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.
npx agentmods add commands/zekiriabd/sdd-pro/feat-deepengit clone --depth 1 https://github.com/zekiriabd/SDD-ProWrote 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/zekiriabd/sdd-pro/feat-deepen)<a href="https://agentmods.dev/commands/zekiriabd/sdd-pro/feat-deepen"><img src="https://agentmods.dev/badge/commands/zekiriabd/sdd-pro/feat-deepen.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.01756 |
| Opus 5 | $0.00009 | $0.00878 |
| Sonnet 5 | $0.00004 | $0.00351 |
| Haiku 4.5 | $0.00002 | $0.00176 |
Grade A, and why
feat-deepen 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/feat-deepen — Élicitation structurée d'une FEAT
⚠️ Commande interne v7.0.0 — invocation manuelle ou via
/feat-generate --deepen. Préférer/sdd-fullou/dev-runen usage normal. (Fusion dans/feat-generateenvisagée — cf. roadmap, non planifiée.)
Enrichit une FEAT fonctionnelle existante via une bibliothèque de
15 techniques d'élicitation (@.sdd/docs/brainstorming-techniques.md,
emprunt BMad v7.0.0+). L'agent elicitor détecte le contexte de la
FEAT (compliance ? B2C ? IA ? incident ?) et recommande 2-3 techniques
adaptées au lieu d'appliquer mécaniquement les 5 historiques.
5 techniques historiques (v6.x défaut) : Pre-mortem, First Principles, Red Team, Stakeholder Mapping, Inversion. Nouvelles v7.0.0+ (10 disponibles) : SCAMPER, Reverse Brainstorming, 5 Whys, Customer Journey Mapping, Empathy Map, Crazy 8s, Six Thinking Hats, Cynefin, OKR Decomposition, Lotus Blossom.
Usage :
/feat-deepen {n}— mode interactif (agent choisit 2-3 techniques)/feat-deepen {n} --quick— mode one-shot, 3 techniques par défaut (§1+§3+§5)/feat-deepen {n} --techniques scamper,empathy— forcer techniques (futur v7.1+)
Quand l'utiliser ?
- Après
/feat-generatepour les features complexes ou critiques - AVANT
/us-generatepour maximiser la qualité des US générées - Optionnel : SDD_Pro fonctionne sans, mais les ACs et le code généré sont moins robustes pour les edge cases.
Hors scope : ne génère PAS de code, ne modifie PAS les US. Enrichit uniquement la FEAT parente + constitution §7 (P3 strict).
STEP 0.7 — Parser les flags via le wrapper Python (v7.0.0+ audit P3 D)
Recommandé : avant de parser manuellement les arguments, invoquer le wrapper déterministe qui valide les 15 noms de techniques canoniques :
echo "{raw user input string}" | python -m sdd_scripts.elicitor_args
# Sortie : workspace/.sys/.state/elicitor-{n}.args.json
# stdout : JSON parsé avec techniques résolues (mode + liste finale)
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.
- 5d ago First seen · 174 lines · 18 tokens per session scan A b8adda279fdf
feat-deepen is a command published in the GitHub repository zekiriabd/SDD-Pro (191 stars, last pushed 2d ago), licensed Apache-2.0. It adds 18 tokens to every session and 1,756 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.