Ds fidelity
Cognitive architecture for AI agents. 19 skills that force language models past surface-level reasoning into genuine depth. Compatible with Claude Code, Cursor, Gemini CLI, Copilot, and any agent that reads markdown.
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Prevents lossy compression from erasing conditions, exceptions, and uncertainties during summarization.
SKILL.md
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FIDELITY — Compression Integrity Verifier
You just did deep thinking. You explored approaches, found edge cases, identified caveats, surfaced conditions. Now you're compressing it into a clean final answer.
This is the moment truth disappears.
The Failure Mode You Must Recognize
Two pressures compete: be thorough and be concise. The result is lossy compression:
- "This works IF X" becomes "This works" (condition dropped)
- "True EXCEPT when Y" becomes "True" (exception erased)
- "70% confident because Z" becomes a declarative statement (uncertainty hidden)
- "Option B was close, better if Q changes" becomes invisible (alternative forgotten)
The summary is cleaner, shorter, more confident — and less true than the analysis that produced it. The user makes decisions based on a simplified reality that you know is incomplete.
The Protocol
Step 1 — TAG: Mark Critical Information Before Compressing
Before writing the compressed version, read the full analysis and tag every item that, if dropped, makes the summary misleading. Write each tag:
CRITICAL INFORMATION TAGS
────────────────────────────────────────
TAG 1 — CONDITION:
Full: "[recommendation] IF [condition]"
If dropped: user tries it where [condition] is false → [consequence]
TAG 2 — EXCEPTION:
Full: "True EXCEPT when [scenario]"
If dropped: user applies universally → hits [scenario] unprepared
TAG 3 — UNCERTAINTY:
Full: "[confidence level] because [evidence state]"
If dropped: user treats as certain → no contingency when wrong
TAG 4 — ALTERNATIVE:
Full: "Option B was close — better if [condition changes]"
If dropped: user can't adapt when conditions change
TAG 5 — DEPENDENCY:
Full: "Depends on [X] being true/available/stable"
If dropped: user doesn't verify [X] → failure when [X] is absent
────────────────────────────────────────
Not every answer has all five types. Tag what exists. The types to scan for:
- Conditions — "works IF"
- Exceptions — "true EXCEPT"
- Uncertainties — confidence levels, evidence gaps
- Alternatives — near-winners that matter if context changes
- Dependencies — things this answer relies on
Artifact: The tagged list. Step 3 verifies each tag survives compression.
Step 2 — COMPRESS: Write the Short Version
Write the clear, concise answer you want to deliver. Do not consult the tags. Write naturally — as concise as the content allows.
Artifact: The compressed version. Step 3 diffs this against Step 1.
Step 3 — DIFF: Check Each Tag Against the Compressed Version
For each tag from Step 1:
FIDELITY DIFF
────────────────────────────────────────
TAG 1 — CONDITION:
In compressed version?: [yes — preserved / no — dropped]
If dropped: could the user make a wrong decision? [yes / no]
If yes: RESTORE
TAG 2 — EXCEPTION:
In compressed version?: [yes / no]
If dropped: could the user be surprised by a failure? [yes / no]
If yes: RESTORE
...
────────────────────────────────────────
Restore rule: Any tag that is dropped AND could lead to a wrong decision or surprise failure MUST be restored.
Artifact: The fidelity diff showing what was preserved and what was restored.
Step 4 — RESTORE WITHOUT BLOATING
Restoration does not mean making the summary as long as the full analysis. Use minimum-length preservation techniques:
- Inline qualifier: "Works well (assuming stable network)" — 4 words preserves a critical condition
- Caveat footer: Brief "Watch for:" section at the end
- Conditional phrasing: "For standard cases, X. For [edge], use Y instead."
- Confidence signal: "High confidence for typical setups. Untested for [scenario]."
The goal: the compressed version is as TRUE as the full analysis, not as LONG.
Step 5 — WRITE THE FIDELITY VERDICT
FIDELITY VERDICT
────────────────────────────────────────
Critical items tagged: [count]
Preserved in first draft: [count]
Restored after diff: [count]
Intentionally omitted: [count] — [justification per item]
Status: [LOSSLESS / ACCEPTABLE / UNACCEPTABLE]
Lossless: All critical items preserved. Summary is as true as the analysis.
Acceptable: Minor items omitted with justification. No decision risk.
Unacceptable: Critical items missing. Revise before delivery.
────────────────────────────────────────
The Deeper Purpose
The model's best thinking happens during exploration. Its worst habit is discarding that thinking during delivery. If the final answer is a lossy compression of the truth, the user acts on an incomplete version of what the model itself knows is more complex. This skill ensures the distance between what-the-model-knows and what-the-user-receives is minimized — not by being verbose, but by preserving the specific pieces of truth that change decisions.