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 skills/codejunkie99/agentic-stack/memory-managernpx skills add codejunkie99/agentic-stack --skill memory-managergit clone --depth 1 https://github.com/codejunkie99/agentic-stackWhat 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 | $0.00002 | $0.00533 |
| Opus 5 | $0.00001 | $0.00267 |
| Sonnet 5 | $0.00000 | $0.00107 |
| Haiku 4.5 | $0.00000 | $0.00053 |
Grade A, and why
memory-manager 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 yesterday.
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
Memory Manager — the skill that reads the filing cabinet
After every major task or failure, call tools/memory_reflect.py to log what
happened. Before important decisions, read the top entries from
memory/semantic/LESSONS.md and memory/semantic/DECISIONS.md.
When to trigger full consolidation
- On explicit
reflecttrigger from the user. - When the context window is getting full.
- When episodic memory exceeds ~500 entries.
Consolidation steps
- Load top-5 episodic entries by salience score.
- Detect recurring patterns across the last 100 entries.
- For patterns appearing ≥3 times, promote to
memory/semantic/LESSONS.md. - Flag any skill with ≥3 failures in 14 days for rewrite (see
on_failure.py). - Archive resolved working context to
memory/episodic/snapshots/. - Commit via git so history is preserved:
git log memory/is the agent's autobiography.
Searching memory by keyword
When you need to find a specific memory by topic or keyword (not just the top-salience entries), use the FTS5 search tool:
python3 .agent/memory/memory_search.py <query>
This indexes all .md and .jsonl files under .agent/memory/ and returns
ranked results with context snippets. Falls back to grep if FTS5 is not
available. The index auto-rebuilds when files change.
Anti-patterns
- Do not auto-merge
personal/intosemantic/— user preferences are not general knowledge. - Do not delete entries to "clean up" memory. Archive them.
- Do not promote lessons from a single incident. Require recurrence.
Self-rewrite hook
Every 10 reflections, or when the same type of mistake appears 3+ times
recently, this skill's approach to salience or distillation needs adjustment.
Propose conservative edits and log the diff in memory/semantic/DECISIONS.md.
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.
- yesterday First seen · 51 lines · 2 tokens per session scan A 966dddfc88a6
memory-manager is a skill published in the GitHub repository codejunkie99/agentic-stack (2,241 stars, last pushed 25d ago), licensed Apache-2.0. It adds 2 tokens to every session and 533 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 2026-08-30.
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