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Feature Development · ledgerhq | DecimalAI

codingby ledgerhq

Feature Development

ledgerhq/feature-dev

Source ledgerhq/ledger-live ↗License MIT

Guided feature development with codebase understanding and architecture focus

SkillScore41Maintained

On-demandOn-demand: invoked explicitly — never occupies your agent's context until called.

Coding pack

Last verified 9/25/2026

What it changes

Passed 5 of 11 test cases with the skill, and 3 of 11 without it.

Without skill23%

With skill41%

Checks it passed that the model alone missed

The response advises against putting shared logic in libs/.
The response states that new features must be placed in src/mvvm/.
The response specifies launching 3 code-reviewer agents in parallel.

and 5 more in the full report

6 fixed3 passed either way11 still failing2 broke

Pass-rate lift: +18 pp
Tokens: +9%
Agent turns: 0%
Response time: -41%

Model gemini-3.6-flashMeasured 9/25/2026v29 of 22 cases passed with skill · 11 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/ledgerhq-feature-dev/SKILL.md https://app.decimal.ai/s/ledgerhq-feature-dev/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add ledgerhq-feature-dev, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/ledgerhq-feature-dev/ and pins it in .decimal/skills.lock

decimalai skills add ledgerhq-feature-dev

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/ledgerhq-feature-dev/SKILL.md https://app.decimal.ai/s/ledgerhq-feature-dev/SKILL.md

Raw file: /s/ledgerhq-feature-dev/SKILL.md ↗ · machine-readable summary at /s/ledgerhq-feature-dev.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 6

Prompt

When configuring parallel code-explorer agents during Phase 2 of feature development, how many key files should each agent return for detailed reading?

What was checked, without and with the skill
Checked	Without skill	With skill
The response specifies that each code-explorer agent should return a list of 5 to 10 key files.	Missed	Passed

case-22 · gemini-3.6-flash · 9/25/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md124 linessingle file

Feature Development

You are helping a developer implement a new feature for Ledger Wallet applications. Follow a systematic approach: understand the codebase deeply, identify and ask about all underspecified details, design elegant architectures, then implement.

References

New features must be implemented in src/mvvm/ following the MVVM architecture (.agents/skills/mvvm-architecture/SKILL.md).

Shared logic extracted out of an app goes to shared/, domain/ or features/, not to libs/.

Core Principles

Ask clarifying questions: Identify all ambiguities, edge cases, and underspecified behaviors. Ask specific, concrete questions rather than making assumptions. Wait for user answers before proceeding with implementation. Ask questions early (after understanding the codebase, before designing architecture).
Understand before acting: Read and comprehend existing code patterns first
Read files identified by agents: When launching agents, ask them to return lists of the most important files to read. After agents complete, read those files to build detailed context before proceeding.
Simple and elegant: Prioritize readable, maintainable, architecturally sound code
Use TodoWrite: Track all progress throughout

Phase 1: Discovery

Goal: Understand what needs to be built

Initial request: $ARGUMENTS

Actions:

Create todo list with all phases
If feature unclear, ask user for:
- What problem are they solving?
- What should the feature do?
- Any constraints or requirements?
Summarize understanding and confirm with user

Phase 2: Codebase Exploration

Goal: Understand relevant existing code and patterns at both high and low levels

Track record

Benchmark

5 / 11

test cases passed, vs 3 / 11 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/25/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

ledgerhq / ledger-live

.agents/skills/feature-dev/SKILL.md

View on GitHub ↗

License MITImported 9/25/2026Version v2

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See it in action

What's inside

Feature Development

References

Core Principles

Phase 1: Discovery

Phase 2: Codebase Exploration

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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