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.
npx skills add gotalab/cc-sdd --skill kiro-steeringgit 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/skills/gotalab/cc-sdd/kiro-steering)<a href="https://agentmods.dev/skills/gotalab/cc-sdd/kiro-steering"><img src="https://agentmods.dev/badge/skills/gotalab/cc-sdd/kiro-steering/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/skills/gotalab/cc-sdd/kiro-steering"><img src="https://agentmods.dev/badge/skills/gotalab/cc-sdd/kiro-steering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00019 | $0.01080 |
| Opus 5.5 | $0.00008 | $0.00432 |
| Sonnet 5.5 | $0.00004 | $0.00216 |
| Haiku 4.5 | $0.00002 | $0.00108 |
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 1mo 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 — 157 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):
Parallel Research
The following research areas are independent and can be executed in parallel:
- Product analysis: README, package.json, documentation files for purpose, value, core capabilities
- Tech analysis: Config files, dependencies, frameworks for technology patterns and decisions
- Structure analysis: Directory tree, naming conventions, import patterns for organization
If multi-agent is enabled, spawn sub-agents for each area above. Otherwise execute sequentially.
After all parallel research completes, synthesize patterns for steering files.
- 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
rules/steering-principles.mdfrom this skill's directory - 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
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.
- 1mo ago First seen · 157 lines · 19 tokens per session scan A 41aa31471f34
kiro-steering is a skill published in the GitHub repository gotalab/cc-sdd (3,738 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 1,080 once invoked, about $0.0001 per session on Opus 5.5. 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 skills, from other repositories
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Append one observation, question, hypothesis, or concern to TASKSTATE.md as lean, append-only task memory, leaving other artifacts and work undisturbed. Use when something surfaces mid-work, a hypothesis needs logging, or a concern is too small for decision-interview or targeted-questions. Do not use for a decision…
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Scan the session and task artifacts for reusable lessons (what was tried, what failed and why, what surprised us) and propose entries to append to LEARNINGS.md, the produce-side counterpart to what task-init reads. Use on demand mid-task after a hard-won fix or surprising failure, or at closure to sweep a long…
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daily-journal
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init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.