Borrowing it
Nothing to install: this file belongs to HelixDevelopment/code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/HelixDevelopment/code/main/.specify/extensions/superspec/.specify-dev/agent-commands/claude/speckit-superspec-tasks/SKILL.mdgit clone --depth 1 https://github.com/HelixDevelopment/codeWrote 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/helixdevelopment/code/speckit-superspec-tasks)<a href="https://agentmods.dev/skills/helixdevelopment/code/speckit-superspec-tasks"><img src="https://agentmods.dev/badge/skills/helixdevelopment/code/speckit-superspec-tasks/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/helixdevelopment/code/speckit-superspec-tasks"><img src="https://agentmods.dev/badge/skills/helixdevelopment/code/speckit-superspec-tasks.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 | $0.00024 | $0.00545 |
| Opus 5 | $0.00012 | $0.00272 |
| Sonnet 5 | $0.00005 | $0.00109 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
speckit-superspec-tasks 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 4d 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.
What it actually says
speckit.superspec.tasks
Generate a phased task breakdown using writing-plans skills.
Usage
/speckit.superspec.tasks [spec-number|spec-path]
Process
- Read the spec, plan, and constitution for the target feature
- Read the template at
.specify/templates/tasks-template.md - Superpowers detection: Check for
writing-plansskill- If found: Read the writing-plans SKILL.md and follow its task decomposition process, adapting outputs to the tasks template structure
- If not found: Decompose directly from the plan using the template
- Organize tasks by phase: Setup → Foundational → User Stories (by priority) → Polish
- Apply execution markers to each task:
[P]— can run in parallel (different files, no dependencies)[TDD]— must follow RED-GREEN-REFACTOR discipline[REVIEW]— requires code review before proceeding[SUBAGENT]— can be delegated to a subagent
- Define phase dependencies and checkpoint gates
- Write to
specs/NNN-feature-name/tasks.md
Output
specs/NNN-feature-name/tasks.md with phased task breakdown.
Execution Markers
| Marker | Meaning | Behavior |
|---|---|---|
[P] |
Parallel | Can run concurrently with other [P] tasks |
[TDD] |
Test-Driven | Must follow RED-GREEN-REFACTOR: write test → fail → implement → pass |
[REVIEW] |
Review Gate | Pause for human code review before proceeding |
[SUBAGENT] |
Subagent | Can be dispatched to a parallel subagent |
Superpowers Adaptation
When using the writing-plans skill, adapt its outputs:
- Implementation blueprints → merge into
specs/NNN/tasks.mdusing template structure - Task dependencies → map to the Dependencies section
- Parallel opportunities → mark with
[P]and[SUBAGENT]
See .specify/extensions/superspec/references/superpowers-bridge.md for full adaptation rules.
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.
- 4d ago First seen · 58 lines · 24 tokens per session scan A 7c4e534ce67d
speckit-superspec-tasks is a skill published in the GitHub repository HelixDevelopment/code (2 stars, last pushed 3d ago), licensed MIT. It adds 24 tokens to every session and 545 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 2026-09-04.
Other skills, from other repositories
babysit
Same-session monitoring loop for PRs, CI runs, tickets, and deployments using the monitorstart / monitorupdate / autonudgestop MCP tools. The loop re-injects your check instructions into THIS session on an idle interval — same context, same tools — and works from dashboard chat, Slack threads, and Discord DMs. Use…
pipeline-conductor
Operating procedure for the kirocrew-pipeline-conductor agent - run one issue/PR pipeline on one repository as a supervised fleet. Auto-pick items, preflight every candidate to one deterministic claim verdict, stand up one worker session per item in a dedicated folder, probe them each cycle with one script call…
feature-request
Conversational workflow for gathering user feedback and filing GitHub Issues on the Kiro Crew repository. Load when the user clicks "Request a Feature", wants to report a bug, or suggest an improvement.
goal-loop
Bootstrap a goal-driven self-improving AutoNudge loop from a goal plus an anchor directory, then run the board autonomously until the Definition of Done is met.
vigilante-create-issue
Help a human author write an implementation-ready GitHub issue that Vigilante can execute reliably.
vigilante-issue-implementation
Implement a GitHub issue end-to-end when Vigilante dispatches work for a watched repository. Use the provided worktree, respect repository instructions, comment on the issue as work progresses, and report failures back to GitHub.