Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add haabe/mycelium/plugin install myceliumWrote 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/haabe/mycelium/theory-fidelity)<a href="https://agentmods.dev/skills/haabe/mycelium/theory-fidelity"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/theory-fidelity/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/haabe/mycelium/theory-fidelity"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/theory-fidelity.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 | $0.00057 | $0.02657 |
| Opus 5 | $0.00028 | $0.01328 |
| Sonnet 5 | $0.00011 | $0.00531 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
theory-fidelity 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 11d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Theory Fidelity Audit
Most framework checks evaluate process (cycle health, gates) or artifact performance (evals, DORA). None of them ask the question this skill exists for: for every theory a project claims to represent, is the mapped mechanism actually faithful to what the theory says — or is it theatre? This is the audit of the theory→mechanism mapping itself.
The framework's own stated bar (docs/theories.md): "every theory is mechanism-mapped … citations without mechanism-mapping are theatre." This skill holds the project to that bar — including holding the theory doc to it.
When to Use
- Quarterly, alongside
/mycelium:framework-health(process health) — this is the theory-fidelity half. - After adding/citing a new theory, or after editing a theory's mechanism (skill/gate/schema).
- When a citation looks decorative, or when a doc claims a mechanism you suspect doesn't exist.
The Grading Rubric (three axes)
For each claimed theory, record:
- Representation —
Mechanized(a skill/gate/schema/canvas applies it) ·Prose-only(cited + described, no mechanism) ·Absent. - Fidelity (only if Mechanized):
Faithful— the mechanism matches the theory's real claims.Justified-Adaptation— the mechanism deliberately diverges and the rationale is documented in-repo. Divergence with no documented rationale isDistorted, not Justified.Partial— a faithful subset, with a named gap.Distorted— diverges without rationale, or misrepresents the theory.Over-claim— the theory doc claims more than the mechanism delivers (the project's own "theatre" failure mode).Name-only— cited but not actually mechanized.
- Evidence-basis —
source-grounded(verified against the author's canonical work) ·model-knowledge(from the agent's training — provisional / consistency-only).
Workflow
- Rule on the PREVIOUS audit's findings — before producing any new ones. Per
${CLAUDE_PLUGIN_ROOT}/engine/canvas-guidance.yml#prior_findings_first.
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.
- 11d ago First seen · 148 lines · 57 tokens per session scan A a37a1da2035e
theory-fidelity is a skill published in the GitHub repository haabe/mycelium (45 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 2,657 once invoked, about $0.0003 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 skills, from other repositories
api-enumeration-fuzzing-discovery
Systematically discover hidden Application Programming Interfaces (APIs), uncover undocumented endpoints (Shadow APIs), and fuzz parameters. Use this skill as the pivotal first step in API Bug Hunting, transforming a basic frontend application into a vast mapped attack surface.
Full Stack Engineer
Full Stack Engineer delivers production-grade technical work with clear architecture, tests, maintainability, and operational awareness.
api-client
Patterns for consuming the img.automators.work placeholder image API via HTTP. Use when building HTML mockups, CSS backgrounds, or design prototypes that need SVG placeholder images of specific dimensions and colors.
webhook
Webhook receiver patterns — HMAC signature verification, idempotency, retry handling, delivery guarantees, dead-letter logging, and local testing with ngrok or smee.
detailed-design
Skill for detailed design — class diagrams, sequence diagrams, API contracts, database schema design, and error handling strategy.
ha-discovery-augment
Augments an existing Home Assistant Custom Integration with one network or bus discovery mechanism beyond Zeroconf — DHCP, SSDP/uPnP, USB, HomeKit, or MQTT discovery — conforming to spec/ha/discovery-mechanisms. Sets the manifest matcher list, implements the typed asyncstep in configflow.py, forwards into a confirm…