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 skills add event4u-app/agent-config --skill memory-consolidationgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/skills/event4u-app/agent-config/memory-consolidation)<a href="https://agentmods.dev/skills/event4u-app/agent-config/memory-consolidation"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/memory-consolidation.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 236 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00051 | $0.05206 |
| Opus 5 | $0.00026 | $0.02603 |
| Sonnet 5 | $0.00010 | $0.01041 |
| Haiku 4.5 | $0.00005 | $0.00521 |
Grade A, and why
memory-consolidation 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 4d 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 — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memory-consolidation
When to use
- Intake JSONL has accumulated unreviewed signals and
/memory:loadshows the inline-review block. - A pattern recurred across recent sessions (correction, preference, decision, repeat-bug) and is at risk of being forgotten by the next fresh chat.
- Before closing out a multi-day implementation, capture project-scoped facts so the next agent does not re-discover them.
Do NOT use for one-off code review notes (those belong in PR comments,
not memory), or for transient TODOs (use the task list). User-attribute
facts like name, language, or IDE preference route to the global
user-scoped channel (§ Global user-scoped channel, below) — never into
agents/memory/ curated YAML — and land via /agents:user review /
/agents:user accept, not /memory:promote.
Cognition cluster
- Mental model 5 — Signal vs. noise. A consolidation pass that
promotes 30 entries from a 50-message session is noise; the Pareto
cut is roughly 3–5 promote-worthy signals per cycle. See
docs/contracts/mental-models.md§ 5. - Mental model 12 — Defense in depth. Date-discipline, tag intersection, and per-invocation transcript-access confirmation are three independent guards; any one alone fails open. See § 12.
Procedure
The loop is four sequential phases. Each phase has one exit gate; do not advance until the gate is green.
Phase 1 — ORIENT (review scope and assess adapter)
- Confirm scope: which project, which time window, which transcript
source. Default window: last 14 days. The agent must read the
user's last chat message for an explicit
--sinceoverride before defaulting. - Inspect the current curated state: list files under
agents/memory/and check the most recentlast_validatedtimestamps. Identify which schemas are stale before mining adds noise. Also resolvedetect_managed_agents_folder(projectRoot)once here — CONSOLIDATE step 2 needs the answer to know whether project-scoped facts land inagents/memory/intake/or route through the global buffer instead (§ Project attribution channel, below). - Review the repo slot of the context-spine for project boundaries (modules, owners, sensitive paths). If empty, note the gap in the consolidation report; do not invent.
- Resolve the
TranscriptAdapterfor the current host (see Adapter contract below). If no adapter matches, stop and route the user to/memory:proposefor manual signal entry. Do not synthesize.
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.
- 4d ago First seen · 401 lines · 51 tokens per session scan A 699b0dd291bc
memory-consolidation is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 5,206 once invoked, about $0.0003 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 2026-09-04.
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