Borrowing it
Nothing to install: this file belongs to AIPMAndy/dna-memory. 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/AIPMAndy/dna-memory/main/.github/skills/tessl__bulk-optimize/SKILL.mdgit clone --depth 1 https://github.com/AIPMAndy/dna-memoryWrote 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/aipmandy/dna-memory/tessl__bulk-optimize)<a href="https://agentmods.dev/skills/aipmandy/dna-memory/tessl__bulk-optimize"><img src="https://agentmods.dev/badge/skills/aipmandy/dna-memory/tessl__bulk-optimize/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/aipmandy/dna-memory/tessl__bulk-optimize"><img src="https://agentmods.dev/badge/skills/aipmandy/dna-memory/tessl__bulk-optimize.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00090 | $0.01006 |
| Opus 5.5 | $0.00036 | $0.00402 |
| Sonnet 5.5 | $0.00018 | $0.00201 |
| Haiku 4.5 | $0.00009 | $0.00101 |
Grade A, and why
bulk-optimize 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 2mo 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulk Optimize
Improve external SKILL.md files for PR contributions using tessl skill review plus safety checks that prevent rejected PRs. Respects maintainers by detecting auto-generated files, preserving domain expertise, and suggesting appropriate PR scope.
For External PR Contributions
Adds safety checks for contributing to other people's repositories. See TEMPLATES.md for lessons from past PR rejections.
Workflow
Phase 1: Safety Checks (CRITICAL)
Run before making changes to prevent rejected PRs:
# 1. Auto-generated file detection
grep -i "auto-generated\|generated by\|DO NOT EDIT\|generator:" <skill-path>/SKILL.md
# 2. Recent migration detection
git log --oneline -10 --all -- <skill-directory>/ | grep -i "migrate\|rename\|move"
# 3. Repository pattern analysis
find . -name "SKILL.md" -type f | head -5 | xargs head -20
If auto-generated detected: STOP - changes must go to generator source If recent migration found: WARN - may cause merge conflicts Note domain patterns: Preserve expert framing and terminology in Phase 4
Phase 2: Baseline Evaluation
Run review for each skill:
tessl skill review <skill-path>/SKILL.md
Parse output to extract:
- Overall score
- Validation issues
- Description/content dimension scores
- Judge suggestions
If improving multiple skills:
- Track baseline scores for all
- Identify which need improvement most
- Consider PR scope (see Phase 7)
Phase 3: Discover Skill Bundle
Read SKILL.md and list files in its directory. Bundle = SKILL.md + sibling files + referenced files. Use for progressive disclosure improvements.
Use bundle context for recommendations.
Phase 4: Generate Recommendations (Preserve Domain Expertise)
Review recommendations to preserve valuable domain knowledge. Grep for specialized terminology (CRO, funnel, A/B test, etc.).
KEEP: Expert framing with domain concepts ("CRO expert who understands funnel optimization, conversion psychology...") REMOVE: Generic expert statements ("You are an expert at helping users...")
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.
- 2mo ago First seen · 136 lines · 90 tokens per session scan A 6f9684a8824b
bulk-optimize is a skill published in the GitHub repository AIPMAndy/dna-memory (97 stars, last pushed today), licensed Apache-2.0. It adds 90 tokens to every session and 1,006 once invoked, about $0.0004 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.
Other skills, from other repositories
kayba-ace
This skill ships learnfromtraces.py, a script that reads OpenClaw session transcripts, feeds them through the ACE learning pipeline, and writes an updated skillbook to disk.
pre-pr
Prepare a Honcho change for a pull request to plastic-labs/honcho. Invoke before opening a PR, when drafting a PR body, when asked if a branch is PR-ready, or when filling the pull request template. Checks required tests and docs, then writes Description / Proofs / Fixes.
ai-knot
Deterministic, self-hosted long-term memory for AI agents. Trigger when the user wants persistent memory, MCP-backed assistant memory, or wants to integrate ai-knot into Python, CrewAI, LlamaIndex, AutoGen, the OpenAI Agents SDK, PydanticAI, LangChain/LangGraph, TypeScript, or HTTP services. This is the default…
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
mem0-cli
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0…