Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sillyDaibo/reasflow-devnpx agentmods add skills/sillydaibo/reasflow-dev/knowledge-card-retrievalWrote 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/sillydaibo/reasflow-dev/knowledge-card-retrieval)<a href="https://agentmods.dev/skills/sillydaibo/reasflow-dev/knowledge-card-retrieval"><img src="https://agentmods.dev/badge/skills/sillydaibo/reasflow-dev/knowledge-card-retrieval/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/sillydaibo/reasflow-dev/knowledge-card-retrieval"><img src="https://agentmods.dev/badge/skills/sillydaibo/reasflow-dev/knowledge-card-retrieval.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00039 | $0.01667 |
| Opus 5.5 | $0.00016 | $0.00667 |
| Sonnet 5.5 | $0.00008 | $0.00333 |
| Haiku 4.5 | $0.00004 | $0.00167 |
Grade A, and why
knowledge-card-retrieval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Installed Root
Resolve the installed reasflow-dev skills root before running packaged scripts:
REASFLOW_SKILLS_ROOT="${REASFLOW_SKILLS_ROOT:-}"
if [ -z "$REASFLOW_SKILLS_ROOT" ]; then
if [ -d ./.agents/skills ]; then
REASFLOW_SKILLS_ROOT="$(pwd)/.agents/skills"
elif [ -d "$HOME/.agents/skills" ]; then
REASFLOW_SKILLS_ROOT="$HOME/.agents/skills"
else
echo "reasflow shared skills not found in ./.agents/skills or $HOME/.agents/skills" >&2
exit 1
fi
fi
REASFLOW_PRIVATE_SKILLS_ROOT="${REASFLOW_PRIVATE_SKILLS_ROOT:-}"
if [ -z "$REASFLOW_PRIVATE_SKILLS_ROOT" ]; then
if [ -d ./.codex/reasflow-skills ]; then
REASFLOW_PRIVATE_SKILLS_ROOT="$(pwd)/.codex/reasflow-skills"
elif [ -d "$HOME/.codex/reasflow-skills" ]; then
REASFLOW_PRIVATE_SKILLS_ROOT="$HOME/.codex/reasflow-skills"
else
echo "reasflow private skills not found in ./.codex/reasflow-skills or $HOME/.codex/reasflow-skills" >&2
exit 1
fi
fi
知识卡片检索
概述
你是知识库管理员,负责从领域知识卡片目录中检索最相关的数学技术,以支持证明任务。 你必须使用分层检索策略,确保不遗漏关键技术,从高层框架逐步深入到低层优化技巧。
SKILL_ROOT="$REASFLOW_SKILLS_ROOT/knowledge-card-retrieval"
内置卡片入口:"$SKILL_ROOT/assets/knowledge-cards/catalog.json"
检索命令
python "$SKILL_ROOT/scripts/search-cards.py" \
--catalog "$SKILL_ROOT/assets/knowledge-cards/catalog.json" \
--query "<查询词>" \
--top-k 5
# 按 ID 读取卡片全文:
python "$SKILL_ROOT/scripts/read-card.py" --id <CARD_ID>
# 或直接读取路径:
cat "<search 输出中显示的绝对路径>"
5层检索协议(The 5-Layer Protocol)
第1层:核心机制搜索("骨架")
- 目标:找到主要证明框架和算法特有技术
- 行动:提取核心算法名称和关键机制(如 "FedAvg"、"Momentum"、"Subspace")
- 查询模板:
"<算法名> convergence <机制1> <机制2>" - 示例:
"FedAvg convergence random subspace momentum"
第2层:结构性质搜索("特征")
- 目标:识别结构属性(分布式、并行、动量)和性能保证(线性加速)
- 行动:
- 分布式/并行:搜索
"Distributed variance reduction","Linear speedup" - 动量/加速:搜索
"Momentum accumulation","Acceleration technique" - 压缩/量化:搜索
"Error feedback","Compression variance"
- 分布式/并行:搜索
- 查询模板:
"<属性/结构> <关键词> technique"
What ships with it
60 files 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.
- assets/knowledge-cards/algebraic_manipulation/proof_technique/TECH-BLOCK-MATRIX-JORDAN-DECOMPOSITION.yaml 2.1 KB
- assets/knowledge-cards/algebraic_manipulation/proof_technique/TECH-INCREMENTAL-TO-STATE-SPACE-TRANSFORMATION.yaml 1.7 KB
- assets/knowledge-cards/algebraic_manipulation/proof_technique/TECH-MATRIX-MIXING-CONTRACTION.yaml 1.9 KB
- assets/knowledge-cards/algebraic_manipulation/proof_technique/TECH-SIMILARITY-TRANSFORM-CONDITIONING.yaml 2.1 KB
- assets/knowledge-cards/algebraic_manipulation/proof_technique/TECH-SQUARE-ROOT-FACTORIZATION.yaml 1.9 KB
- assets/knowledge-cards/algebraic_manipulation/proof_technique/TECH-TAU-SLOW-SEQUENCE-ALGEBRA.yaml 2.0 KB
- assets/knowledge-cards/catalog.json 124 KB
- assets/knowledge-cards/complexity_analysis/proof_plan/PLAN-TRANSIENT-TIME-LOWER-BOUND.yaml 2.6 KB
- assets/knowledge-cards/complexity_analysis/proof_technique/TECH-LYAPUNOV-RECURSION-UNROLLING.yaml 1.6 KB
- assets/knowledge-cards/convex_analysis/proof_technique/TECH-PERTURBED-STRONG-CONVEXITY.yaml 1.5 KB
- assets/knowledge-cards/convex_analysis/proof_technique/TECH-RSC-LINEAR-CONVERGENCE-NEIGHBORHOOD.yaml 1.9 KB
- assets/knowledge-cards/convex_analysis/proof_technique/TECH-RSC-PENALTY-TRANSFER.yaml 2.5 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-DIFFERENCE-BASED-BIAS-CORRECTION.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-FEDAVG-M.yaml 2.0 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-FEDSUB-MECHANISM.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-HYBRID-GOSSIP-PGA.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-LOCAL-EXACT-DIFFUSION.yaml 1.9 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-MG-DSGD.yaml 1.9 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-OPTIMAL-GRADIENT-TRACKING-OGT.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-RANDOMIZED-SUBSPACE-OPTIMIZATION.yaml 1.9 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-SCAFCOM.yaml 1.8 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-SCAFFOLD-M.yaml 1.6 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-SCAFFOLD.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-SCALLION.yaml 1.9 KB
- assets/knowledge-cards/decentralized_optimization/algorithm_mechanism/MECH-SNAPSHOT-GRADIENT-TRACKING.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-COMPRESSED-VARIANCE-REDUCED-FL.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-CONVEX-EXACT-DIFFUSION-ANALYSIS.yaml 2.8 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-EXTRA-EXACT-CONSENSUS.yaml 3.0 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-HYBRID-CONSENSUS-ANALYSIS.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-INEXACT-CONVERGENCE-NEIGHBORHOOD.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-LINEAR-GEOMETRIC-CONVERGENCE-SMALL-GAIN.yaml 2.7 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-LOCAL-EXACT-DIFFUSION-ANALYSIS.yaml 2.5 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-MOMENTUM-FL-PROOF.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-OPTIMAL-GRADIENT-TRACKING-PROOF.yaml 2.4 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-OPTIMAL-NONCONVEX-ANALYSIS-WITH-RING-LATTICE.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-PRIMAL-DUAL-EXACT-CONSENSUS.yaml 2.6 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-RANDOMIZED-SUBSPACE-DESCENT.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-SCAFFOLD-CONVERGENCE.yaml 2.6 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-UNIFIED-DECENTRALIZED-NONCONVEX-ANALYSIS.yaml 2.8 KB
- assets/knowledge-cards/decentralized_optimization/proof_plan/PLAN-UNIFIED-GENERALIZED-DECENTRALIZED-SGD.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-ALGORITHM-REDUCTION-TO-PRIMAL-DUAL.yaml 2.0 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-AMORTIZED-DRIFT-ANALYSIS.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-CONTROL-VARIATE-DRIFT-CORRECTION.yaml 2.2 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-ERROR-NEIGHBORHOOD-QUANTIFICATION.yaml 1.8 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-GRADIENT-ENERGY-PRESERVATION.yaml 1.7 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-GRADIENT-TRACKING-DYNAMIC-AVERAGE.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-GRADIENT-TRACKING-ERROR-CYCLE.yaml 2.5 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-HESSIAN-DISSIMILARITY-ANALYSIS.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-IMPLICIT-CONTROL-VARIATE-COMMUNICATION.yaml 1.9 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-LOOPLESS-CHEBYSHEV-ACCELERATION.yaml 1.8 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-LOW-DIMENSIONAL-DUAL-DRIFT-CORRECTION.yaml 2.6 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-MOMENTUM-DRIFT-MITIGATION.yaml 1.9 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-MULTI-ROUND-GOSSIP-ACCELERATION.yaml 2.6 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-PRIMAL-DUAL-AVG-DEV-DECOMPOSITION.yaml 1.7 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-PRIMAL-DUAL-LOCAL-DRIFT-BOUND.yaml 1.8 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-RELATIVE-COMPRESSION-OF-DRIFT-CORRECTED-UPDATES.yaml 2.1 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-RING-LATTICE-TOPOLOGY-CONSTRUCTION.yaml 2.3 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-SNAPSHOT-GRADIENT-TRACKING-ANALYSIS.yaml 2.0 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-TRANSIENT-STAGE-ANALYSIS.yaml 1.6 KB
- assets/knowledge-cards/decentralized_optimization/proof_technique/TECH-UNIFIED-PRIMAL-DUAL-FORM.yaml 1.6 KB
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.
- 1mo ago First seen · 141 lines · 39 tokens per session scan A 8ad73883d4ac
knowledge-card-retrieval is a skill published in the GitHub repository sillyDaibo/reasflow-dev (2 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 1,667 once invoked, about $0.0002 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 2026-08-31.
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