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 simota/agent-skills --skill sparkgit clone --depth 1 https://github.com/simota/agent-skillsWrote 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/simota/agent-skills/spark)<a href="https://agentmods.dev/skills/simota/agent-skills/spark"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/spark/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/simota/agent-skills/spark"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/spark.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 Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- low Excessive Agency · line 99 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00035 | $0.05494 |
| Opus 5 | $0.00017 | $0.02747 |
| Sonnet 5 | $0.00007 | $0.01099 |
| Haiku 4.5 | $0.00003 | $0.00549 |
Grade A, and why
spark 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 6d 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark
"The best features are already hiding in your data. You just haven't seen them yet."
Spark proposes one high-value feature at a time by recombining existing data, workflows, logic, and product signals. Spark writes proposal documents, not implementation code.
What ships with it
21 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.
- _common 10 B
- reference/_common 13 B
- reference/autorun-schema.md 1.5 KB
- reference/collaboration-patterns.md 8.7 KB
- reference/compete-conversion.md 3.9 KB
- reference/experiment-lifecycle.md 5.1 KB
- reference/feature-ideation-anti-patterns.md 5.3 KB
- reference/feature-retrospective.md 9.4 KB
- reference/judge 11 B
- reference/kill-criteria-sunset.md 9.8 KB
- reference/lean-validation-techniques.md 3.8 KB
- reference/modern-product-discovery.md 7.1 KB
- reference/opportunity-sizing.md 8.3 KB
- reference/outcome-roadmapping-alignment.md 2.7 KB
- reference/persona-jtbd.md 1.1 KB
- reference/prioritization-frameworks.md 1.4 KB
- reference/proposal-templates.md 12 KB
- reference/spark 11 B
- reference/technical-integration.md 4.7 KB
- reference/tri-engine-proposal.md 15 KB
- reference/value-proposition-canvas.md 1.1 KB
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.
- 6d ago First seen · 289 lines · 35 tokens per session scan A 8664ec2f255c
spark is a skill published in the GitHub repository simota/agent-skills (76 stars, last pushed 7d ago), licensed MIT. It adds 35 tokens to every session and 5,494 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
gsd-tools
Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).
babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
spec-driven-development
Specification creation and management for the Pilot Shell methodology. Covers semantic search, clarifying questions, structured spec generation, and iterative refinement.
cog-meeting-processing
Process meeting recordings and transcripts into decisions, action items, and team dynamics.
cog-onboarding
Personalize COG Second Brain workflow through role pack selection and vault initialization.
cog-team-intelligence
Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs.