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 koersliven/Lore --skill lore-digestgit clone --depth 1 https://github.com/koersliven/LoreWrote 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/koersliven/lore/lore-digest)<a href="https://agentmods.dev/skills/koersliven/lore/lore-digest"><img src="https://agentmods.dev/badge/skills/koersliven/lore/lore-digest.svg" alt="Measured on agentmods" 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 | $0.00928 |
| Opus 5 | $0.00008 | $0.00464 |
| Sonnet 5 | $0.00003 | $0.00186 |
| Haiku 4.5 | $0.00002 | $0.00093 |
Grade A, and why
lore-digest 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 7d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/digest — Knowledge Buffer Flush
When to Trigger
- Stop hook detects buffer has reached flush threshold (5+ entries)
- Post-commit hook after git commit
- User explicitly invokes this skill
- Session end with buffered knowledge
Purpose
Convert raw conversation knowledge (stored in .ai-context/buffer.md) into a structured, versioned increment file in .ai-context/increments/. Each increment includes author, timestamp, confidence, affected files, and evidence for full source tracing.
Process
Step 1: Read Buffer
Read .ai-context/buffer.md. If empty or contains only lock markers, exit silently — no knowledge to flush.
Step 2: Analyze and Structure
Parse the buffer entries and reorganize them into a proper increment format with full metadata.
Buffer format (raw):
[round: 1] [DECISION] 支付超时改为 5s 因为上游 SLA 从 3s 升级
- Detail: 上游接口 SLA 从 3s 升级为 5s,支付超时需要同步调整...
Transform to increment format:
# YYYY-MM-DD payment-timeout-adjust
## Meta
- author: {{current agent name}}
- timestamp: YYYY-MM-DD HH:MM:SS
- trigger: auto-dialogue
- confidence: high
## Affected Files
- `PaymentService.java` → DECISION (payment timeout threshold)
- `payment-config.yaml` → CONSTRAINT (timeout rule)
## Changes
- `PaymentService.java`: timeout threshold changed from 3000ms to 5000ms
## Decisions
- Payment timeout adjusted to 5s
- 原因: upstream SLA changed from 3s to 5s, old timeout would trigger false rollback
- 讨论中提及: rule of thumb — timeout ≤ upstream SLA × 1.2
## Constraints
- Payment timeout must be ≤ upstream SLA × 1.2
- 来源: user explicitly stated in dialogue
## Evidence
- 来源: 利普, 2026-04-23, development dialogue
- 验证状态: unverified
- 验证人: —
Step 3: Determine Confidence
Assign confidence level based on source:
| Source | Confidence |
|---|---|
| User corrected agent (direct feedback) | high |
| User explained business context | high |
| Agent inferred from code analysis | medium |
| Agent guessed from usage patterns | low |
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.
- 7d ago First seen · 132 lines · 16 tokens per session scan A 62699077169f
lore-digest is a skill published in the GitHub repository koersliven/Lore (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 928 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
context-end
Close a workspace session by reviewing a proposed summary, recording approved outcomes, updating state and decisions, and checking repository safety. Use only when the user explicitly asks to end, close, or hand off the current session.
faf-expert
Expert in .faf (Foundational AI-context Format) files for persistent project context. Use when working with .faf files, project DNA, CLAUDE.md bi-sync, faf-cli commands, MCP server configuration, or AI-readiness scoring (0-100%). Updated for v2.8.0 Tool Visibility System.
context-update
Save a brief mid-session checkpoint to this workspace and update current state only when a priority or open thread changed. Use only when the user explicitly asks to checkpoint or save current progress.
import
Import a self-contained NeatContext context bundle shared by another person, or reconcile a newer copy of a context already on this machine, leaving the source bundle unchanged. Use only when the user explicitly invokes this skill or asks to import a NeatContext bundle.
save
Save durable decisions, findings, plans, and implementation knowledge from the visible Codex conversation into a new or existing NeatContext context. Use only when the user explicitly invokes this skill or asks to preserve the current conversation as reusable context.
create
Create a fresh NeatContext context from a user-defined behavioral profile and an existing local knowledge folder. Use only when the user explicitly invokes this skill or asks to create a new context rather than save the current conversation.