Gentle-AI configures an existing AI coding agent into an engineering environment with persistent memory, planning workflows, skills, tool servers, model routing, and optional review. Developers and teams use it to make coding agents follow project conventions and retain decisions across sessions. The catalogue entries are its skills, commands, agents, and instruction.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Gentleman-Programming/gentle-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/gentleman-programming/gentle-ai/gentle-sdd-apply)<a href="https://agentmods.dev/commands/gentleman-programming/gentle-ai/gentle-sdd-apply"><img src="https://agentmods.dev/badge/commands/gentleman-programming/gentle-ai/gentle-sdd-apply/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/gentleman-programming/gentle-ai/gentle-sdd-apply"><img src="https://agentmods.dev/badge/commands/gentleman-programming/gentle-ai/gentle-sdd-apply.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00011 | $0.01006 |
| Opus 5 | $0.00005 | $0.00503 |
| Sonnet 5 | $0.00002 | $0.00201 |
| Haiku 4.5 | $0.00001 | $0.00101 |
Grade A, and why
gentle-sdd-apply scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to sdd-apply — 67 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
If the native sdd-apply sub-agent is available, delegate this command to it.
Otherwise, read the skill file at ~/.claude/skills/sdd-apply/SKILL.md FIRST, then follow its instructions exactly inline.
The sdd-apply skill (v2.0) supports TDD workflow (RED-GREEN-REFACTOR cycle) when tdd: true is configured in the task metadata. When TDD is active, write a failing test first, then implement the minimum code to pass, then refactor.
CONTEXT:
- Working directory: Detect agent-side before proceeding by running
git rev-parse --show-toplevelwith the Bash tool; if that fails, runpwdwith the Bash tool. - Current project: Derive agent-side from the detected working directory basename. Do not use slash-command shell interpolation for this value.
- Artifact store mode: engram
TASK: Implement the remaining incomplete tasks for the active SDD change.
STATUS GATE:
Read ~/.claude/skills/_shared/sdd-status-contract.md and produce structured status before acting. If $ARGUMENTS is missing or ambiguous, ask the user to choose and STOP. Do not guess. Continue only when status says apply is ready, spec/design/tasks exist, and actionContext allows implementation edits. If status reports workspace-planning with no allowed edit roots, STOP before launching apply or editing inline. Carry contextFiles, task progress, dependency states, and actionContext into the native sub-agent prompt when delegating.
ENGRAM PERSISTENCE (artifact store mode: engram): CRITICAL: mem_search returns 300-char PREVIEWS, not full content. You MUST call mem_get_observation(id) for EVERY artifact. STEP A — SEARCH (get IDs only): mem_search(query: "sdd/{change-name}/spec", project: "{project}") → save spec_id mem_search(query: "sdd/{change-name}/design", project: "{project}") → save design_id mem_search(query: "sdd/{change-name}/tasks", project: "{project}") → save tasks_id STEP A2 — CHECK PREVIOUS PROGRESS (before starting work): mem_search(query: "sdd/{change-name}/apply-progress", project: "{project}") → if found, save progress_id
- Previous apply-progress (if exists):
mem_search(query: "sdd/{change-name}/apply-progress", project: "{project}")→ read and merge STEP B — RETRIEVE FULL CONTENT (mandatory): mem_get_observation(id: spec_id) → full spec mem_get_observation(id: design_id) → full design mem_get_observation(id: tasks_id) → full tasks (keep tasks_id for updates) IF progress_id exists: mem_get_observation(id: progress_id) → read previous progress, skip completed tasks, MERGE when saving Update tasks as you complete them: mem_update(id: {tasks-observation-id}, content: "{updated tasks with [x] marks}") Save progress: mem_save(title: "sdd/{change-name}/apply-progress", topic_key: "sdd/{change-name}/apply-progress", type: "architecture", project: "{project}", capture_prompt: false, content: "{progress report}") Set capture_prompt: false when the Engram tool schema supports it; if an older schema rejects or does not expose the field, omit it rather than failing.
For each task:
- Read the relevant spec scenarios (acceptance criteria)
- Read the design decisions (technical approach)
- Read existing code patterns in the project
- Write the code (if TDD is enabled: write failing test first, then implement, then refactor)
- Mark the task as complete [x]
Return a structured result with: status, executive_summary, detailed_report (files changed), artifacts, and next_recommended.
REVIEW ROUTING (post-verify, not post-apply):
Return control to the parent orchestrator. Apply itself never routes to review — nextRecommended proceeds to verify once tasks are complete. If the parent later observes a fresh reviewOffer block, it may present and run only its exact invocation. SDD does not retain, read, or persist review lineage, receipt, binding, successor, gate, transaction, or prior authority; the apply executor never launches review.
{{GENTLE_AI_AUTHORITY_FIRST_TERMINAL_PROCEDURE}}
Approval returns one exact pending acknowledgement continuation. Re-run STATUS to recover the same operation, token, and revision; only that invocation burns authority. Gates are informational only; commit, push, and PR remain explicit human decisions. Never auto-launch Judgment Day.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 56 lines · 11 tokens per session scan A 8d419458df51
gentle-sdd-apply is a command published in the GitHub repository Gentleman-Programming/gentle-ai (6,475 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 1,006 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to sdd-apply, differing in 67 lines, and is treated as a copy.
Other commands, from other repositories
api-aqa-flow
Workflow for backend API test automation: TMS / Issue Tracker test cases → automated API tests, HITL-gated.
api-aqa-flow-test-case-specification
Phase 4 Test Case Specification of api-aqa-flow (HITL APPROVAL GATE).
implement-approved-slice
Implement only the approved slice with minimal, explicit, review-friendly changes, then persist execution evidence in slice notes and TASKSTATE.md. The single official execution path of the workflow. Supports an opt-in test-first (TDD) mode, enabled per slice or via --tdd, that writes the failing test before the code…
test-strategy
Define the smallest set of meaningful tests that protects behavior and reduces regression risk for the active task, then persist as TESTSTRATEGY.md plus a TASKSTATE.md update. Avoids forcing a strategy artifact when it adds no material signal. Detects a Godot or Unity target and routes to the matching headless runner…
execute-task
Execute a specific task and its sub-tasks systematically following a Test-Driven Development (TDD) workflow. This command reads task specifications from .code-captain/specs/ directories and implements features with comprehensive testing, following established code standards and best practices.
testgen-flow-project-config-loading
Phase 0 Project Config Loading of testgen-flow.