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 strikersam/autonomous-ai-agency --skill brain-dumpgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/brain-dump)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/brain-dump"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/brain-dump/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/strikersam/autonomous-ai-agency/brain-dump"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/brain-dump.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Anti-Refusal · line 66 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00520 |
| Opus 5 | $0.00000 | $0.00260 |
| Sonnet 5 | $0.00000 | $0.00104 |
| Haiku 4.5 | $0.00000 | $0.00052 |
Grade A, and why
brain-dump 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 11d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Brain Dump
Purpose
Convert a chaotic list of scattered ideas, notes, or thoughts into a structured, prioritized action plan. Transforms raw cognitive output into organized tasks with clear next steps.
When to Use
- You have a messy list of ideas and need structure
- Starting a new project and need to organize your thinking
- After a brainstorming session that needs to be actionable
- When feeling overwhelmed by too many loose threads
Instructions
Step 1: Capture Everything
Ask the user to dump all their thoughts without filtering. Accept bullet points, sentences, fragments — anything goes.
Step 2: Categorize
Group the raw input into logical buckets:
- Now — urgent or blocking items
- Soon — important but not immediate
- Later — good ideas to revisit
- Discard — noise or duplicates
Step 3: Structure Each Item
For each retained item, define:
- A clear action title (verb + object, e.g., "Write onboarding doc")
- The outcome if completed
- Any dependencies on other items
- Estimated effort (S/M/L)
Step 4: Produce the Plan
Output a structured markdown document:
## Action Plan — [Date]
### 🔴 Now
- [ ] [Action] — [Why urgent] (Effort: S/M/L)
### 🟡 Soon
- [ ] [Action] — [Outcome] (Effort: S/M/L)
### 🟢 Later
- [ ] [Action] — [Outcome] (Effort: S/M/L)
### 🗑️ Discarded
- [Item] — [Reason discarded]
Step 5: Confirm & Save
Ask the user to confirm the plan. If working inside the repo, offer to save to docs/plans/brain-dump-YYYY-MM-DD.md.
Example Prompt to Trigger
/brain-dump
Here's everything on my mind: [paste messy notes]
Output Format
- Structured markdown action plan
- Grouped by priority tier
- Each item has effort estimate and outcome
- Discarded items documented with reason
Notes
- Don't judge the quality of the input — organize it as-is
- Keep action titles under 10 words
- If an item is vague, flag it with a ❓ and ask a clarifying question
- Cross-link items that depend on each other
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.
- 11d ago First seen · 70 lines · 0 tokens per session scan A 298d620c3b5c
brain-dump is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 520 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 2026-08-31.
Other skills, from other repositories
context-preservation
State capture and restore across context window compactions. Monitors usage thresholds and serializes quality, task, and spec state for seamless continuation.
cog-braindump-capture
Capture raw thoughts with automatic domain classification and vault routing.
cog-knowledge-consolidation
Build structured knowledge frameworks from scattered vault notes with source attribution.
cog-onboarding
Personalize COG Second Brain workflow through role pack selection and vault initialization.
cog-weekly-reflection
Cross-domain pattern analysis with personal, professional, and project domain synthesis.
context-engineering
Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.