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/harshanandak/forge/memorynpx skills add harshanandak/forge --skill memorygit clone --depth 1 https://github.com/harshanandak/forgeWhat 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 | $0.00208 | $0.01539 |
| Opus 5 | $0.00104 | $0.00770 |
| Sonnet 5 | $0.00042 | $0.00308 |
| Haiku 4.5 | $0.00021 | $0.00154 |
Grade A, and why
memory 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory
Durable project memory for agents. This skill teaches you where a fact belongs, which backend is active, and how to use graph memory when it is enabled.
Applies to: Forge-managed memory
This skill describes memory managed by Forge's own store — forge remember /
forge recall, whose backend the Backends section (below) explains. If the repo you are working in ships
its own memory system (a database, an API, a "log a decision" surface, an
in-app memory service), that system is authoritative here and this skill does
not apply — follow the repo's own memory instructions instead. When unsure, prefer
the host repo's memory over forge remember; a note written to the wrong store is
a note nobody finds.
The one rule
Persistent, project-level knowledge goes to forge remember — never to a
MEMORY.md file, never to a scratch note that dies with the session. Retrieve it
with forge recall. Both verbs route through one backend router; the default
is a local file store, so they always work offline with zero setup.
When to use what
forge remember "<note>"— a lasting fact that outlives this issue: a convention, a decision and its rationale, a non-obvious gotcha, an environment quirk, a "we tried X, it failed because Y". Add--tag <label>for retrieval.forge recall "<query>"— before assuming, check what is already known. Search first; do not re-derive knowledge the project already recorded. Note that memory relevant to your current prompt may already be injected automatically (a per-turn hook surfaces the best-matching notes; silent when nothing clears the relevance bar). Treat auto-surfaced memory as a head start, and stillrecallexplicitly when you need to search for something specific it did not surface.forge issue comment <id> "<note>"— progress or context that belongs to ONE issue's lifecycle (status, a blocker, a hand-off). Issue-scoped, not global.- Rule of thumb: would a future session on a different issue want this? →
remember. Is it only meaningful inside this issue? →issue comment.
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 · 118 lines · 208 tokens per session scan A d30c3ad66b7b
memory is a skill published in the GitHub repository harshanandak/forge (4 stars, last pushed 3d ago), licensed MIT. It adds 208 tokens to every session and 1,539 once invoked, about $0.0010 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…