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Press Conference Revision Evidence Bound

Community
lensback940701
press-conference-revision-evidence-bound

Diagnose and revise defensive academic writing while preserving claim ceilings, evidence status, scope conditions, rival explanations, and conceptual hierarchy. Use for a whole-manuscript batch audit or a tightly authorized, tracked-change revision when an academic paper hides its contribution beneath self-defence, repeated caveats, process narration, or unnecessary comparisons; especially when a qualitative, historical, or mixed-method manuscript must become contribution-forward without becoming promotional or overclaimed. Do not use for first drafting, new research, citation repair, evidence admission, grammar-only polishing, or making claims sound stronger than the evidence permits.

Overview

Publisherlensback940701
RepositoryEvidence-Bound-Press-Conference-Revision-Skill
Skill namepress-conference-revision-evidence-bound
Stars
274
Forks
6
Bundled files
9
LicenseMIT
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.

  • 9 bundled files

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

  • Open source

    Published by lensback940701 on GitHub. Read the source before you install it.

Installation

Install the Press Conference Revision Evidence Bound 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/lensback940701/Evidence-Bound-Press-Conference-Revision-Skill/tree/main
  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/lensback940701/Evidence-Bound-Press-Conference-Revision-Skill.git \
  .claude/skills/press-conference-revision-evidence-bound
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Press Conference Revision Evidence Bound 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 Press Conference Revision Evidence Bound 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 Press Conference Revision Evidence Bound 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-Bound Press-Conference Revision

Treat a paper as a clear academic presentation of its best supported contribution, not as a work log or a self-audit. Reframe defensiveness; never remove the information that lets readers judge what the evidence supports.

Non-negotiable distinction

Do not treat a cue word as a verdict. The target is rhetorical defensiveness, not scholarly caution.

  • Cut, compress, or reframe self-deprecation, anticipatory answers to objections the manuscript has not invited, redundant hedge stacks, process-log narration, and negative judgments broader than the evidence.
  • Keep or relocate a real scope condition, source-status distinction, rival explanation, contradiction, negative finding, ethical caveat, or method limitation whenever it changes how the claim must be read.
  • When uncertain, retain the wording and register a query. Do not convert possible to certain, report to observation, authorization to delivery, sequence to mechanism, or a bounded case result to a general law.

Read references/triage-and-regression.md before classifying candidates. For the Dalian Urban Geography manuscript, also read references/urban-geography-guardrails.md before any diagnosis or revision.

Required inputs and authority order

Collect the smallest current set of materials needed for the task:

  1. the latest clean manuscript and, for a revision pass, its tracked baseline;
  2. the current manuscript lock and any later author-authorized departure from it;
  3. current author decisions and unresolved queries;
  4. target-journal guidance and any supplied corpus/style calibration;
  5. the source principles and prior audit/return records, as context rather than evidence.

Apply this order: manuscript evidence and active author decisions → lock and claim ceiling → verified journal guidance → corpus-level rhetorical calibration → this skill. Do not use a corpus as a phrase bank or let a style preference override a source status.

Phase 0 — create a contribution-and-scope contract

Before scanning, write a compact contract. Do not edit until it is complete.

fieldrequired content
core contributionOne evidence-bound sentence naming the primary analytical move and payoff.
contribution hierarchyPrimary concept; supporting lens; bounded empirical contribution; portable payoff, if any.
claim ceilingThe strongest permissible formulation already supported by the manuscript and its evidence.
load-bearing cautionsScope, source-status, method, rival, contradiction, and ethics statements that must survive.
edit authoritySections/paragraphs authorized, locked items, tracking requirements, and prohibited changes.
live queriesMeaning-dependent choices that must not be silently resolved.

If the hierarchy, evidence ceiling, or editing authority is unclear, stop after the diagnostic register and request a decision.

Phase 1 — batch diagnostic (read-only)

Read the whole manuscript once for section roles and the contribution contract. Then make a candidate register; do not revise prose in this phase.

  1. Optionally run scripts/scan_defensive_cues.py on the clean DOCX to create a candidate list only. Inspect every result in context; the script makes no rhetorical or evidentiary decision.
  2. Review every prose-bearing paragraph, including title, abstract, introductions, discussion, conclusion, captions, notes, and references only where they affect visible prose.
  3. Classify each candidate using the taxonomy in references/triage-and-regression.md.
  4. Record the candidate in a copy of assets/defensive-writing-audit-template.csv or an equivalent table. Include its evidentiary function, source-status tag, contribution relation, and provisional disposition.
  5. Mark non-defensive but load-bearing language as KEEP; this makes the integrity decision auditable rather than invisible.

Allowed dispositions are KEEP, TIGHTEN, REFRAME, RELOCATE, CUT, and QUERY. A CUT must remove rhetoric only, never a factual proposition, citation function, evidence status, rival, or scope limit.

Phase 2 — concentrated revision (only with authority)

Revise by pattern group and section role, not by a blind word hunt. Use tracked changes when the manuscript is under review.

  1. Start with the abstract, introduction, discussion, and conclusion: make the problem, gap, distinctive analytical move, evidence-bound finding, and payoff legible before qualifications appear.
  2. In theory and methods, replace self-defence with precise positioning, but keep definitions, scope, evidence-status distinctions, and analytical rivals.
  3. In empirical sections, retain contradictions, delay, non-adoption, reversal, and negative findings when they are the data or explain the mechanism. Remove only authorial apology or unnecessary rehearsal of a limitation.
  4. Keep a contribution sentence standing in each major section. A later qualifier may narrow it; it may not erase it or turn the paragraph into a response to a hypothetical reviewer.
  5. For every changed sentence, run the three-part preservation test: (a) same or narrower claim, (b) same source-status distinction, and (c) same citation/quotation role.
  6. Route a change to QUERY if it would alter research questions, terminology, hierarchy, evidence, citation role, table/figure text, paragraph order, or a real methodological limitation.

Never solve a mismatch by adding evidence, strengthening a causal verb, or extending a case-specific statement into a general claim.

Phase 3 — whole-manuscript regression

Read the revised manuscript independently of the change list. Use the regression protocol in references/triage-and-regression.md.

Verify all of the following:

  • contribution hierarchy is stable from title and abstract through conclusion;
  • a new general-first opening is aligned with the body, or the mismatch is explicitly left as a query;
  • every changed claim stays within the original evidence ceiling;
  • plan, authorization, reported work, observation, outcome, interpretation, and causal inference remain distinct;
  • terminology is consistent without flattening meaningful distinctions;
  • citations, quotations, dates, numbers, captions, footnotes, figure/table cross-references, and paragraph boundaries remain protected;
  • no automatic global replacement has elevated a secondary lens or introduced a prohibited keyword;
  • the clean file has accepted changes only and the tracked file exposes every text change.

Use a global terminology scan only to locate candidates. Never replace a concept merely because it is frequent.

Dalian Urban Geography special gate

When the target is the Dalian heritage-metabolism manuscript, apply the project guardrails before Phase 1 and again before delivery. In particular:

  • keep heritage metabolism primary and operation-centred;
  • keep rescaling supporting, rival-open, and conditional on a documented capacity-to-operation relation;
  • do not promote governance/governing into a master concept or weaken source-specific terms by global substitution;
  • preserve overlapping configurations, contradiction, lag, decoupling, reversal, and recombination as analytic findings rather than defensive residue;
  • treat the recent general-first abstract/introduction reframe as a cross-section consistency question, not permission to rewrite the body without explicit authority.

Deliverables and stop rules

For a diagnostic pass, return:

  • the contribution-and-scope contract;
  • the candidate register with KEEP decisions visible;
  • a section-level concentration map and an exception/query register;
  • a bounded revision recommendation, including which changes require author authority.

For an authorized revision pass, additionally return:

  • tracked and clean manuscript copies;
  • a change summary organized by pattern group;
  • a claim/evidence regression report;
  • a list of deliberate non-edits and unresolved author decisions.

Stop after the requested diagnostic or authorized revision pass. Do not submit, conduct new research, change citations, silently resolve a meaning-dependent query, or turn the paper into promotional prose.

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 Press Conference Revision Evidence Bound AI skill do?

Diagnose and revise defensive academic writing while preserving claim ceilings, evidence status, scope conditions, rival explanations, and conceptual hierarchy. Use for a whole-manuscript batch audit or a tightly authorized, tracked-change revision when an academic paper hides its contribution beneath self-defence, repeated caveats, process narration, or unnecessary comparisons; especially when a qualitative, historical, or mixed-method manuscript must become contribution-forward without becoming promotional or overclaimed. Do not use for first drafting, new research, citation repair, evide...

Why use Press Conference Revision Evidence Bound on TypingMind?

Because you install it once and use it with any model. Press Conference Revision Evidence Bound 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 Press Conference Revision Evidence Bound in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lensback940701/Evidence-Bound-Press-Conference-Revision-Skill/tree/main. 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 Press Conference Revision Evidence Bound?

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 Press Conference Revision Evidence Bound?

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

Is the Press Conference Revision Evidence Bound AI skill free?

Yes. It is published on GitHub by lensback940701 under the MIT license. 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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