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/contacttam/q-command-system/q-learningsgit clone --depth 1 https://github.com/contactTAM/q-command-systemWrote 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/contacttam/q-command-system/q-learnings)<a href="https://agentmods.dev/commands/contacttam/q-command-system/q-learnings"><img src="https://agentmods.dev/badge/commands/contacttam/q-command-system/q-learnings.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 | $0.00008 | $0.00358 |
| Opus 5 | $0.00004 | $0.00179 |
| Sonnet 5 | $0.00002 | $0.00072 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
q-learnings 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 5d 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
Session Learnings
Purpose: Reflect on and summarize key insights from the current session.
Step 1: Analyze Session
Review the current session for:
- Technical discoveries
- Process improvements
- Best practices identified
- Important decisions and their rationale
- Problems solved and how
- Things that didn't work (and why)
Step 2: Categorize Learnings
Organize by type:
Technical:
- Code patterns discovered
- Tool behaviors learned
- Architecture insights
- Performance findings
Process:
- Workflow improvements
- Communication patterns
- Efficiency gains
- What to do differently
Decisions:
- Key choices made
- Trade-offs considered
- Rationale documented
Best Practices:
- Patterns to repeat
- Anti-patterns to avoid
- Documentation insights
Step 3: Present in Chat
Display learnings as a clear summary:
=== Key Learnings from This Session ===
Technical:
- [Learning 1]
- [Learning 2]
Process:
- [Learning 1]
- [Learning 2]
Decisions:
- [Decision]: [Why we chose this]
Best Practices:
- [Practice to repeat]
- [Thing to avoid]
---
These learnings will be included in session notes if you run /q-end.
Note: This displays in chat only. To save to a file, use /q-end which includes learnings in session notes.
When to use:
- End of session to reflect
- After solving a difficult problem
- When you want to capture insights before they're forgotten
- For personal growth tracking
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.
- 5d ago First seen · 82 lines · 8 tokens per session scan A d59ff1e5b69d
q-learnings is a command published in the GitHub repository contactTAM/q-command-system (5 stars, last pushed 8mo ago), licensed MIT. It adds 8 tokens to every session and 358 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.