deepseek-harness: Skill for Claude Code

.agents/skills/dsh-archive-agent-notes/SKILL.md

dsh-archive-agent-notes is a skill for Claude Code, Codex from deepseek-ai/deepseek-harness. It costs 85 tokens per session (1,311 once invoked), scanned A, original, MIT.

A procedure for maintaining Agent Notes in DeepSeek Harness, a software project. Agent Notes record important decisions, rejected approaches, and reasons that may guide future work.

In plain words
What is it for?
Use it when adding, reviewing, merging, archiving, restoring, or removing Agent Notes, including checking whether a newer note replaces an older one.
Why use it?
It prevents the notes from becoming a confusing collection of outdated or duplicate records while keeping history that can still affect decisions.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is deepseek-ai/deepseek-harness's own configuration. It tells Claude Code and Codex how to work on deepseek-harness itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything deepseek-harness configures →

About the project

DeepSeek Harness is an open-source framework for building and running AI agents, organized so that its functionality is provided through plugins. Developers use it to launch agent workflows through a local web interface or from source, while the catalogue add-ons extend the harness as plugins.

deepseek-ai/deepseek-harness · 260,751 stars · on GitHub · deepseek.com

Reuse

Borrowing it

Nothing to install: this file belongs to deepseek-ai/deepseek-harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/deepseek-ai/deepseek-harness/master/.agents/skills/dsh-archive-agent-notes/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/deepseek-ai/deepseek-harness

Made for: Claude Code, Codex.

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agentmods badge for dsh-archive-agent-notes

README.md
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Your own site
<a href="https://agentmods.dev/skills/deepseek-ai/deepseek-harness/dsh-archive-agent-notes"><img src="https://agentmods.dev/badge/skills/deepseek-ai/deepseek-harness/dsh-archive-agent-notes/github.svg" alt="Measured on agentmods" height="20"></a>

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Your own site · 80×15
<a href="https://agentmods.dev/skills/deepseek-ai/deepseek-harness/dsh-archive-agent-notes"><img src="https://agentmods.dev/badge/skills/deepseek-ai/deepseek-harness/dsh-archive-agent-notes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,311 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk pass 7 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00085 $0.01311
Opus 5.5 $0.00034 $0.00524
Sonnet 5.5 $0.00017 $0.00262
Haiku 4.5 $0.00009 $0.00131

Measured 1mo ago against content hash 298e95629ade, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dsh-archive-agent-notes 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.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

.agents/skills/dsh-archive-agent-notes/SKILL.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Archive DeepSeek Harness Agent Notes

Reduce the active decision corpus without erasing history that can still guide work. Judge every note semantically; word count and age are discovery aids, never archive criteria.

Read the contracts

Read the Agent Note rules, the archive instructions, and the applicable active lifecycle instructions before classifying. Use current code, configuration, package docs, generated catalogs, newer Agent Notes, and inbound links to establish whether a rationale still owns or constrains anything.

Check supersession when adding a note

Before drafting, apply the creation criteria. Every new Agent Note triggers a scoped audit of active notes covering the same decision, mechanism, or rejected alternative. Classify each full or partial supersession while writing the new note: archive qualifying implemented triplets in the same PR, retain and cross-link partial supersessions or independently useful rationale, reject obsolete proposals, and delete rejected notes that no longer prevent a plausible mistake. Apply the Agent Note consolidation rule when the new owner absorbs every unique proposition; do not defer a known match to a later corpus audit.

Classify by future value

Apply these lifecycle-specific outcomes:

  • Implemented — delete: delete complete triplets that only describe small UI adjustments or purely mechanical changes, and repair or remove inbound links. Local bug fixes, performance changes, new capabilities, and substantive behavior or ownership decisions do not qualify merely because they are implemented. Apply this criterion before keep/archive classification.
  • Implemented — keep active: retain a note when its rationale, alternatives, negative guarantees, durable/wire semantics, ownership boundary, security rule, or reintroduction condition is likely to guide a future change. Classify the decision, not the mechanics of its implementation: a mechanical rename or type extraction can still record lasting naming, compatibility, or ownership rules. Length does not matter.
  • Implemented — archive: archive a substantive historical decision when it is complete and unlikely to guide future work, but its historical rationale still warrants preservation. Do not archive records that meet the direct-deletion criterion.
  • Proposed — never archive: keep a live proposal active; if it is no longer worth pursuing, reject it with an honest reason and satisfy the rejected lifecycle format.
  • Rejected — keep only as a guardrail: retain a rejection only when the losing proposal remains a tempting, meaningful mistake and the note explains why it loses.
  • Rejected — delete: delete the whole triplet when the rejected idea is obsolete, superseded, no longer plausible, or unlikely to prevent re-litigation. Repair or delete inbound links.

Read the full file on GitHub · 69 lines

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. 1mo ago Changed · +9 tokens per session 298e95629ade
  2. 1mo ago Changed 86b7c6338f2e
  3. 1mo ago First seen · 69 lines · 76 tokens per session scan A 5500e960a645

Subscribe to this mod's changes

dsh-archive-agent-notes is a skill published in the GitHub repository deepseek-ai/deepseek-harness (260,751 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 1,311 once invoked, about $0.0003 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.

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