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.
git clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/recall)<a href="https://agentmods.dev/commands/naimkatiman/continuous-improvement/recall"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/recall/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/commands/naimkatiman/continuous-improvement/recall"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/recall.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00044 | $0.00464 |
| Opus 5.5 | $0.00018 | $0.00186 |
| Sonnet 5.5 | $0.00009 | $0.00093 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
recall 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 1mo 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
/recall — Episodic Search Over Past Sessions
Search this project's observation history for relevant prior activity and return the most relevant past tool calls, ranked.
What it does
Builds an in-memory BM25 index over ~/.claude/instincts/<project-hash>/observations.jsonl and returns the top matches with redacted snippets, relevance scores, and timestamps. Backed by the ci_recall MCP tool (expert mode) and the recall skill.
How to invoke
ci_recall query="permission denied push" # top 5 matches
ci_recall query="jq command not found" k=3 # cap results
ci_recall query="auth login" since=7d # only the last 7 days
since accepts an ISO timestamp or a relative window: 7d, 24h, 30m.
Output shape
## Recall: "permission denied push"
2 match(es), most relevant first:
- [2026-05-20T10:00:00Z] **Bash** (score 3.41)
…git push origin main Permission denied: harness blocked direct push to main…
- ...
Privacy
Snippets are passed through a secret redactor (AWS keys, JWT-shaped triplets, bearer tokens, KEY/SECRET/TOKEN/PASSWORD assignments, long hex strings) before display.
Notes
- Lexical, not semantic — search with the vocabulary that actually appeared in the tool calls; try several phrasings if the first returns nothing.
- Searches the full captured history, not just the recent window.
Pairs with
recallskill — the discipline this command runs.continuous-improvement(core SKILL.md, Law 1 — Research Before Executing).gateguard— recall a past failure before clearing a high-risk action.
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.
- 1mo ago First seen · 50 lines · 44 tokens per session scan A ad4a23529711
recall is a command published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 464 once invoked, about $0.0002 per session on Opus 5.5. 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
reflect-promote
Promote learnings from skills to global CLAUDE.md.
rem_help
Each section is printed verbatim by --help for the command it names. The agreed design is issue #1656; change a page there and here together.
memory
Read-side memory operations on the knowledge graph: search past decisions and patterns, load session context, view decision timelines, render Mermaid graph visualizations. Subcommands: search, load, history, viz, status. Use when finding or reviewing what memory already holds. For storing new knowledge instead, invoke…
compact-prep
Ask the agent to prepare for conversation compaction by updating any relevant state and providing guidance for the compaction agent and to kick off the session there after.
fire-reflect
After any failure (debug resolution, test failure, approach rotation, stalled loop), capture what was tried, why it failed, and what actually worked as a persistent reflection. Future sessions search these before investigating.
sync
Mid-session context refresh command. Keeps context fresh and consistent during work.