Anchor Intake logo

Anchor Intake

Community
lynxlangya
anchor-intake

Interrogate a written engineering implementation brief before work starts. Use when a user brings a ticket, PRD, design note, or substantive engineering task brief and wants to begin implementation planning; force every fixed rubric element to be accounted, cite present/weak claims against the brief, surface gaps, solution-as-goal traps, contradictions, and questions, then emit an intent-level plan. Do not use for bare one-line requests, non-engineering artifacts, code diff review, bug fixing, or execution itself.

Overview

Publisherlynxlangya
Repositorytechne
Skill nameanchor-intake
Stars
106
Forks
18
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Anchor Intake 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/lynxlangya/techne/tree/main/skills/anchor-intake
  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/lynxlangya/techne.git /tmp/techne
mkdir -p .claude/skills
cp -r /tmp/techne/skills/anchor-intake .claude/skills/anchor-intake
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anchor Intake 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 Anchor Intake 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 Anchor Intake 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.

anchor-intake

Force the skipped move before execution: interrogate the written brief against a fixed engineering implementation rubric, then plan only after gaps and weak assumptions are visible.

Trigger Check

Use this skill when the user provides a substantial written engineering implementation brief: a ticket, PRD, design note, or task brief with enough text to interrogate before work starts.

Do not use it for a bare one-line request, an already precise command, a marketing brief, research proposal, prose draft, code diff review, bug fix, or implementation execution. For unsupported or too-thin artifacts, self-eject in one sentence and write no .techne/plan/<slug> output. If a captured artifact later proves unsupported, use finalize --unscopable --reason ....

Boundary test: can you point to a user-authored written artifact and ask whether it names the engineering goal, users, measurable success threshold, scope, non-goals, inputs, data sources, dependencies, constraints, and acceptance method? If yes, use anchor-intake. If no, eject or ask for the brief first.

Forced Procedure

  1. Capture the brief. Save the user-authored artifact verbatim. Run: python3 skills/anchor-intake/scripts/intake_gate.py init --project <root> --plan <slug> --brief-file <path>.
  2. Read the brief in full. Do not fill the rubric from memory or from the user's surrounding chat if the value is not in the captured brief.
  3. Account every fixed rubric element. For each of the ten elements, write one disposition in .techne/plan/<slug>/intake.json: present with citation and valueItems, present-weak with citation and a dependent question, or gap with a question or visible modelDefault.
  4. Surface traps and conflicts. Add span-anchored solution-as-goal and contradiction findings whenever the brief implies them. Contradictions need two verified spans.
  5. Ask only dependent questions. Every question must resolve an element or finding and carry a planDelta. Weak/gap questions must reference the weak span/value or the missing element label.
  6. Author intent-level steps. Steps are not execution yet. Each step needs an id, dependsOnAssumptions, dependsOnSteps, verifiableOutcome, and at least one terminal step reachable from a root step.
  7. Check and repair. Run: python3 skills/anchor-intake/scripts/intake_gate.py check --project <root> --plan <slug>. Fix structural failures by doing the missing intake work, not by padding JSON.
  8. Finalize. Run: python3 skills/anchor-intake/scripts/intake_gate.py finalize --project <root> --plan <slug>. Relay the generated intakeReport.json to the user, especially gaps, weak elements, questions, solution-as-goal findings, contradictions, and warnings.

Script Contract

Artifacts are written under the target project:

text
.techne/plan/<slug>/
  brief.txt          # captured external artifact
  context.json       # computed by init
  intake.json        # authored by the reviewer/planner
  report.json        # computed by check
  plan.json          # computed by finalize
  intakeReport.json  # computed by finalize

Generated .techne/ output belongs to target projects. Do not commit it to this repository.

Minimal intake.json shape:

json
{
  "schema": "techne.intake/1",
  "elements": {
    "data-sources": {
      "disposition": "gap",
      "questionIds": ["Q1"]
    }
  },
  "findings": [
    {
      "id": "S1",
      "kind": "solution-as-goal",
      "citation": {"offset": 16, "quote": "Build a Slack bot"}
    }
  ],
  "questions": [
    {
      "id": "Q1",
      "text": "Which data sources should the implementation read?",
      "resolves": ["data-sources"],
      "planDelta": {"adds": ["Bind implementation to the named source"]}
    }
  ],
  "steps": [
    {
      "id": "s1",
      "title": "Implement only the scoped behavior",
      "dependsOnAssumptions": ["goal-and-why"],
      "dependsOnSteps": [],
      "verifiableOutcome": "The scoped behavior can be verified by the acceptance method",
      "terminal": true
    }
  ]
}

Use reference.md for the full JSON contract, fixed rubric, STOPLIST/RUBRIC-LABEL set, shape rules, warning semantics, and known weak spots.

Stop Conditions

  • Stop before planning if there is no substantial written engineering brief.
  • Stop before finalize if check reports blocking failures.
  • Stop and use --unscopable --reason if the artifact is not an engineering implementation brief or is too garbled/thin to interrogate honestly.
  • Stop before executing the work. anchor-intake ends at a surfaced report and intent-level plan; implementation belongs to the next workflow step.

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 Anchor Intake AI skill do?

Interrogate a written engineering implementation brief before work starts. Use when a user brings a ticket, PRD, design note, or substantive engineering task brief and wants to begin implementation planning; force every fixed rubric element to be accounted, cite present/weak claims against the brief, surface gaps, solution-as-goal traps, contradictions, and questions, then emit an intent-level plan. Do not use for bare one-line requests, non-engineering artifacts, code diff review, bug fixing, or execution itself.

Why use Anchor Intake on TypingMind?

Because you install it once and use it with any model. Anchor Intake 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 Anchor Intake in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lynxlangya/techne/tree/main/skills/anchor-intake. 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 Anchor Intake?

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 Anchor Intake?

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

Is the Anchor Intake AI skill free?

Yes. It is published on GitHub by lynxlangya 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇