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/jl-cmd/claude-dev-env/recallnpx skills add jl-cmd/claude-dev-env --skill recallgit clone --depth 1 https://github.com/jl-cmd/claude-dev-envWrote 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/jl-cmd/claude-dev-env/recall)<a href="https://agentmods.dev/skills/jl-cmd/claude-dev-env/recall"><img src="https://agentmods.dev/badge/skills/jl-cmd/claude-dev-env/recall.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.00331 |
| Opus 5 | $0.00010 | $0.00166 |
| Sonnet 5 | $0.00004 | $0.00066 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
recall 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 4d 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.
What it actually says
Recall
Search the Obsidian vault for prior session context, decisions, research, and gotchas relevant to the current task.
Instructions
-
Determine search query. If
$ARGUMENTSis provided, use it as the search query. Otherwise, infer the project name from the current conversation context (git remote, working directory, or topic being discussed). -
Search by frontmatter first. Use
mcp__obsidian__search_noteswithsearchFrontmatter: trueand the project name. This finds session reports and decision notes tagged with the project. -
Search by content keywords. If frontmatter search returns few results, search again by content using relevant keywords from the current task (component names, error messages, library names).
-
Read top matches. Use
mcp__obsidian__read_noteto read the top 3 most relevant results. Prefer recent notes over older ones. Prefer decision notes and session summaries over raw research. -
Present findings with attribution. For each note, include:
- Note path and date
- Project name
- Relevant excerpts (not the full note unless it's short)
- Whether any decisions are marked
status: Superseded
-
Handle no results honestly. If no vault history exists for this project, say so explicitly. Do not fabricate or infer history that isn't in the vault.
What ships with it
2 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.
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.
- 4d ago First seen · 28 lines · 19 tokens per session scan A 4775b0629df3
recall is a skill published in the GitHub repository jl-cmd/claude-dev-env (5 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 331 once invoked, about $0.0001 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.
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