DeepChat is an open-source desktop client for AI agents that keeps user data local first and connects agents with services such as MCP, Skills, ACP, and messaging apps. It is for people who want to use and control AI assistants from a desktop application. The catalogue add-ons extend DeepChat’s agent workflows.
Borrowing it
Nothing to install: this file belongs to ThinkInAIXYZ/deepchat. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ThinkInAIXYZ/deepchat/dev/.agents/skills/deepchat-sdd-cleanup/SKILL.mdgit clone --depth 1 https://github.com/ThinkInAIXYZ/deepchatWrote 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/thinkinaixyz/deepchat/deepchat-sdd-cleanup)<a href="https://agentmods.dev/skills/thinkinaixyz/deepchat/deepchat-sdd-cleanup"><img src="https://agentmods.dev/badge/skills/thinkinaixyz/deepchat/deepchat-sdd-cleanup.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00099 | $0.00679 |
| Opus 5 | $0.00049 | $0.00340 |
| Sonnet 5 | $0.00020 | $0.00136 |
| Haiku 4.5 | $0.00010 | $0.00068 |
Grade A, and why
deepchat-sdd-cleanup 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 8d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepChat SDD Cleanup
Rule
Run this skill only when the developer explicitly asks for SDD cleanup, documentation tidying, pruning, or removal of completed/stale SDD files. Do not run it as an automatic final step of ordinary feature, bug, architecture, or release work.
Workflow
- Inspect
docs/spec-driven-dev.md,docs/README.md, andgit status. - Inventory
docs/features,docs/issues, anddocs/architecturewithfindorrg. - Prefer parallel sub-agent review when available:
- one pass for
docs/features - one pass for
docs/issues - one pass for
docs/architecture - optional verifier pass over proposed deletes
- one pass for
- Apply only changes with clear evidence. Keep a concise keep/delete/update list for handoff.
- Validate references after edits.
Cleanup Rules
- Completed feature or architecture goal: delete
plan.mdand any legacytasks.md; keepspec.mdonly when it still defines a maintained contract, regression guard, platform policy, or architecture decision. - Completed issue goal: delete the issue folder when a linked GitHub issue is closed or the implementation and validation evidence prove the bug no longer exists.
- Removed feature: delete its folder when the product/code path is gone and the spec has no reusable decision record.
- Obsolete architecture: delete its folder when the module was fully replaced and the doc no longer describes a maintained boundary; otherwise update the spec.
- Historical feature spec affected by an architecture refactor: update the retained spec instead of leaving contradictory docs.
GitHub Checks
Use gh only when it is installed and authenticated. For linked issue docs, verify closure with
gh issue view <number> --json state,url,title when possible. If gh is unavailable, do not delete
solely because a GitHub link looks old.
Never Delete
- Active work with unchecked
plan.mdsteps or legacy tasks. - Any document containing unresolved
[NEEDS CLARIFICATION]. - A document referenced by
docs/README.md,docs/ARCHITECTURE.md,docs/FLOWS.md, or AGENTS instructions unless the reference is updated in the same change. - Runtime baselines or machine-read files unless the cleanup request explicitly covers them.
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.
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.
- 8d ago First seen · 61 lines · 99 tokens per session scan A b819f5401883
deepchat-sdd-cleanup is a skill published in the GitHub repository ThinkInAIXYZ/deepchat (6,310 stars, last pushed today), licensed Apache-2.0. It adds 99 tokens to every session and 679 once invoked, about $0.0005 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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