Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add MasonEgger/bpe-claude-code-plugin/plugin install bpeWrote 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/masonegger/bpe-claude-code-plugin/session-summary)<a href="https://agentmods.dev/skills/masonegger/bpe-claude-code-plugin/session-summary"><img src="https://agentmods.dev/badge/skills/masonegger/bpe-claude-code-plugin/session-summary/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/skills/masonegger/bpe-claude-code-plugin/session-summary"><img src="https://agentmods.dev/badge/skills/masonegger/bpe-claude-code-plugin/session-summary.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 | $0.00016 | $0.01179 |
| Opus 5.5 | $0.00006 | $0.00472 |
| Sonnet 5.5 | $0.00003 | $0.00236 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
session-summary 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 2mo 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Summary Command
Create a complete session summary and capture lessons learned. This command performs two actions:
- Generates a session summary file in
.ai-sessions/ - Appends lessons learned to
.ai-sessions/lessons.md
Step 1: Load the Format Reference and Setup
Read the canonical format reference at ${CLAUDE_PLUGIN_ROOT}/references/session-management.md. Treat that file as the single source of truth for templates, naming conventions, and lesson capture guidance; do not restate or reinterpret its rules in this command.
If .ai-sessions/ does not exist, create it:
mkdir -p .ai-sessions
Generate the timestamp using this exact command:
date +%Y%m%d-%H%M
Step 2: Generate Session Summary
Create .ai-sessions/session-{timestamp}-{slug}.md per the naming convention and section template in the format reference ("Session Summary Template" section).
Populate every required section. Pull content from this conversation:
- Header metadata (date, duration, conversation turns, estimated cost, model)
- Goal Context (only if a
/goalran in this session, per "Goal Context Populating Rule" in the reference; omit the section entirely otherwise) - Key actions taken
- Prompt inventory (table of user prompts → actions → outcomes)
- Efficiency insights, process improvements, observations
- Suggested skills for next session (which skills the next
/bpe:execute-planshould invoke at its hardened skill-loading step; see "Suggested Skills Populating Rule" in the reference)
If .ai-sessions/implementation-notes.md exists and contains a ## Step <N> section for the step this summary covers, extract its bullets and add a ## Deviations from Plan section to the session summary with them.
Then remove that ## Step N section from implementation-notes.md: keep the file if other sections remain, else delete the file.
The entry format (- Plan said: <what> / - Deviated: <what actually happened> / - Impact: <consequence> under a ## Step N heading) is defined in the format reference ("implementation-notes.md Format" section).
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.
- 2mo ago First seen · 84 lines · 16 tokens per session scan A 948809137124
session-summary is a skill published in the GitHub repository MasonEgger/bpe-claude-code-plugin (7 stars, last pushed 27d ago), licensed MIT. It adds 16 tokens to every session and 1,179 once invoked, about $0.0001 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 skills, from other repositories
knowledge-ingest
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mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
forget
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Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…