Gate Trace

Community

danielvm-git

gate-trace

Deterministic traceability quality gate — reads coverage matrix + blind-spot data, applies decision rules with oracle confidence downgrade, emits PASS/CONCERNS/FAIL/WAIVED verdict. Use before release-branch to gate merges on traceability.

Overview

Publisher	danielvm-git
Repository	`bigpowers`
Skill name	`gate-trace`
Stars	206
Forks	18
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by danielvm-git on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/gate-trace .claude/skills/gate-trace

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gate Trace 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 Gate Trace 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 Gate Trace 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.

Gate Trace

Deterministic quality gate that combines traceability coverage and blind-spot data into a single PASS/FAIL/CONCERNS/WAIVED decision before release.

Decision Rules

Rule	Condition	Verdict
R1	Any undone story with 0 code tags	FAIL
R2	Any story done but no verify evidence	CONCERNS
R3	P0 story (top WSJF quartile) with 0% coverage	FAIL
R4	Overall coverage < 60%	CONCERNS
R6	P0 story + eNN-TEST_PLAN_LATEST.md exists + zero SC-eNNsYY-P0-* in test files	CONCERNS
R5	Overall ≥ 80% + no critical gaps + all verify → PASS	PASS

Oracle Confidence Downgrade

TEA-inspired: if trace links rely on heuristics rather than explicit tags, confidence drops.

Heuristic Ratio	Downgrade
> 50% links from heuristics (file-name or task-reference)	One level (PASS→CONCERNS, CONCERNS→FAIL)
> 80% links from heuristics	Two levels (PASS→FAIL, CONCERNS→FAIL)

Process

Pre-flight: Ensure scripts/trace-stories.sh --json has been run (produces specs/traceability-matrix.json). If not present, run it.
Ensure scripts/check-blind-spots.sh has been run (produces specs/blind-spots.json). If not present, run it.
Read specs/traceability-matrix.json and specs/blind-spots.json.
Apply decision rules R1–R5 in order (first match wins).
Apply oracle confidence downgrade based on the heuristic link ratio from the matrix's oracle_stats.
Drift check (e39s03): If specs/drift-report.json exists and has suspect links, mark verdict as CONCERNS with note: "Drift detected — some implementing files are newer than their specs. Run scripts/check-spec-drift.sh for details."
Output verdict + rationale to stdout.
Adversarial refute check (e45s32): Before emitting PASS, attempt to refute the verdict — list at least one concrete traceability gap that would block merge if it were real. If the gap is real, downgrade the verdict. Rubber-stamping is prohibited; every PASS must survive one refutation attempt.
Completeness critic (e45s05) — Run adversarial gap-finding:

bash
bash scripts/lib/completeness-critic.sh

Classify output as BLOCKER / WARNING / FILLED. BLOCKER overrides any PASS verdict → FAIL. Append critic summary to rationale. 10. Update specs/execution-status.yaml with gate-trace result.

Verdict Semantics

Verdict	Meaning	Action
PASS	All gates satisfied	Proceed with merge
CONCERNS	Non-critical issues found	Requires explicit human override in `state.yaml`
FAIL	Critical traceability gap	Block merge — fix gap first
WAIVED	Cannot evaluate (missing inputs)	Skip gate — data not available

Output Format

yaml
gate_trace:
  verdict: PASS|CONCERNS|FAIL|WAIVED
  generated_at: "<ISO 8601>"
  rationale: "<human-readable explanation>"
  heuristic_ratio: <0.0–1.0>
  downgrade_applied: <true|false>

Integration Points

release-branch SKILL.md — pre-PR gate (FAIL blocks merge).
sync-skills.yml — runs trace-stories.sh --strict as its "Traceability gate" step (FAIL blocks pipeline), which shares this skill's coverage data (traceability-matrix.json) but does not invoke a gate-trace PASS/CONCERNS/FAIL/WAIVED verdict directly.
verify-work SKILL.md — runs blind-spot checks; gate-trace consumes the result.

References

BMAD TEA traceability approach (market survey 2026-07-02).
scripts/trace-stories.sh (e38s01) — produces traceability-matrix.json.
scripts/check-blind-spots.sh (e38s04) — produces blind-spots.json.
specs/execution-status.yaml — output target for gate result.

Handoff

Gate: READY → next: release-branch (final step before merge) Writes: state.yaml handoff.next_skill = release-branch

Verify

→ verify: test -x scripts/run-gate-trace-verify.sh && bash scripts/run-gate-trace-verify.sh --self-test && echo OK

Frequently asked questions

What does the Gate Trace AI skill do?

Why use Gate Trace on TypingMind?

Because you install it once and use it with any model. Gate Trace 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 Gate Trace in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielvm-git/bigpowers/tree/main/skills/gate-trace. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gate Trace?

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 Gate Trace?

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

Is the Gate Trace AI skill free?

Yes. It is published on GitHub by danielvm-git 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Gate Trace

Decision Rules

Oracle Confidence Downgrade

Process

Verdict Semantics

Output Format

Integration Points

References

Handoff

Verify

Frequently asked questions

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