capture

A conversation note taker that saves selected facts from the current session into a temporary staging file. The saved notes can later be reviewed and added to a structured knowledge base.

In plain words
What is it for?
Use it to capture general facts, decisions, technical details, or specific information for later processing.
Why use it?
It prevents useful decisions and technical details from being lost when a conversation ends.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/harnessprotocol/harness-kit/capture
Any agent
npx skills add harnessprotocol/harness-kit --skill capture
Clone the repo
git clone --depth 1 https://github.com/harnessprotocol/harness-kit

Made for: Claude Code, Codex.

Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 997 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00073 $0.00997
Opus 5 $0.00036 $0.00498
Sonnet 5 $0.00015 $0.00199
Haiku 4.5 $0.00007 $0.00100

Measured 2d ago against content hash ca96a94da504, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

capture 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 2d 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.

plugins/capture/skills/capture/SKILL.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Session Staging

Overview

Capture information from the current conversation into a staging file for the daily reflection pipeline to consume and write to the knowledge graph.

Core principles:

  1. Token-conscious — 3-8 bullets, max 20 words each
  2. Complementary to Stop hook — captures nuance and specificity the auto-summary misses
  3. Append-only — never modify existing entries in the staging file
  4. Confirm what was staged — always show the user exactly what was written

Argument Types

Argument Behavior
(none) Auto-extract 3-8 most important facts from the conversation
Specific text Stage the provided facts as bullets
decisions Extract only decisions made this session
technical Extract only technical facts and implementation details

Workflow (MANDATORY — follow in order)

Step 1: Parse Input

Classify the argument:

  • No argument → auto-extract mode
  • Exactly the word decisions → filter to decisions only
  • Exactly the word technical → filter to technical facts only
  • Anything else → treat as specific facts provided by the user

Step 2: Resolve Staging File

Check in order:

  1. scripts/session-staging.md in the project root (current working directory)
  2. ~/.claude/session-staging.md as fallback

If the resolved file does not exist, create it with this header:

# Session Staging

Facts staged here are consumed by the daily reflection and written to the knowledge graph.

Step 3: Extract or Formulate Bullets

Based on argument type, produce 3-8 bullets. Each bullet: max 20 words, factual, specific.

Auto-extract (no argument): Scan the conversation for the most important facts — decisions made, technical details learned, status changes, new entities or tools introduced. Prefer concrete facts over vague summaries.

Specific facts (user-provided text): Convert the user's text into clean bullet points. If they already gave you bullets, clean and tighten them. If they gave you prose, extract the key facts.

Read the full file on GitHub · 105 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 2d ago First seen · 105 lines · 73 tokens per session scan A ca96a94da504

Subscribe to this mod's changes

capture is a skill published in the GitHub repository harnessprotocol/harness-kit (10 stars, last pushed 3d ago), licensed Apache-2.0. It adds 73 tokens to every session and 997 once invoked, about $0.0004 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.

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