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 Filip-Podstavec/claude-leverage --skill session-loggit clone --depth 1 https://github.com/Filip-Podstavec/claude-leverageWrote 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/filip-podstavec/claude-leverage/session-log)<a href="https://agentmods.dev/skills/filip-podstavec/claude-leverage/session-log"><img src="https://agentmods.dev/badge/skills/filip-podstavec/claude-leverage/session-log/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/filip-podstavec/claude-leverage/session-log"><img src="https://agentmods.dev/badge/skills/filip-podstavec/claude-leverage/session-log.svg" alt="Reviewed on agentmods" width="80" 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 120 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 | $0.00097 | $0.01788 |
| Opus 5.5 | $0.00039 | $0.00715 |
| Sonnet 5.5 | $0.00019 | $0.00358 |
| Haiku 4.5 | $0.00010 | $0.00179 |
Grade A, and why
session-log 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.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/session-log
What it does
At the end of a working session, writes a short distilled summary to
docs/sessions/YYYY-MM-DD-<topic>.md following the standard template
(context, what was done, key decisions, open questions, next steps,
references). The next agent — yours tomorrow, or a different agent in a
month — reads it to pick up the thread without re-discovering everything
cold.
This is not a transcript dump. Raw chat is noise the next agent can't usefully consume. A session log is the distillate: 30–80 lines that compress a multi-hour conversation into actionable continuity.
Workflow
-
Find target dir. Default
docs/sessions/from the current repo root (viagit rev-parse --show-toplevel). If it doesn't exist, ask "create it now?" — if yes, create with a stubREADME.mddescribing the convention (mirror the one shipped with this plugin atdocs/sessions/README.md). -
Get topic. If
$ARGUMENTShas a positional, use it. Otherwise ask: "what was this session about, in one line?" Convert to kebab-case for the filename:2026-05-24-pivot-cleanup.md. -
Compute filename.
YYYY-MM-DD-<kebab-topic>.md. If a file with the same name exists (multiple sessions on the same topic same day), suffix with-2,-3, etc. -
Gather facts from git and the conversation:
- Current branch:
git rev-parse --abbrev-ref HEAD. - Recent commits this session:
git log --oneline -10. Use the first commit hash after the previous session's HEAD if you can infer it, otherwise show the last 5–10 with the human confirming which ones belong. - Conversation summary: distill from the actual chat above (you,
the model, have access). Categorize into:
- What was done — concrete actions: code shipped, decisions recorded, files moved, skills added. Bullet list.
- Key decisions — non-obvious calls made in conversation.
Link to ADRs if applicable (suggest
/adr-newfor any decision likely to be re-litigated). - Open questions — anything deferred or unresolved.
- Next steps — what should happen first in the next session.
- Current branch:
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 · 158 lines · 97 tokens per session scan A f20e5351d0e7
session-log is a skill published in the GitHub repository Filip-Podstavec/claude-leverage (68 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 1,788 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
cdb-scan
Map this codebase into project memory — a code graph of every symbol and how they connect, plus a written profile of stack, layout, conventions and workflows. Re-run any time to refresh both in place. Use when memory is newly installed on an existing project, or when the project has changed enough that the stored map…
handoff
Write a short handoff note for the next chat or a teammate, with what is done, what is open, what comes next and what was rejected and why. Saves it to project memory so the next chat sees it at the start, and prints it so it can be pasted into a message or a pull request. Use when the user ends a piece of work…
catchup
Answer "where did I stop?" for this project. Gives three short groups, done, open and next, taken from the last chat, the recorded to-dos, the last handoff note and git. Use when the user comes back to a project, starts a new chat, or asks where they left off. It writes nothing.
checkpoint
Save the current session to TokenMizer graph memory. Creates a persistent checkpoint with all tasks, decisions, files, and errors — resumable in any future session. Use when user says "save", "checkpoint", "remember this", "I'm done for today", or session is getting long.
resume
Load a previous session from TokenMizer graph memory. Returns a compact context block (100-600 tokens) covering goals, completed work, decisions, open tasks, and files. Inject this as system context to continue exactly where you left off. Use when user says "resume", "continue from last time", "load my project", "what…
pickup
Resume work on this project after an interruption or at the start of a new session: show recent wins, what was being done, the next action, and open decisions, in that order, then stop. Trigger only on an explicit /squirrel:pickup invocation, or an explicit request to resume or pick up this project's past work at the…