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Anchor Repro

Community
lynxlangya
anchor-repro

Reproduce a behavioral bug before fixing it, record the failing probe, and verify the fix with the same probe. Use for bug reports, failing tests, crashes, hangs, regressions, wrong output, and observable behavior that should change.

Overview

Publisherlynxlangya
Repositorytechne
Skill nameanchor-repro
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 Repro 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-repro
  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-repro .claude/skills/anchor-repro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anchor Repro 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 Repro 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 Repro 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-repro

Force the skipped move in debugging: observe the bug before editing, then prove the fix with the same observation.

Trigger Check

Use this skill when the task is to correct observable behavior that is currently wrong: a failing test, error message, stack trace, regression, crash, hang, wrong return value, wrong render, or "X does Y; it should do Z."

Do not use it for new features, refactors, renames, formatting, docs/copy changes, dependency bumps without a behavioral symptom, or performance work without a measurable probe.

Boundary test: can the wrongness be written as "running X currently produces Y; it should produce Z"? If yes, use this skill and make X the probe seed. If the statement cannot be formed from current information, ask for or find the missing observation before editing.

Forced Procedure

  1. Capture the symptom and stable anchor. Choose the most stable literal substring of the observable symptom for --expect. Omit heap addresses, PIDs, timestamps, temp paths, and wrapped fragments. Use --expect whenever the symptom has stable text. If no stable text exists, --no-stable-expect is the honest fallback, not an unanchored run; do not use it when stable textual output exists.
  2. Locate, then probe. Read enough code to place the behavior. Pick the smallest CLI probe that exercises the reported symptom. In workspaces or monorepos, run from the package with --cwd <package-dir>, not with a cd ... && convention. If required environment changes matter, encode them inside --shell as one quoted command string such as 'VAR=... command'; inherited environment changes are not part of probe identity in v1.
  3. Demonstrate. Run the probe through scripts/repro_ledger.py run --project <root> --bug <slug> ... -- <command>. Read the failing output and confirm it fails for the reported reason: mechanically, expectMatched should be true when --expect was supplied; procedurally, inspect the tail/context instead of trusting an exit code.
  4. Diagnose against the probe. Test hypotheses with discriminating runs before stacking edits. If a better probe is needed, the new probe starts a new fail -> pass cycle and must be observed failing too.
  5. Fix.
  6. Verify with the identical probe identity. Re-run the same execution mode, argv vector or shell string, --cwd, and --timeout. Then run scripts/repro_ledger.py close --project <root> --bug <slug>.
  7. Promote when possible. If the target repo has a test suite, promote the probe into a committed regression test. This is encouraged, not a gate.
  8. Report. Cite the close JSON, cite the first repro entry's git evidence, name the strength rung from reference.md, and carry speculative when the ledger took an unreproduced path.

Ledger Usage

Run and record a probe:

bash
python3 skills/anchor-repro/scripts/repro_ledger.py run \
  --project /path/to/project \
  --bug login-crash \
  --cwd packages/app \
  --expect "TypeError: cannot read" \
  --timeout 60 \
  -- npm test -- login.test.ts

Verify after the fix:

bash
python3 skills/anchor-repro/scripts/repro_ledger.py run \
  --project /path/to/project \
  --bug login-crash \
  --cwd packages/app \
  --expect "TypeError: cannot read" \
  --timeout 60 \
  -- npm test -- login.test.ts

python3 skills/anchor-repro/scripts/repro_ledger.py close \
  --project /path/to/project \
  --bug login-crash

Mark an honestly speculative fix only when reproduction is impossible:

bash
python3 skills/anchor-repro/scripts/repro_ledger.py mark-unreproduced \
  --project /path/to/project \
  --bug customer-only-crash \
  --no-probe-possible \
  --reason "Requires customer-only data and credentials unavailable in this environment"

The ledger writes target-project artifacts under .techne/repro/ and idempotently adds .techne/ to the target project's .gitignore. Do not commit .techne/ output.

Stop Conditions

  • Stop before editing if you have not run a failing probe or recorded an explicit mark-unreproduced path.
  • Stop if a failing run does not match the reported symptom; a hollow failure is not a reproduction.
  • Stop if a passing post-fix run uses a different identity; changed probes need their own fail -> pass cycle.
  • Stop before claiming a non-speculative fix if close does not exit 0 with a verified same-probe summary.

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 Repro AI skill do?

Reproduce a behavioral bug before fixing it, record the failing probe, and verify the fix with the same probe. Use for bug reports, failing tests, crashes, hangs, regressions, wrong output, and observable behavior that should change.

Why use Anchor Repro on TypingMind?

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

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

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 Repro?

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

Is the Anchor Repro 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.

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