cc-sdd is a spec-driven development workflow for coding agents: it turns approved software specifications into requirements, designs, task plans, and extended autonomous implementation. Developers use it across several AI coding agents, with independent review and task-level continuation for long-running work. The catalogue entries provide commands, skills, agents, and instructions for using this workflow.
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/gotalab/cc-sddWrote 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/gotalab/cc-sdd/kiro-steering)<a href="https://agentmods.dev/commands/gotalab/cc-sdd/kiro-steering"><img src="https://agentmods.dev/badge/commands/gotalab/cc-sdd/kiro-steering.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 | $0.00000 | $0.00983 |
| Opus 5 | $0.00000 | $0.00491 |
| Sonnet 5 | $0.00000 | $0.00197 |
| Haiku 4.5 | $0.00000 | $0.00098 |
Grade A, and why
kiro-steering 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 8d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- kiro-steering — 88% identical, 22 lines differ
- kiro-steering — 88% identical, 22 lines differ
- steering — 83% identical, 36 lines differ
- steering — 83% identical, 28 lines differ
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kiro Steering Management
<background_information>
Role: Maintain {{KIRO_DIR}}/steering/ as persistent project memory.
Mission:
- Bootstrap: Generate core steering from codebase (first-time)
- Sync: Keep steering and codebase aligned (maintenance)
- Preserve: User customizations are sacred, updates are additive
Success Criteria:
- Steering captures patterns and principles, not exhaustive lists
- Code drift detected and reported
- All
{{KIRO_DIR}}/steering/*.mdtreated equally (core + custom) </background_information>
Check {{KIRO_DIR}}/steering/ status:
Bootstrap Mode: Empty OR missing core files (product.md, tech.md, structure.md)
Sync Mode: All core files exist
Bootstrap Flow
- Load templates from
{{KIRO_DIR}}/settings/templates/steering/ - Analyze codebase (JIT):
glob_file_searchfor source filesread_filefor README, package.json, etc.grepfor patterns
- Extract patterns (not lists):
- Product: Purpose, value, core capabilities
- Tech: Frameworks, decisions, conventions
- Structure: Organization, naming, imports
- Generate steering files (follow templates)
- Load principles from
{{KIRO_DIR}}/settings/rules/steering-principles.md - Present summary for review
Focus: Patterns that guide decisions, not catalogs of files/dependencies.
Sync Flow
- Load all existing steering (
{{KIRO_DIR}}/steering/*.md) - Analyze codebase for changes (JIT)
- Detect drift:
- Steering → Code: Missing elements → Warning
- Code → Steering: New patterns → Update candidate
- Custom files: Check relevance
- Propose updates (additive, preserve user content)
- Report: Updates, warnings, recommendations
Update Philosophy: Add, don't replace. Preserve user sections.
Granularity Principle
From {{KIRO_DIR}}/settings/rules/steering-principles.md:
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.
- 8d ago First seen · 144 lines · 0 tokens per session scan A d9ddca0375ca
kiro-steering is a command published in the GitHub repository gotalab/cc-sdd (3,654 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 983 tokens. 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
capture-observation
Append a single observation, question, hypothesis, or concern to TASKSTATE.md as task memory without disrupting in-progress work or requiring a full state sync. Lean append-only; never restructures other artifacts. Use when something surfaces mid-work that should be remembered for later (during planning…
compact-task-memory
Produce a lossy compaction summary of TASKSTATE.md when task memory has grown beyond a useful working size, preserving canonical decisions and recommended next step while dropping stale facts. Distinct from sync-task-state (incremental, append-only, never lossy) and state-reconcile (drift repair, no shrinking). Use…
context-save
An elite context engineering specialist focused on comprehensive, semantic, and dynamically adaptable context preservation across AI workflows. This tool orchestrates advanced context capture, serialization, and retrieval strategies to maintain institutional knowledge and enable seamless multi-session collaboration.
init-workspace-flow-discovery
Phase 3 Discovery of init-workspace-flow.
memory-why
Show why a memory recall returned what it did -- BM25 vs vector vs hybrid provenance.
init-workspace-flow-questions
Phase 8 Questions of init-workspace-flow.