected): >= 20 (so each H3 has a usable citation pool, not just a long mapped list)

For draft_profile=course_paper, retain the same traceability shape at compact density: 8 mapped bibkeys, at least 6 selected bibkeys, and at least 6 evidence IDs from at least 4 papers per H3.

Binding policy (how strict to be)

  • Subsection-first by default: the writer should primarily cite bibkeys and use evidence_ids bound to this sub_id.
  • Allow limited within-chapter reuse: citations from sibling H3s within the same H2 chapter may be reused for background/evaluation framing, but:
    • keep >=3 subsection-specific citations per H3 (avoid “free cite drift”)
    • avoid cross-chapter reuse unless the outline explicitly calls for it

Workflow (NO PROSE)

  1. Read outline/subsection_briefs.jsonl to understand each H3’s scope/rq/axes.
  2. Read outline/mapping.tsv to know which papers belong to each subsection.
  3. Read papers/evidence_bank.jsonl and select a subsection-scoped set of evidence_id items per H3.
  4. If citations/ref.bib exists, sanity-check that any cite keys referenced by selected evidence items are defined.
  5. Write outline/evidence_bindings.jsonl and outline/evidence_binding_report.md.

Freeze policy

  • If outline/evidence_bindings.refined.ok exists, the script will not overwrite outline/evidence_bindings.jsonl.
  • Treat this marker as an explicit refinement/completion signal (especially in strict runs): only create it after you have checked binding_gaps and tag mix look subsection-specific.

Heterogeneity sanity check (avoid recipe-like bindings)

A common hidden failure mode is mechanical uniformity: every H3 ends up with the same claim_type/tag mix, which hides what each subsection is actually missing and pushes the writer toward generic prose.

Before you mark bindings as refined:

  • Scan outline/evidence_binding_report.md: different H3 should show meaningfully different tag mix / claim_type balance.
  • If most H3 look identical, treat it as a binder smell: tighten required_evidence_fields, adjust selection rationale, or enrich the evidence bank, then rerun.

Script

Quick Start

  • uv run python .codex/skills/evidence-binder/scripts/run.py --help
  • uv run python .codex/skills/evidence-binder/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>: workspace root
  • --unit-id <U###>: unit id (optional; for logs)
  • --inputs <semicolon-separated>: override inputs (rare; prefer defaults)
  • --outputs <semicolon-separated>: override outputs (rare; prefer defaults)
  • --checkpoint <C#>: checkpoint id (optional; for logs)

Examples

  • Bind evidence IDs after building the evidence bank:
    • Ensure papers/evidence_bank.jsonl exists.
    • Run: uv run python .codex/skills/evidence-binder/scripts/run.py --workspace <workspace>

Troubleshooting

Issue: some subsections have too few evidence IDs

Fix:

  • Strengthen papers/evidence_bank.jsonl via paper-notes (more extractable evidence items).
  • Or broaden the mapped paper set for the subsection via section-mapper, then rerun binder.

Issue: binding_gaps is non-empty (missing evidence types)

What it means:

  • The subsection brief requires certain evidence fields (e.g., benchmarks/metrics/security/tooling), but the bound evidence items do not cover them.

Fix (self-loop upstream):

  • Prefer enriching papers/evidence_bank.jsonl / papers/paper_notes.jsonl for mapped papers (extract benchmark/metric/failure-mode details).
  • If the mapping is weak for that evidence type, expand outline/mapping.tsv for the subsection and rerun binder.
  • If the requirement is unrealistic for the subsection’s scope, revise outline/subsection_briefs.jsonl:required_evidence_fields and rerun binder.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evidence Binder AI skill do?

Bind addressable evidence IDs from `papers/evidence_bank.jsonl` to each subsection (H3), producing `outline/evidence_bindings.jsonl`.

Why use Evidence Binder on TypingMind?

Because you install it once and use it with any model. Evidence Binder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evidence Binder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/evidence-binder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evidence Binder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evidence Binder?

As many as you like. As long as a model supports skills, you can use Evidence Binder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evidence Binder AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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