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-implgit 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-impl)<a href="https://agentmods.dev/skills/gotalab/cc-sdd/kiro-impl"><img src="https://agentmods.dev/badge/skills/gotalab/cc-sdd/kiro-impl/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-impl"><img src="https://agentmods.dev/badge/skills/gotalab/cc-sdd/kiro-impl.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 228 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 210 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00027 | $0.04136 |
| Opus 5.5 | $0.00011 | $0.01654 |
| Sonnet 5.5 | $0.00005 | $0.00827 |
| Haiku 4.5 | $0.00003 | $0.00414 |
Grade A, and why
kiro-impl 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 28d 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
kiro-impl Skill
<background_information> You operate in two modes:
-
Autonomous mode (no task numbers): Dispatch a fresh sub-agent per task, with independent review after each
-
Manual mode (task numbers provided): Execute selected tasks directly in the main context
-
Success Criteria:
- All tests written before implementation code
- Code passes all tests with no regressions
- Tasks marked as completed in tasks.md
- Implementation aligns with design and requirements
- Task completion follows the selected review mode
-
Review Mode:
- Default is
required - Accept explicit forms:
--review required|inline|off - Also accept clear natural-language opt-outs such as
skip revieworwithout reviewasoff - If the request is ambiguous, keep
required</background_information>
- Default is
Step 1: Gather Context
Reuse steering/spec context already available from conversation; load missing context below. Select skills for the current task even when steering/spec context is already available:
{{KIRO_DIR}}/specs/{feature}/spec.jsonandtasks.mdfor approvals, task selection, and dependencies- Referenced sections of
requirements.mdanddesign.md, expanding to related contracts as needed for the selected task - Core steering context:
product.md,tech.md,structure.md - Additional steering files only when directly relevant to the selected task's boundary, runtime prerequisites, integrations, domain rules, security/performance constraints, or team conventions that affect implementation or validation
- Use explicitly requested skills and task-relevant local skills/playbooks, including design, accessibility, and UX. Select by description and read only needed guidance, even for small tasks; preserve required checks and host/project rules.
Preflight
Validate approvals:
- Verify tasks are approved in spec.json (stop if not, see Safety & Fallback)
Discover validation commands:
- Inspect repository-local sources of truth in this order: project scripts/manifests (
package.json,pyproject.toml,go.mod,Cargo.toml, app manifests), task runners (Makefile,justfile), CI/workflow files, existing e2e/integration configs, thenREADME* - Derive a canonical validation set for this repo:
TEST_COMMANDS,BUILD_COMMANDS, andSMOKE_COMMANDS - Prefer commands already used by repo automation over ad hoc shell pipelines
- For
SMOKE_COMMANDS, choose the lightest trustworthy runtime-liveness check for the app shape (for example: root URL load, Electron launch, CLI--help, service health endpoint, mobile simulator/e2e harness if one already exists) - Keep the full command set in the parent context, and pass only the task-relevant subset to implementer and reviewer sub-agents
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 28d ago Changed · -12 lines 332e2348997c
- 29d ago Changed · +3 lines bc589734417e
- 1mo ago First seen · 270 lines · 27 tokens per session scan A 7a08471f7215
kiro-impl is a skill published in the GitHub repository gotalab/cc-sdd (3,731 stars, last pushed 9d ago), licensed MIT. It adds 27 tokens to every session and 4,136 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.
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