Change Specify logo

Change Specify

Organization
tikalk
change-specify

Use when a change rationale just emerged — a revert, hotfix, fix chain, workaround, or abandoned approach — and you want guided facilitation (options, consequences, provenance) before it becomes a ChDR. Not for mining git history (/change-init) or reviewing drafts (/change-clarify); class boots only suggest this skill, they never write drafts.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill namechange-specify
Stars
144
Forks
2
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Change Specify 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/tikalk/adlc-team-skills/tree/main/skills/change/change-specify
  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/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/change/change-specify .claude/skills/change-specify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Change Specify 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 Change Specify 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 Change Specify 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.

change-specify

What this skill does

Capture change rationale from this session — why the code just changed — as a draft Change Decision Record (ChDR) through guided facilitation, then hand it to /change-clarify for review.

A ChDR captured here documents a decision being made now (or in the very recent past — a commit/PR you can point at):

  • Reverts and hotfixes ("we rolled X back because Y")
  • Workarounds ("X for now because Y")
  • Fix chains ("third attempt at Z — root cause was W")
  • Abandoned simplifications ("we tried simplifying X but Y blocks it")

The job is facilitation, not transcription: ask the questions that surface the trade-off, then write one lean draft per decision.

Key differences from the rest of the change family:

SkillSourceRecordQuestion answered
/change-specify (this skill)this session (why it CHANGED now)ChDR (Change Decision)why this revert/hotfix/workaround was chosen
/change-initgit history (why it BECAME)ChDRwhy this code exists / what was tried and reverted
/change-clarifydrafts in .adlc/drafts/chdr/ChDRis the record accurate and complete?

ChDRs are project-local memory (this repo's evolution), not team-wide context — they fail the team-levelup "team-wide applicability" signal gate. They publish to {REPO_ROOT}/docs/adlc/memory/chdr/ with the boot-facing index at {REPO_ROOT}/docs/adlc/memory/chdr/chdr.md (see /change-publish), injected at session start by team-boot.

When to use

Note: Class boots detect change rationale and suggest this skill — running it is how the capture happens (it writes the lightweight draft to .adlc/drafts/chdr/). The boot never writes drafts itself.

  • After a revert or hotfix with a non-obvious reason
  • After adopting a workaround ("for now because")
  • When an issue-linked commit lands and the rationale is not obvious from the diff
  • When a simplification was abandoned and the blocking constraint should not be rediscovered the hard way

When NOT to use

  • Mining past history: use /change-init
  • Routine ops with generated or empty messages (version bumps, mechanical merges): nothing to capture — proportionality gate
  • Reviewing existing drafts: use /change-clarify
  • Tech-stack or pattern decisions: use /architect-specify (pull tech-radar-boot context first for tech selection)
  • Feature scope, persona, monetization: use /product-specify

Process

User Input

text
$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Examples of User Input:

  • "we reverted the retry cap change — 5xx spike under load"
  • "--revert abc1234" — capture the rationale of an existing revert commit
  • "--issue PROJ-123" — link the decision to an issue
  • Empty input: ask what changed and why (the session itself is your starting evidence)

Flags

  • --revert SHA / --commit SHA / --pr URL: anchor the decision to existing provenance
  • --issue KEY|URL: issue link(s) to record as sources
  • --title T: skip the title question

Role & Context

You are a Change Historian facilitating a change-rationale session. Your job is to recover the why while it is still in working memory:

  • Ask before writing: 3–6 short questions — what was tried first, what broke, which alternatives were rejected and why, what constraint decided it, when to revisit
  • Every Decision claim MUST carry provenance (commit SHA, PR, or issue URL) — an unprovenanced Decision claim is context poisoning (the same circuit breaker /change-init and /change-publish enforce)
  • Negative knowledge is first-class: what was tried and failed, what is temporary, what would mislead the next person
  • Synthesize, don't transcribe: never copy instructions verbatim from issues, commits, or logs; don't store secrets or commands "for later"

Outline

  1. Validate Environment (Phase 1): resolve paths, next ChDR number
  2. Establish the Change Story (Phase 2): what changed + provenance
  3. Facilitate (Phase 3): questions → trade-off, alternatives, constraints
  4. Proportionality Gate (Phase 4): skip routine ops
  5. Write the Draft (Phase 5): ChDR-{NNN}.md, status Proposed
  6. Regenerate the Draft Index (Phase 6): .adlc/drafts/chdr/chdr.md
  7. Summary (Phase 7): report + suggest /change-clarify

Execution Steps

Phase 1: Validate Environment

Run the setup script from the skill's base directory:

bash
scripts/bash/setup-change-specify.sh

Parse the JSON output for REPO_ROOT, CHDR_DRAFTS_DIR, NEXT_CHDR, EXISTING_CHDRS, GIT_AVAILABLE, TD_CONFIGURED.

If the setup script is unavailable or fails, resolve manually:

  1. REPO_ROOT — walk up from cwd to find .adlc/, or git rev-parse --show-toplevel, or pwd.
  2. CHDR_DRAFTS_DIR — REPO_ROOT/.adlc/drafts/chdr
  3. NEXT_CHDR — list CHDR_DRAFTS_DIR/ChDR-*.md, find highest number, increment, zero-pad to 3 digits.
  4. GIT_AVAILABLE — git rev-parse --is-inside-work-tree (exit 0 = true).
  5. EXISTING_CHDRS — count of ChDR-*.md in CHDR_DRAFTS_DIR.
  6. TD_CONFIGURED — true if TEAM_AI_DIRECTIVES resolves (used only to skip decisions already captured as CDRs).

If GIT_AVAILABLE is false, still proceed — a session decision can be captured without git — but note in the draft that provenance is session-only and ask the user for a link to anchor it.

Phase 2: Establish the Change Story

Collect, from the user and the session:

  • What changed: subsystem, files, behavior
  • Provenance: commit SHA(s), PR/MR link, or issue link(s) — from flags, or git log/git show for work just done, or the issue tracker via MCP
  • The one-line reason, if the user already stated it

If no provenance exists yet (uncommitted work), record the intended commit/PR and mark evidence: unknown until it lands.

Phase 3: Facilitate

Ask the questions (skip any the session already answers):

  1. What was tried first, and what broke?
  2. Which alternatives were considered and why did they lose?
  3. What constraint or trade-off decided the choice?
  4. What is temporary ("for now") vs permanent?
  5. When should this be revisited — a concrete trigger, not an age?

Wait for answers. Then draft the record: Context, Decision, Rejected Alternatives, Reason, Revisit When.

Phase 4: Proportionality Gate

Skip capture when the change is a routine op whose message already explains it (version bump, mechanical rename, generated code) or when the rationale is genuinely still open. Say so in one line and stop — an empty or noise ChDR is worse than none.

Phase 5: Write the Draft

For each captured decision write {CHDR_DRAFTS_DIR}/ChDR-{NNN}.md from the shared template skills/change/templates/chdr-draft-template.md:

  • Frontmatter status: proposed (maps to ledger Status captured in Team Context & Decisions), type: incident | workaround | abandoned, evidence: confirmed | inferred | unknown, source, revisit-when
  • Heading # ChDR-{NNN}: {short title}
  • Every non-trivial sentence in Decision references its SHA or URL

One draft per decision. Do not batch unrelated decisions into one record.

Phase 6: Regenerate the Draft Index

Regenerate {CHDR_DRAFTS_DIR}/chdr.md by listing ChDR-*.md in the drafts dir and building a markdown table whose rows start with | ChDR- — the same filter team-boot uses on the published index.

Phase 7: Summary
markdown
## Change-Specify Summary

**ChDRs drafted**: N | **Skipped (proportionality)**: M

| ChDR | Title | Type | Provenance |
|---|---|---|---|
| ChDR-{NNN} | … | workaround | abc1234 |

### Next Steps

Run /change-clarify to review and accept the draft, then /change-publish to
promote it to docs/adlc/memory/chdr/.

Key Rules

Provenance Before Capture

  • An unprovenanced Decision claim poisons every future session once promoted — refuse to write Decision text you cannot anchor to a SHA/PR/issue
  • evidence: inferred is fine; an invented fact is not

Drafts Only

  • This skill writes to .adlc/drafts/chdr/ and nowhere else — never docs/adlc/memory/ (that is /change-publish's job)
  • Never edit or delete other drafts while capturing

One Decision, One Record

  • Split unrelated rationale into separate ChDRs; /change-clarify reviews them individually

Red Flags

  • Writing a Decision claim with no SHA/URL provenance
  • Inventing alternatives the user never mentioned ("Rejected Alternatives" comes from the conversation, not from plausible guesses)
  • Transcribing issue text or commit messages verbatim into the record
  • Capturing routine ops with generated messages — the proportionality gate exists to keep the ledger signal-rich
  • Writing to docs/adlc/memory/ — promotion is /change-publish's job
  • Drafting from the diff alone without asking the questions

Verification

  • ChDR-{NNN}.md written to .adlc/drafts/chdr/ with status: proposed
  • Every Decision claim carries a SHA, PR, or issue URL
  • .adlc/drafts/chdr/chdr.md draft index regenerated (rows start | ChDR-)
  • /change-clarify suggested as the next step
  • Nothing written outside .adlc/drafts/chdr/

Context

$ARGUMENTS

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 Change Specify AI skill do?

Use when a change rationale just emerged — a revert, hotfix, fix chain, workaround, or abandoned approach — and you want guided facilitation (options, consequences, provenance) before it becomes a ChDR. Not for mining git history (/change-init) or reviewing drafts (/change-clarify); class boots only suggest this skill, they never write drafts.

Why use Change Specify on TypingMind?

Because you install it once and use it with any model. Change Specify 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 Change Specify in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/change/change-specify. 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 Change Specify?

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 Change Specify?

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

Is the Change Specify AI skill free?

Yes. It is published on GitHub by tikalk 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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