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 agentmods add commands/thesmokedev/taskchad-os/rcagit clone --depth 1 https://github.com/TheSmokeDev/taskchad-osWrote 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/commands/thesmokedev/taskchad-os/rca)<a href="https://agentmods.dev/commands/thesmokedev/taskchad-os/rca"><img src="https://agentmods.dev/badge/commands/thesmokedev/taskchad-os/rca.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.01118 |
| Opus 5 | $0.00005 | $0.00559 |
| Sonnet 5 | $0.00002 | $0.00224 |
| Haiku 4.5 | $0.00001 | $0.00112 |
Grade A, and why
rca scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root Cause Analysis: GitHub Issue #$ARGUMENTS
Objective
Investigate GitHub issue #$ARGUMENTS from this repository, identify the root cause, and document findings for future implementation.
Prerequisites:
- Working in a local Git repository with GitHub origin
- GitHub CLI installed and authenticated (
gh auth status) - Valid GitHub issue ID from this repository
Investigation Process
1. Fetch GitHub Issue Details
Use GitHub CLI to retrieve issue information:
gh issue view $ARGUMENTS
This fetches:
- Issue title and description
- Reporter and creation date
- Labels and status
- Comments and discussion
2. Search Codebase
Identify relevant code:
- Search for components mentioned in issue
- Find related functions, classes, or modules
- Check similar implementations
- Look for patterns or recent changes
Use grep/search to find:
- Error messages from issue
- Related function names
- Component identifiers
3. Review Recent History
Check recent changes to affected areas:
!git log --oneline -20 -- [relevant-paths]
Look for:
- Recent modifications to affected code
- Related bug fixes
- Refactorings that might have introduced the issue
4. Investigate Root Cause
Analyze the code to determine:
- What is the actual bug or issue?
- Why is it happening?
- What was the original intent?
- Is this a logic error, edge case, or missing validation?
- Are there related issues or symptoms?
Consider:
- Input validation failures
- Edge cases not handled
- Race conditions or timing issues
- Incorrect assumptions
- Missing error handling
- Integration issues between components
5. Assess Impact
Determine:
- How widespread is this issue?
- What features are affected?
- Are there workarounds?
- What is the severity?
- Could this cause data corruption or security issues?
6. Propose Fix Approach
Design the solution:
- What needs to be changed?
- Which files will be modified?
- What is the fix strategy?
- Are there alternative approaches?
- What testing is needed?
- Are there any risks or side effects?
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 · 221 lines · 10 tokens per session scan A a362b6ec29a0
rca is a command published in the GitHub repository TheSmokeDev/taskchad-os (23 stars, last pushed 13d ago), licensed MIT. It adds 10 tokens to every session and 1,118 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 2026-08-30.
Other commands, from other repositories
agent-brain-graph
Search using GraphRAG for relationship and dependency queries.
noosphere-upload
Submit a verified debugging lesson as Shared Skill evidence.
noosphere-consult
Consult Noosphere shared debug memory before fixing a bug.
diagnose
Run a self-check on your AI Brain Starter install (CLAUDE.md, Meta folder, skills, hooks, MCPs).
debug_pending_errors
../../foundation/agentinstructions/cursorcommands/debugpendingerrors.md.
foundation_manage_error_debugging
../../foundation/agentinstructions/cursorcommands/manageerrordebugging.md.