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 michalparkola/tapestry-skills --skill session-loggit clone --depth 1 https://github.com/michalparkola/tapestry-skillsWrote 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/michalparkola/tapestry-skills/session-log)<a href="https://agentmods.dev/skills/michalparkola/tapestry-skills/session-log"><img src="https://agentmods.dev/badge/skills/michalparkola/tapestry-skills/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/michalparkola/tapestry-skills/session-log"><img src="https://agentmods.dev/badge/skills/michalparkola/tapestry-skills/session-log.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 | $0.00048 | $0.01146 |
| Opus 5 | $0.00024 | $0.00573 |
| Sonnet 5 | $0.00010 | $0.00229 |
| Haiku 4.5 | $0.00005 | $0.00115 |
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 11d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Log
Summarize the current conversation and append to the weekly agent-log file.
Output Location
YYYY-wWW Agent Log.md
Where YYYY-wWW is the ISO week of today's date. Calculate with:
date +%Y-w%V
Format Rules
- Reverse chronological order — newest day on top
- One
##heading per day — format:## YYYY-MM-DD - Bullets, not subheadings — inside a day, use plain bullet
- Topic titleas topic separator, not###. No bold, no formatting on topic lines. - Details as nested bullets — one sentence per sub-bullet, can nest if needed for details. No bold. Nesting uses a TAB.
- CHUNK markers — if a topic produced a reusable output (a plan, a summary, a framework, a draft message), add nested bullet:
CHUNK: <descriptive title> - No explanatory text — no intros, no "in this session we discussed", no meta-commentary
- Append, don't replace — when a day heading already exists, add new bullets under it without removing existing content
Example
## 2026-02-28
- Analiza strategii X vs framework Y + moje obserwacje
- Strategia jest silna w A i B, słaba w C — brakuje fosy i horyzontu 3+lat.
- Naming produktu "Rescue" implikuje że kupujący jest ofiarą, co blokuje referencje.
- Anty-segment nie jest sprawdzalny z zewnątrz — to opis doświadczenia, nie filtr.
- CHUNK: 3-zdaniowe podsumowanie strategii
- CHUNK: Scorecard po 6 osiach
- Decyzja: follow-up z klientem
- Nie wysyłać feedbacku (nie prosił), wysłać link do artykułu jako wartość bez CTA.
Step-by-Step Workflow
1. Determine the target file
WEEK=$(date +%Y-w%V)
Target: ${WEEK} agent-log.md
2. Read existing file (if any)
The file may already have entries from earlier sessions this week. Read it first to avoid overwriting.
3. Review the full conversation and determine dates
Scan the entire conversation history. Identify:
- Topics — distinct subjects discussed (group related back-and-forth into one topic)
- Decisions — what was decided or concluded
- Outputs — any reusable artifacts (summaries, plans, draft messages, frameworks, scorecards)
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.
- 11d ago First seen · 104 lines · 48 tokens per session scan A 75bb16fb56c3
session-log is a skill published in the GitHub repository michalparkola/tapestry-skills (540 stars, last pushed 6mo ago), licensed MIT. It adds 48 tokens to every session and 1,146 once invoked, about $0.0002 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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