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saivarun3407saivarun3407/DFIR-AI/.claude/skills/gap-acknowledgment/SKILL.md

gap-acknowledgment

Skill0 starsChanged 5 months agoApache-2.0View on GitHub

Discipline for acknowledging what we don't know. Calling out gaps explicitly via a `confidence='unknown'` finding is a strength signal — judges score honesty positively and detect inflated claims fast. This skill teaches when to refuse vs. when to assert.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Don't flood the audit log with trivial "couldn't determine X" entries — gaps should be substantive

    line 23
  2. Distinctive:

    Don't acknowledge to dodge work.

    line 24
  3. Very specific:

    Read orchestrator/src/mh_orchestrator/handle_dump_registry.py (or call handle_dump_registry.targets_for_process(name)) to get the list of …

    line 34
  4. Very specific:

    Run mcp__protocol_sift__memory_volatility with plugin=windows.dumpfiles args=["--pid", "<PID>"].

    line 35
  5. Very specific:

    Call finding_record with confidence='unknown', a single pin pointing at the artifact you couldn't conclude on, and a claim describing the …

    line 44

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Gap Acknowledgment

"I don't know" is a valid output. Use it.

When To Acknowledge a Gap

SituationAction
Tool failed with no fallback that produces same evidence classacknowledge + note alternative attempted
Symbol table missing for memory dumpacknowledge — do NOT invent process names
Encrypted artifact (Signal DB without keychain access)acknowledge — do NOT speculate on contents
Evidence corrupted / partialacknowledge — note recoverable vs unrecoverable
Single ambiguous artifact, not enough to pin confidence higher than uncertainEITHER pin as uncertain OR acknowledge gap, not both
Question outside scope (e.g., asked about Linux when only Windows evidence ingested)acknowledge — do NOT extrapolate

When NOT To Acknowledge

  • Don't flood the audit log with trivial "couldn't determine X" entries — gaps should be substantive
  • Don't acknowledge to dodge work; if the evidence is available, do the analysis

Pre-Gate: Handle-Dump Discipline (HARD requirement)

Before recording any gap whose claim contains the phrases "memory cap" / "memory only" / "from memory alone" / "cannot recover" / "irrecoverable from this image", you MUST first attempt windows.dumpfiles --pid <PID> against every process that holds a relevant file handle.

Cloud-sync clients (OneDrive, Drive FS, Dropbox), messaging apps (Slack, Teams), browsers (Chrome, Edge, Firefox), mail clients (Outlook), and shells (Explorer) keep high-value logs/databases cached in their working set. The Rocba case (case-id rocba-memory, 2026-05) lost real investigative time recording G-2: specific files exfiltrated unknowable from memory while the OneDrive AODL was a live handle on PID 9648; one windows.dumpfiles --pid 9648 recovered downloads3.txt (49 SharePoint download events) and Fred's user-state .dat (≈30 SRL filenames), collapsing G-2 entirely.

Procedure:

  1. Read orchestrator/src/mh_orchestrator/handle_dump_registry.py (or call handle_dump_registry.targets_for_process(name)) to get the list of file patterns worth recovering for that process.
  2. Run mcp__protocol_sift__memory_volatility with plugin=windows.dumpfiles args=["--pid", "<PID>"].
  3. Filter the dump output for the registered patterns (.aodl, downloads3.txt, drive_fs.db, places.sqlite, etc.).
  4. Decode (UTF-16-LE for OneDrive logs; SQLite for browser DBs; JSON for Slack).
  5. Only after that — if the patterns aren't in the dump output OR the pages were paged out at capture — is the gap legitimate. Record the gap with the dumpfiles attempt named explicitly in the claim text so the verifier can confirm.

A gap of the form "memory only — can't tell" without a paired windows.dumpfiles audit event in the same case output is a doctrine violation, not a finding.

Format

Call finding_record with confidence='unknown', a single pin pointing at the artifact you couldn't conclude on, and a claim describing the gap:

mcp__protocol_sift__finding_record(
    finding_id="GAP-001",
    claim="Cannot determine cridex.exe network destinations: netscan returned no entries for PID 1484. Possible causes: (a) connections closed before snapshot, (b) memory paged out, (c) covert channel not visible to netscan. Insufficient evidence to choose.",
    confidence="unknown",
    pins=[{
        "artifact": "/input/memory.raw",
        "tool": "memory_volatility",
        "locator": {"type": "memory_vad", "value": "PID:1484"},
        "raw_excerpt": "netscan: 0 records for PID 1484",
        "captured_at": "<iso8601>"
    }]
)

The claim field is read by the accuracy-report skill and surfaced in the final report. Make it specific.

Demo Value

Calling out 3-5 honest gaps in the demo video sets MemoryHound apart. Most teams will hide or paper over uncertainty. Judges notice.

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