ShadowFrog is a suite of coding-agent skills that maintains a file-backed knowledge base of tacit information about a codebase, such as fragile dependencies, untested invariants, and hard-to-notice edge cases. It helps coding agents preserve lessons from code reading, experiments, and conversations across future work. The catalogue contains skills that create, update, explore, organize, and view this shadow knowledge base.
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 skills add microsoft/ShadowFrog --skill shadow-frog-meditategit clone --depth 1 https://github.com/microsoft/ShadowFrogWrote 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/microsoft/shadowfrog/shadow-frog-meditate)<a href="https://agentmods.dev/skills/microsoft/shadowfrog/shadow-frog-meditate"><img src="https://agentmods.dev/badge/skills/microsoft/shadowfrog/shadow-frog-meditate/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/microsoft/shadowfrog/shadow-frog-meditate"><img src="https://agentmods.dev/badge/skills/microsoft/shadowfrog/shadow-frog-meditate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 302 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00081 | $0.03077 |
| Opus 5.5 | $0.00032 | $0.01231 |
| Sonnet 5.5 | $0.00016 | $0.00615 |
| Haiku 4.5 | $0.00008 | $0.00308 |
Grade A, and why
shadow-frog-meditate 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 29d 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ShadowFrog Meditate
Shadow hygiene — deduplicate, merge, and resolve conflicts across the
entire .shadow/ knowledge base. Prerequisite: .shadow/ exists with
discoveries.
Phase 1: Scan
Use parallel subagents to scan the shadow. Each subagent handles a batch of shadow files.
Scope Optimization
Not every file needs scanning. To reduce cost:
- Skip files with 0-1 discoveries — they can't have internal duplicates
- Focus on files modified since last meditate — check
_meta/state.jsonlast_update_atagainst file modification times - Always scan files with 5+ discoveries — highest duplicate risk
For the first meditate after a large dream run, most files will need scanning.
Per-File Scan
For each per-file shadow (e.g., src/auth.py.md):
- Read all discoveries under each
## symbolheading - Compare discoveries under the same symbol by behavioral meaning,
not wording:
- Duplicate: same behavioral claim, different wording
- Near-duplicate: one discovery is a subset/refinement of the other
- Conflict: the two discoveries make contradicting claims
- Distinct: genuinely different insights — no action needed
- Record each finding as a structured action (see below)
Overlapping Also involves: refs can help identify related claims.
Scan Output Format
Subagents must output findings as one JSON object per line so the orchestrator can auto-apply resolutions. This is critical for automation — prose recommendations require manual interpretation.
{"action": "merge", "file": "src/auth.py.md", "symbol": "authenticate_user", "keep": "- silently returns None on expired tokens...", "remove": "- returns None when token expires...", "merged": "- authenticate_user() silently returns None on expired tokens instead of raising. 3 of 7 callers don't check.\n _(verified, source: exploration, citation_score: 0)_", "reason": "duplicate: same claim, different wording"}
{"action": "merge", "file": "src/db.py.md", "symbol": "connect", "keep": "- connection pool exhaustion...", "remove": "- pool runs out...", "merged": "...", "reason": "near-duplicate: first extends second"}
{"action": "conflict", "file": "src/auth.py.md", "symbol": "validate_token", "entry_a": "- raises ValueError...", "entry_b": "- returns False...", "resolution": "verified_a", "reason": "code inspection: line 42 raises ValueError"}
{"action": "conflict", "file": "src/cache.py.md", "symbol": "invalidate", "entry_a": "...", "entry_b": "...", "resolution": "escalate", "reason": "both claims have evidence, needs user input"}
{"action": "move_to_cross", "file": "src/auth.py.md", "symbol": "validate_token", "entry": "- all validators share...", "cross_slug": "shared-validation-pattern", "reason": "cross-scope: involves 4 files"}
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.
- 29d ago Changed · +4 lines 32c832d4a735
- 1mo ago Changed · -38 lines d758b8f5c17f
- 1mo ago First seen · 329 lines · 81 tokens per session scan A e3ee2df106bb
shadow-frog-meditate is a skill published in the GitHub repository microsoft/ShadowFrog (30 stars, last pushed 6d ago), licensed MIT. It adds 81 tokens to every session and 3,077 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
knowledge-ingest
Ingest documents, articles, conversations, or supplied text into Knowledge with retained originals, sourced claims, entity updates, timelines, and cross-links; also learn durable lessons from a set of conversations, transcripts, or past sessions (/learn), including the Learn from conversations and Learn from Memory…
forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…