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.
git clone --depth 1 https://github.com/DyanGalih/spec-kit-memory-hubWrote 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/commands/dyangalih/spec-kit-memory-hub/speckit.memory-md.token-report)<a href="https://agentmods.dev/commands/dyangalih/spec-kit-memory-hub/speckit.memory-md.token-report"><img src="https://agentmods.dev/badge/commands/dyangalih/spec-kit-memory-hub/speckit.memory-md.token-report/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/commands/dyangalih/spec-kit-memory-hub/speckit.memory-md.token-report"><img src="https://agentmods.dev/badge/commands/dyangalih/spec-kit-memory-hub/speckit.memory-md.token-report.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 | $0.00012 | $0.00345 |
| Opus 5 | $0.00006 | $0.00172 |
| Sonnet 5 | $0.00002 | $0.00069 |
| Haiku 4.5 | $0.00001 | $0.00034 |
Grade A, and why
speckit.memory-md.token-report 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 10d 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
Token Report
Compare estimated token usage between the full durable-memory read and the optimized synthesis flow.
Use this when:
- you want a quick estimate of how much context the SQLite cache saves
- you want to compare the full
.mdbackup size against the optimized synthesis path
Call:
speckit_memory_token_report(feature="specs/<feature>")
Report:
- baseline full durable memory read
- optimized index-and-synthesis flow
- estimated token reduction
When the optimizer is enabled, the same baseline/cached/saved summary should be surfaced after memory-aware MCP search and synthesis runs so the comparison stays visible during normal use.
Token counts are estimates using @dqbd/tiktoken with the cl100k_base encoding (GPT-4 calibrated).
Actual provider billing tokens may differ. Use these calibration factors when interpreting results for other models:
| Model family | Adjustment |
|---|---|
| GPT-4 / GPT-4o | ×1.00 (baseline) |
| Claude 3.x / 3.5 / 3.7 | ×1.05–1.15 (slightly higher token count) |
| Gemini 1.5 / 2.x | ×0.90–1.10 (varies by content type) |
| Llama / Mistral | ×1.10–1.30 (depends on tokenizer) |
These multipliers are rough estimates. They indicate how many tokens the model will actually consume relative to the cl100k_base count. Use them as a planning guide, not billing telemetry.
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.
- 10d ago First seen · 37 lines · 12 tokens per session scan A 2d9f2a771b91
speckit.memory-md.token-report is a command published in the GitHub repository DyanGalih/spec-kit-memory-hub (15 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 345 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 2026-08-30.
Other commands, from other repositories
wiki-query
Ask questions against the wiki. Synthesizes answers from wiki pages with cross-reference citations.
wiki-digest
Generate a daily or weekly digest of wiki changes — new sources, pages, insights, and gaps.
autospec.plan
Generate YAML implementation plan from feature specification.
q-research
Read the research-mode skill's SKILL.md for the full ruleset before proceeding. Follow all constraints, the source lookup cascade, the token budget, and the "what counts as cited" rules exactly.
web-search
Search fetched web-source Markdown by regex and pull surrounding context for the best hits. Optionally restrict to one source alias.
docs-refresh
Re-fetch a cached source, ignoring the 7-day cache. Use when upstream content has changed.