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.
npx agentmods add skills/d-o-hub/github-template-ai-agents/memory-contextnpx skills add d-o-hub/github-template-ai-agents --skill memory-contextgit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWhat 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 | $0.00086 | $0.00771 |
| Opus 5 | $0.00043 | $0.00385 |
| Sonnet 5 | $0.00017 | $0.00154 |
| Haiku 4.5 | $0.00009 | $0.00077 |
Grade A, and why
memory-context 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Context
Retrieve semantically relevant past learnings, analysis outputs, and project knowledge using the csm (Chaotic Semantic Memory) CLI.
Prerequisites
cargo install chaotic_semantic_memory --features cli
When to Use
- At session start to recall previous work
- When facing a problem that might have been solved before
- To retrieve specific findings from
analysis/oragents-docs/
Indexing (Run Once)
# Index lessons (lessons.jsonl stores lesson summary text in "title")
csm index-jsonl -F agents-docs/lessons.jsonl --field title --id-field id --tag-field tags
# Index analysis outputs and docs
csm index-dir --glob "analysis/**/*.md" --glob "agents-docs/*.md" --heading-level 2
Index stored in .git/memory-index/csm.db (per-clone, never committed).
Querying
# Natural language query (default: hybrid retrieval)
csm query "how to handle git worktree cleanup" --top-k 5
# Code identifier query (exact match optimized)
csm query "MAX_CONTEXT_TOKENS" --top-k 3 --output-format json
# Code-heavy query
csm query "get_user_by_id" --code-aware --top-k 5
Output Formats
--output-format table(default): human-readable--output-format json: machine-parseable for agent consumption--output-format quiet: IDs only
Token Budget
Use a hard post-query cap from .agents/config.sh:
source .agents/config.sh
csm query "how to handle git worktree cleanup" --top-k 8 --output-format table |
awk -v max_tokens="$MAX_CONTEXT_TOKENS" '
{
for (i = 1; i <= NF; i++) {
if (token_count < max_tokens) {
printf "%s%s", $i, (token_count + 1 < max_tokens ? " " : "\n")
token_count++
} else {
exit
}
}
}
'
This enforces an approximate token ceiling even if retrieval output is verbose.
See Also
learn— Extract learnings into AGENTS.mddelegate— Context retrieval and handoff
Rationalizations
| Rationalization | Reality |
|---|---|
| "I'll just search with grep instead" | grep finds literal text; semantic retrieval surfaces related concepts and non-obvious connections. |
| "The index is probably out of date" | A stale index is better than no index; re-index periodically rather than skipping retrieval entirely. |
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.
- 2d ago First seen · 101 lines · 86 tokens per session scan A 301595863df2
memory-context is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed 2d ago), licensed MIT. It adds 86 tokens to every session and 771 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.
Other skills, from other repositories
opencli-sitemap-author
Use when creating or maintaining OpenCLI site sitemaps: agent-facing navigation, page-state, action, workflow, API-reference, pitfall, and fallback knowledge for a website. Use after browser exploration discovers durable site context, when a sitemap is stale, or when promoting local site knowledge into the repo.
golden-rss
Use when testing the rss golden build.
omh-buzz
This is a Hermes-native buzz workflow skill.
omh-code-review
This is a Hermes-native code-review workflow skill.
redteam-api-detail-pack
Domain routing and boundary guidance for authorized API security testing, including BOLA/IDOR, authentication bypass, mass assignment, missing rate limits, and GraphQL issues. Use when a task belongs to the API testing domain and needs scope, evidence, pivot, or exit criteria.
redteam-postex-detail-pack
Domain routing and boundary guidance for authorized post-exploitation testing after initial access, including privilege escalation, persistence, lateral movement, data collection, and cleanup considerations. Use when a task belongs to the post-exploitation domain and needs scope, evidence, pivot, or exit criteria.