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/mozilla-ai/cq/reflectgit clone --depth 1 https://github.com/mozilla-ai/cqWhat 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 | $0.00033 | $0.02231 |
| Opus 5 | $0.00016 | $0.01115 |
| Sonnet 5 | $0.00007 | $0.00446 |
| Haiku 4.5 | $0.00003 | $0.00223 |
Grade A, and why
cq:reflect 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 2d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cq:reflect
Retrospectively mine this session for shareable knowledge units and submit approved candidates to cq.
Instructions
Step 1 — Summarize the session context
Construct a compact session summary covering:
- External APIs, libraries, or frameworks used.
- Errors encountered and how each was resolved.
- Workarounds applied for known or unexpected issues.
- Configuration decisions that only work under specific conditions.
- Tool calls that failed before the correct approach was found.
- Any behavior observed that differed from documentation or expectation.
- Dead ends abandoned and why.
The summary should be dense prose — enough for a reader with no prior context to reconstruct the session's technical events. Omit routine file edits, standard library calls, and anything already well-documented.
Step 2 — Identify candidate knowledge units
Reflection is agent-led — there is no MCP tool for this step. Using your own reasoning, scan the session for insights worth sharing.
A candidate is worth sharing if it meets all of these criteria:
- Generalizable — applies beyond this specific project or codebase. Strip all organization-specific names, internal service names, and proprietary identifiers.
- Non-obvious — not directly stated in official documentation, or contradicts documentation.
- Actionable — another agent could apply it immediately with a concrete change.
- Novel — unlikely to already exist in the commons (err toward including, not excluding).
Look specifically for:
- Undocumented API behavior — an endpoint returned an unexpected status code, response shape, or side effect.
- Workarounds for known issues — a library or tool required a non-standard setup to function correctly.
- Condition-specific configuration — a setting, flag, or option that behaves differently across versions, environments, or operating systems.
- Multi-attempt error resolution — an error that required more than one failed fix, where the solution was not obvious from the error message or documentation.
- Version incompatibilities — two libraries, tools, or runtimes that conflict at specific version combinations.
- Novel patterns — a non-obvious approach that solved a class of problem elegantly.
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.
- 2d ago First seen · 214 lines · 33 tokens per session scan A 466792497568
cq:reflect is a command published in the GitHub repository mozilla-ai/cq (1,258 stars, last pushed 18d ago), licensed Apache-2.0. It adds 33 tokens to every session and 2,231 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 2026-08-30.
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